<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vakali, Athena</style></author><author><style face="normal" font="default" size="100%">Kitmeridis, Nikolaos</style></author><author><style face="normal" font="default" size="100%">Panourgia, Maria</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Angelov, Plamen</style></author><author><style face="normal" font="default" size="100%">Manolopoulos, Yannis</style></author><author><style face="normal" font="default" size="100%">Iliadis, Lazaros</style></author><author><style face="normal" font="default" size="100%">Roy, Asim</style></author><author><style face="normal" font="default" size="100%">Vellasco, Marley</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Distributed Framework for Early Trending Topics Detection on Big Social Networks Data Threads</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Big Data: Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-47898-2_20</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><pages><style face="normal" font="default" size="100%">186–194</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-47898-2</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Social networks have become big data production engines and their analytics can reveal insightful trending topics, such that hidden knowledge can be utilized in various applications and settings. This paper addresses the problem of popular topics’ and trends’ early prediction out of social networks data streams which demand distributed software architectures. Under an online time series classification model, which is implemented in a flexible and adaptive distributed framework, trending topics are detected. Emphasis is placed on the early detection process and on the performance of the proposed framework. The implemented framework builds on the lambda architecture design and the experimentation carried out highlights the usefulness of the proposed approach in early trends detection with high rates in performance and with a validation aligned with a popular microblogging service.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Passalis, Nikolaos</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Sanjay Kumar Madria</style></author><author><style face="normal" font="default" size="100%">Hara, Takahiro</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">MultiSpot: Spotting Sentiments with Semantic Aware Multilevel Cascaded Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Big Data Analytics and Knowledge Discovery</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Multilevel features</style></keyword><keyword><style  face="normal" font="default" size="100%">Sentiment detection</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-22729-0_26</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><volume><style face="normal" font="default" size="100%">9263</style></volume><pages><style face="normal" font="default" size="100%">337-350</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-22728-3</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Nick Bassiliades</style></author><author><style face="normal" font="default" size="100%">Mirjana Ivanovic</style></author><author><style face="normal" font="default" size="100%">Margita Kon-Popovska</style></author><author><style face="normal" font="default" size="100%">Yannis Manolopoulos</style></author><author><style face="normal" font="default" size="100%">Themis Palpanas</style></author><author><style face="normal" font="default" size="100%">Goce Trajcevski</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">New Trends in Database and Information Systems II - Selected papers of the 18th East European Conference on Advances in Databases and Information Systems and Associated Satellite Events, ADBIS 2014 Ohrid, Macedonia, September 7-10, 2014 Proceedings II</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Intelligent Systems and Computing</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%"> </style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-10518-5</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">312</style></volume><isbn><style face="normal" font="default" size="100%">978-3-319-10517-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christos Zigkolis</style></author><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Filippou, George</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Collaborative event annotation in tagged photo collections</style></title><secondary-title><style face="normal" font="default" size="100%">Multimedia Tools Appl.</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Event authoring</style></keyword><keyword><style  face="normal" font="default" size="100%">Ground truth generation</style></keyword><keyword><style  face="normal" font="default" size="100%">Multimedia annotation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">70</style></volume><pages><style face="normal" font="default" size="100%">89-118</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Events constitute a significant means of multimedia content organizationand sharing. Despite the recent interest in detecting events and annotating mediacontent in an event-centric way, there is currently insufficient support for managingevents in large-scale content collections and limited understanding of the eventannotation process. To this end, this paper presents CrEve, a collaborative eventannotation framework which uses content found in social media sites with theprime objective to facilitate the annotation of large media corpora with eventinformation. The proposed annotation framework could significantly benefit socialmedia research due to the proliferation of event-related user-contributed content.We demonstrate that, compared to a standard â€śbrowse-and-annotateâ€ť interface,CrEve leads to a 19% increase in the coverage of the generated ground truth in alarge-scale annotation experiment. Furthermore, the paper discusses the results of auser study that quantifies the performance of CrEve and the contribution of differentevent dimensions in the event annotation process. The study confirms the prevalenceof spatio-temporal queries as the prime option of discovering event-related contentin a large collection. In addition, textual queries and social cues (content contributor) were also found to be significant as event search dimensions. Finally, it demonstratesthe potential of employing automatic photo clustering methods with the goal offacilitating event annotation.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Polymerou, Evangelia</style></author><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Akerkar, Rajendra</style></author><author><style face="normal" font="default" size="100%">Bassiliades, Nick</style></author><author><style face="normal" font="default" size="100%">Davies, John</style></author><author><style face="normal" font="default" size="100%">Ermolayev, Vadim</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">EmoTube: A Sentiment Analysis Integrated Environment for Social Web Content</style></title><secondary-title><style face="normal" font="default" size="100%">WIMS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pages><style face="normal" font="default" size="100%">20</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4503-2538-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Barbara Catania</style></author><author><style face="normal" font="default" size="100%">Cerquitelli, Tania</style></author><author><style face="normal" font="default" size="100%">Chiusano, Silvia</style></author><author><style face="normal" font="default" size="100%">Guerrini, Giovanna</style></author><author><style face="normal" font="default" size="100%">Kämpf, Mirko</style></author><author><style face="normal" font="default" size="100%">Kemper, Alfons</style></author><author><style face="normal" font="default" size="100%">Novikov, Boris</style></author><author><style face="normal" font="default" size="100%">Palpanas, Themis</style></author><author><style face="normal" font="default" size="100%">Pokorny, Jaroslav</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">New Trends in Databases and Information Systems, 17th East European Conference on Advances in Databases and Information Systems</style></title><secondary-title><style face="normal" font="default" size="100%">ADBIS (2)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Advances in Intelligent Systems and Computing</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2013</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Genoa, Italy</style></pub-location><volume><style face="normal" font="default" size="100%">241</style></volume><isbn><style face="normal" font="default" size="100%">978-3-319-01863-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Hameurlain, Abdelkader</style></author><author><style face="normal" font="default" size="100%">Küng, Josef</style></author><author><style face="normal" font="default" size="100%">Wagner, Roland</style></author><author><style face="normal" font="default" size="100%">Barbara Catania</style></author><author><style face="normal" font="default" size="100%">Guerrini, Giovanna</style></author><author><style face="normal" font="default" size="100%">Palpanas, Themis</style></author><author><style face="normal" font="default" size="100%">Pokorny, Jaroslav</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Transactions on Large-Scale Data- and Knowledge-Centered Systems</style></title><secondary-title><style face="normal" font="default" size="100%">T. Large-Scale Data- and Knowledge-Centered Systems</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">8920</style></volume><isbn><style face="normal" font="default" size="100%">978-3-662-45760-3</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kastrinakis, Dimitrios</style></author><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Barbara Catania</style></author><author><style face="normal" font="default" size="100%">Guerrini, Giovanna</style></author><author><style face="normal" font="default" size="100%">Pokorny, Jaroslav</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Compact and Distinctive Visual Vocabularies for Efficient Multimedia Data Indexing</style></title><secondary-title><style face="normal" font="default" size="100%">ADBIS</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">composite visual word</style></keyword><keyword><style  face="normal" font="default" size="100%">local descriptors</style></keyword><keyword><style  face="normal" font="default" size="100%">multimedia data indexing</style></keyword><keyword><style  face="normal" font="default" size="100%">visual word</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">8133</style></volume><pages><style face="normal" font="default" size="100%">98-111</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-40682-9</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Multimedia data indexing for content-based retrieval has attractedsignificant attention in recent years due to the commoditizationof multimedia capturing equipment and the widespread adoption of social networking platforms as means for sharing media content online. Due to the very large amounts of multimedia content, notably images, produced and shared online by people, a very important requirement for multimedia indexing approaches pertains to their efficiency both in terms of computation and memory usage. A common approach to support query-by-example image search is based on the extraction of visual words from images and their indexing by means of inverted indices, a method proposed and popularized in the field of text retrieval.The main challenge that visual word indexing systems currently facearises from the fact that it is necessary to build very large visual vocabularies (hundreds of thousands or even millions of words) to support sufficiently precise search. However, when the visual vocabulary is large,the image indexing process becomes computationally expensive due to the fact that the local image descriptors (e.g. SIFT) need to be quantized to the nearest visual words.To this end, this paper proposes a novel method that significantly decreases the time required for the above quantization process. Instead of using hundreds of thousands of visual words for quantization, the proposed method manages to preserve retrieval quality by using a much smaller number of words for indexing. This is achieved by the concept of composite words, i.e. assigning multiple words to a local descriptor in ascending order of distance. We evaluate the proposed method in the Oxford and Paris buildings datasets to demonstrate the validity of the proposed approach.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Sagonas, Christos</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Li, Shipeng</style></author><author><style face="normal" font="default" size="100%">El-Saddik, Abdulmotaleb</style></author><author><style face="normal" font="default" size="100%">Wang, Meng</style></author><author><style face="normal" font="default" size="100%">Mei, Tao</style></author><author><style face="normal" font="default" size="100%">Sebe, Nicu</style></author><author><style face="normal" font="default" size="100%">Yan, Shuicheng</style></author><author><style face="normal" font="default" size="100%">Hong, Richang</style></author><author><style face="normal" font="default" size="100%">Gurrin, Cathal</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Semi-supervised Concept Detection by Learning the Structure of Similarity Graphs</style></title><secondary-title><style face="normal" font="default" size="100%">MMM (1)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">7732</style></volume><pages><style face="normal" font="default" size="100%">1-12</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-35725-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present an approach for detecting concepts in images bya graph-based semi-supervised learning scheme. The proposed approach builds a similarity graph between both the labeled and unlabeled images of the collection and uses the Laplacian Eigemaps of the graph as features for training concept detectors. Therefore, it offers multiple options for fusing different image features. In addition, we present an incremental learning scheme that, given a set of new unlabeled images, efficiently performs the computation of the Laplacian Eigenmaps. We evaluate the performance of our approach both on synthetic datasets and on MIR Flickr, comparing it with high-performance state-of-the-art learning schemes with competitive and in some cases superior results.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Desai, Bipin C.</style></author><author><style face="normal" font="default" size="100%">Pokorny, Jaroslav</style></author><author><style face="normal" font="default" size="100%">Bernardino, Jorge</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Evolving social data mining and affective analysis methodologies, framework and applications</style></title><secondary-title><style face="normal" font="default" size="100%">IDEAS</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">evolving social data mining</style></keyword><keyword><style  face="normal" font="default" size="100%">microblogging data analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">social affective analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Social networking</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pages><style face="normal" font="default" size="100%">1-7</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4503-1234-9</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Social networks drive todays opinions and content diffusion.Large scale, distributed and unpredictable social data streams areproduced and such evolving data production offers the ground forthe data mining and analysis tasks. Such social data streamsembed human reactions and inter-relationships and affective andemotional analysis has become rather important in todaysapplications. This work highlights the major data structures andmethodologies used in evolving social data mining and proceedsto the relevant affective analysis techniques. A particularframework is outlined along with indicative applications whichemploy evolving social data analysis with emphasis on theseminal criteria of topic, location and time. Such mining andanalysis overview is beneficial for various scientific andenterpreneural audiences and communities in the socialnetworking area.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Christos Zigkolis</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Larson, Martha</style></author><author><style face="normal" font="default" size="100%">Rae, Adam</style></author><author><style face="normal" font="default" size="100%">Demarty, Claire-Helene</style></author><author><style face="normal" font="default" size="100%">Kofler, Christoph</style></author><author><style face="normal" font="default" size="100%">Metze, Florian</style></author><author><style face="normal" font="default" size="100%">Troncy, Raphaël</style></author><author><style face="normal" font="default" size="100%">Mezaris, Vasileios</style></author><author><style face="normal" font="default" size="100%">Jones, Gareth J. F.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">CERTH @ MediaEval 2011 Social Event Detection Task</style></title><secondary-title><style face="normal" font="default" size="100%">MediaEval</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">CEUR Workshop Proceedings</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><volume><style face="normal" font="default" size="100%">807</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper describes the participation of CERTH in the â€śSocialEvent Detection Task @ MediaEval 2011â€ť, which aimsat discovering social events in a large photo collection. Thetask comprises two challenges: (i) identification of soccerevents in the cities of Barcelona and Rome, and (ii) identificationof events taking place in two specific venues. Weadopt an approach that combines spatial and temporal filterswith tag-based location classification models and an ef-ficient photo clustering method. In our best runs, we achieveF-measure and NMI scores of 77.4% and 0.63 respectivelyfor Challenge 1, and 64% and 0.38 for Challenge 2.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Christos Zigkolis</style></author><author><style face="normal" font="default" size="100%">Kapiris, Stefanos</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Natale, Francesco G. B. De</style></author><author><style face="normal" font="default" size="100%">Bimbo, Alberto Del</style></author><author><style face="normal" font="default" size="100%">Hanjalic, Alan</style></author><author><style face="normal" font="default" size="100%">Manjunath, B. S.</style></author><author><style face="normal" font="default" size="100%">Satoh, Shin’ichi</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">City exploration by use of spatio-temporal analysis and clustering of user contributed photos</style></title><secondary-title><style face="normal" font="default" size="100%">ICMR</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">content browsing</style></keyword><keyword><style  face="normal" font="default" size="100%">landmark/event detection</style></keyword><keyword><style  face="normal" font="default" size="100%">spatio-temporal mining</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pages><style face="normal" font="default" size="100%">65</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4503-0336-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present a technical demonstration of an online city explorationapplication that helps users identify interesting spotsin a city by use of spatio-temporal analysis and clusteringof user contributed photos. Our framework analyzes thespatial distribution of large city-centered collections of usercontributed photos at different time scales in order to indexthe most popular spots of a city in a time-aware manner.Subsequently, the photo sets belonging to the same spatiotemporalcontext are clustered in order to extract representativephotos for each spot. The resulting applicationenables users to obtain flexible summaries of the most importantspots in a city given a temporal slice (time of theday, month, season). The demonstration will be based on aphoto dataset covering major European cities.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Christos Zigkolis</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cluster-Based Landmark and Event Detection for Tagged Photo Collections</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE MultiMedia</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">52-63</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The rising popularity of photosharingapplications on the Webhas led to the generation of hugeamounts of personal image collections.Browsing through image collections ofsuch magnitude is currently supported by theuse of tags. However, tags suffer from severallimitationsâ€”such as polysemy, lack of uniformity,and spamâ€”thus not presenting an adequatesolution to the problem of contentorganization. Therefore, automated contentorganizationmethods are of particular importanceto improve the content-consumptionexperience. Because itâ€™s common for users to associatetheir photo-captured experiences withsome landmarksâ€”for example, a tourist site oran event, such as a music concert or a gatheringwith friendsâ€”we can view landmarks andevents as natural units of organization forlarge image collections. Itâ€™s for this reasonthat automating the process of detecting suchconcepts in large image sets can enhance theexperience of accessing massive amounts ofpictorial content.In this article, we present a novel scheme forautomatically detecting landmarks and eventsin tagged image collections. Our proposal isbased on the simple yet elegant concept ofimage similarity graphs as a means of combiningmultiple notions of similarity betweenimages in a photo collection; in our case, weuse visual and tag similarity. We perform clusteringon such image similarity graphs bymeans of community detection,1 a processthat identifies on the graph groups of nodesthat are more densely connected to eachother than to the rest of the network. In contrastto conventional clustering schemes suchas k-means or hierarchical agglomerative clustering,community detection is computationallymore efficient and doesnâ€™t require thenumber of clusters to be provided as input. Subsequently,we classify the resulting image clustersas landmarks or events by use of featuresrelated to the temporal, social, and tag characteristicsof image clusters. In the case of landmarks,we also conduct a cluster-merging stepon the basis of spatial proximity to enrich ourlandmark model.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nikolopoulos, Spiros</style></author><author><style face="normal" font="default" size="100%">Giannakidou, Eirini</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author><author><style face="normal" font="default" size="100%">Patras, Ioannis</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Hoi, Steven C. H.</style></author><author><style face="normal" font="default" size="100%">Luo, Jiebo</style></author><author><style face="normal" font="default" size="100%">Boll, Susanne</style></author><author><style face="normal" font="default" size="100%">Xu, Dong</style></author><author><style face="normal" font="default" size="100%">Jin, Rong</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Combining Multi-modal Features for Social Media Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Social Media Modeling and Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">71-96</style></pages><isbn><style face="normal" font="default" size="100%">978-0-85729-435-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Pardede, Eric</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Community Detection in Collaborative Tagging Systems</style></title><secondary-title><style face="normal" font="default" size="100%">Community-Built Databases</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">107-131</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-19046-9</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christos Zigkolis</style></author><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Martinez, José M.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Detecting the long-tail of Points of Interest in tagged photo collections</style></title><secondary-title><style face="normal" font="default" size="100%">CBMI</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">235-240</style></pages><isbn><style face="normal" font="default" size="100%">978-1-61284-433-6</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The paper tackles the problem of matching the photosof a tagged photo collection to a list of â€ślong-tailâ€ť PointsOf Interest (PoIs), that is PoIs that are not very popularand thus not well represented in the photo collection. Despitethe significance of improving â€ślong-tailâ€ť PoI photoretrieval for travel applications, most landmark detectionmethods to date have been tested on very popular landmarks.In this paper, we conduct a thorough empirical analysiscomparing four baseline matching methods that relyon photo metadata, three variants of an approach that usescluster analysis in order to discover PoI-related photo clusters,and a real-world retrieval mechanism (Flickr search)on a set of less popular PoIs.A user-based evaluation of the aforementioned methodsis conducted on a Flickr photo collection of over 100, 000photos from 10 well-known touristic destinations in Greece.A set of 104 â€ślong-tailâ€ť PoIs is collected for these destinationsfrom Wikipedia, Wikimapia and OpenStreetMap. Theresults demonstrate that two of the baseline methods outperformFlickr search in terms of precision and F-measure,whereas two of the cluster-based methods outperform it interms of recall and PoI coverage. We consider the results ofthis study valuable for enhancing the indexing of pictorialcontent in social media sites.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nikolopoulos, Spiros</style></author><author><style face="normal" font="default" size="100%">Chatzilari, Elisavet</style></author><author><style face="normal" font="default" size="100%">Giannakidou, Eirini</style></author><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Bessis, Nik</style></author><author><style face="normal" font="default" size="100%">Xhafa, Fatos</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Leveraging Massive User Contributions for Knowledge Extraction</style></title><secondary-title><style face="normal" font="default" size="100%">Next Generation Data Technologies for Collective Computational Intelligence</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Studies in Computational Intelligence</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">352</style></volume><pages><style face="normal" font="default" size="100%">415-443</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-20343-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Jain, Lakhmi C.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Massive Graph Management for the Web and Web 2.0</style></title><secondary-title><style face="normal" font="default" size="100%">New Directions in Web Data Management 1</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Studies in Computational Intelligence</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">331</style></volume><pages><style face="normal" font="default" size="100%">19-58</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-17550-3</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gabriel, Hans-Henning</style></author><author><style face="normal" font="default" size="100%">Spiliopoulou, Myra</style></author><author><style face="normal" font="default" size="100%">Stachtiari, Emmanouela</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Boissier, Olivier</style></author><author><style face="normal" font="default" size="100%">Benatallah, Boualem</style></author><author><style face="normal" font="default" size="100%">Papazoglou, Mike P.</style></author><author><style face="normal" font="default" size="100%">Ras, Zbigniew W.</style></author><author><style face="normal" font="default" size="100%">Hacid, Mohand-Said</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Summarization Meets Visualization on Online Social Networks</style></title><secondary-title><style face="normal" font="default" size="100%">Web Intelligence</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">communities</style></keyword><keyword><style  face="normal" font="default" size="100%">community representatives</style></keyword><keyword><style  face="normal" font="default" size="100%">social network summarization</style></keyword><keyword><style  face="normal" font="default" size="100%">social network visualization</style></keyword><keyword><style  face="normal" font="default" size="100%">Social networks</style></keyword><keyword><style  face="normal" font="default" size="100%">visualization</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE Computer Society</style></publisher><pages><style face="normal" font="default" size="100%">475-478</style></pages><isbn><style face="normal" font="default" size="100%">978-0-7695-4513-4</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Getting an overview of a large online social networkand deciding which communities to join is a challengingtask for a new user. We propose a method that maps a largenetwork into a smaller graph with two kinds of nodes: a nodeof the first kind is representative of a community; a node ofthe second kind is neighbor to a representative and reflectsthe semantics of that community. Our approach encompassesa learning and ranking algorithm that derives this smallergraph from the original one, and a visualization algorithmthat returns a graph layout to the observer. We report on ourresults on inspecting the network of a folksonomy.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">ul Islam, Saif</style></author><author><style face="normal" font="default" size="100%">Stamos, Konstantinos</style></author><author><style face="normal" font="default" size="100%">Pierson, Jean-Marc</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Kranzlmller, Dieter</style></author><author><style face="normal" font="default" size="100%">Tjoa, A Min</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Utilization-Aware Redirection Policy in CDN: A Case for Energy Conservation</style></title><secondary-title><style face="normal" font="default" size="100%">ICT-GLOW</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CDNs</style></keyword><keyword><style  face="normal" font="default" size="100%">Energy conservation</style></keyword><keyword><style  face="normal" font="default" size="100%">QoE</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">6868</style></volume><pages><style face="normal" font="default" size="100%">180-187</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-23446-0</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Due to the gradual and rapid increase in Information andCommunication Technology (ICT) industry, it is very important to introduce energy efficient techniques and infrastructures in large scale distributed systems. Content Distribution Networks (CDNs) are one of these popular systems which try to make the contents closer to the widely dispersed Internet users. A Content Distribution Network provides its services by using a number of surrogate servers geographicallydistributed in the web. Surrogate servers have the copies of the original contents belonging to the origin server, depending on their storage capacity.When a client requests for some particular contents from a surrogateserver, either this request can be fulfilled directly by it or in case of absence of the requested contents, surrogate servers cooperate with eachother or with the origin server. In this paper, our focus is on the surrogate servers utilization and using it as a parameter to conserve energy in CDNs while trying to maintain an acceptable Quality of Experience (QoE).&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Paparrizos, Ioannis K.</style></author><author><style face="normal" font="default" size="100%">Vassiliki A. Koutsonikola</style></author><author><style face="normal" font="default" size="100%">Angelis, Lefteris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Chignell, Mark H.</style></author><author><style face="normal" font="default" size="100%">Toms, Elaine G.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatic extraction of structure, content and usage data statistics of web sites</style></title><secondary-title><style face="normal" font="default" size="100%">HT</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Crawling</style></keyword><keyword><style  face="normal" font="default" size="100%">Structure Content and Usage data</style></keyword><keyword><style  face="normal" font="default" size="100%">Web Mining Algorithm</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pages><style face="normal" font="default" size="100%">301-302</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4503-0041-4</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this paper we present a web mining tool which automaticallyextracts the structure, content and usage data statistics of websites. This work inspired by the fact that web mining consists ofthree axes: web structure mining, web content mining and webusage mining. Each one of those axes is using the structure,content and usage data respectively. The scope is to use thedeveloped multi-thread web crawler as a tool to automaticallyextract from web pages data that are associated with each one ofthose three axes in order afterwards to compute several usefuldescriptive statistics and apply advanced mathematical andstatistical methods. A description of our system is provided aswell as some experimentation results.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stamos, Konstantinos</style></author><author><style face="normal" font="default" size="100%">Pallis, George</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Katsaros, Dimitrios</style></author><author><style face="normal" font="default" size="100%">Sidiropoulos, Antonis</style></author><author><style face="normal" font="default" size="100%">Manolopoulos, Yannis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CDNsim: A simulation tool for content distribution networks</style></title><secondary-title><style face="normal" font="default" size="100%">ACM Trans. Model. Comput. Simul.</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">caching</style></keyword><keyword><style  face="normal" font="default" size="100%">Content Distribution Network</style></keyword><keyword><style  face="normal" font="default" size="100%">services</style></keyword><keyword><style  face="normal" font="default" size="100%">trace-driven simulation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">20</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Content Distribution Networks (CDNs) have gained considerable attention in the past few years.As such, there is need for developing frameworks for carrying out CDN simulations. In this paper,we present a modeling and simulation framework for CDNs, called CDNsim. CDNsim hasbeen designated to provide a realistic simulation for CDNs, simulating the surrogate servers, theTCP/IP protocol and the main CDN functions. The main advantages of this tool are its high performance,its extensibility and its user interface which is used to configure its parameters. CDNsimprovides an automated environment for conducting experiments and extracting client, server andnetwork statistics. The purpose of CDNsim is to be used as a testbed for CDN evaluation andexperimentation. This is quite useful both for the research community (to experiment with newCDN data management techniques) and for CDN developers (to evaluate profits on prior certainCDN installations).&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Christos Zigkolis</style></author><author><style face="normal" font="default" size="100%">Kapiris, Stefanos</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Bimbo, Alberto Del</style></author><author><style face="normal" font="default" size="100%">Chang, Shih-Fu</style></author><author><style face="normal" font="default" size="100%">Smeulders, Arnold W. M.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">ClustTour: city exploration by use of hybrid photo clustering</style></title><secondary-title><style face="normal" font="default" size="100%">ACM Multimedia</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">event and landmark detection</style></keyword><keyword><style  face="normal" font="default" size="100%">tagging</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pages><style face="normal" font="default" size="100%">1617-1620</style></pages><isbn><style face="normal" font="default" size="100%">978-1-60558-933-6</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present a technical demonstration of an online city explorationapplication that helps users identify interesting spotsin a city by use of photo clusters corresponding to landmarksand events. Our application, called ClustTour, is based onan efficient landmark and event detection scheme for taggedphoto collections. The proposed scheme relies on the combinationof a graph-based photo clustering algorithm, makinguse of both visual and tag information of photos, with acluster classification and merging module. ClustTour createsa map-based visualization of the identified photo clustersthat are classified in prominent categories and are filterableby time and tag. We believe that such an applicationcan greatly facilitate the task of knowing a city through itslandmarks and events. So far, the demo has been based on alarge photo dataset focused on Barcelona, and it is graduallyexpanding to contain photo clusters of several major cities ofEurope. Furthermore, an Android application is developedthat complements the web-based version of ClustTour.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Dynamics of Content Popularity in Social Media</style></title><secondary-title><style face="normal" font="default" size="100%">IJDWM</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Collaborative Technologies</style></keyword><keyword><style  face="normal" font="default" size="100%">Data Mining</style></keyword><keyword><style  face="normal" font="default" size="100%">Electronic Media</style></keyword><keyword><style  face="normal" font="default" size="100%">Online Behavior</style></keyword><keyword><style  face="normal" font="default" size="100%">Online Community</style></keyword><keyword><style  face="normal" font="default" size="100%">Resource Sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">Web-Based Applications</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">20-37</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Social Bookmarking Systems (SBS) have been widely adopted in the last years, and thus they havehad a significant impact on the way that online content is accessed, read and rated. Until recently,the decision on what content to display in a publisherâ€™s web pages was made by one or at most fewauthorities. In contrast, modern SBS-based applications permit their users to submit their preferredcontent, to comment on and to rate the content of other users and establish social relations witheach other. In that way, the vision of the social media is realized, i.e. the online users collectivelydecide upon the interestingness of the available bookmarked content. This article attempts to provideinsights into the dynamics emerging from the process of content rating by the user community.To this end, the article proposes a framework for the study of the statistical properties of an SBS,the evolution of bookmarked content popularity and user activity in time, as well as the impact ofonline social networks on the content consumption behavior of individuals. The proposed analysisframework is applied to a large dataset collected from digg, a popular social media application.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A graph-based clustering scheme for identifying related tags in folksonomies</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 12th international conference on Data warehousing and knowledge discovery</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">DaWaK’10</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">community detection</style></keyword><keyword><style  face="normal" font="default" size="100%">folksonomies</style></keyword><keyword><style  face="normal" font="default" size="100%">graph-based clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">tag recommendation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer-Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Berlin, Heidelberg</style></pub-location><pages><style face="normal" font="default" size="100%">65–76</style></pages><isbn><style face="normal" font="default" size="100%">3-642-15104-3, 978-3-642-15104-0</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The paper presents a novel scheme for graph-based clusteringwith the goal of identifying groups of related tags in folksonomies.The proposed scheme searches for core sets, i.e. groups of nodes thatare densely connected to each other by efficiently exploring the twodimensional core parameter space, and successively expands the identified cores by maximizing a local subgraph quality measure. We evaluate this scheme on three real-world tag networks by assessing the relatedness of same-cluster tags and by using tag clusters for tag recommendation. In addition, we compare our results to the ones derived from a baseline graph-based clustering method and from a popular modularity maximization clustering method.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Christos Zigkolis</style></author><author><style face="normal" font="default" size="100%">Tolias, Giorgos</style></author><author><style face="normal" font="default" size="100%">Kalantidis, Yannis</style></author><author><style face="normal" font="default" size="100%">Mylonas, Phivos</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Image clustering through community detection on hybrid image similarity graphs</style></title><secondary-title><style face="normal" font="default" size="100%">ICIP</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">community detection</style></keyword><keyword><style  face="normal" font="default" size="100%">content-based image retrieval</style></keyword><keyword><style  face="normal" font="default" size="100%">image clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">tags</style></keyword><keyword><style  face="normal" font="default" size="100%">visual similarity</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">2353-2356</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4244-7994-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The wide adoption of photo sharing applications such as FlickrÂ°cand the massive amounts of user-generated content uploaded to themraises an information overload issue for users. An established technique to overcome such an overload is to cluster images into groups based on their similarity and then use the derived clusters to assistnavigation and browsing of the collection. In this paper, we presenta community detection (i.e. graph-based clustering) approach thatmakes use of both visual and tagging features of images in orderto efficiently extract groups of related images within large imagecollections. Based on experiments we conducted on a dataset comprising publicly available images from FlickrÂ°c, we demonstrate the efficiency of our method, the added value of combining visual andtag features and the utility of the derived clusters for exploring animage collection.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Katsaros, Dimitrios</style></author><author><style face="normal" font="default" size="100%">Pallis, George</style></author><author><style face="normal" font="default" size="100%">Stamos, Konstantinos</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Sidiropoulos, Antonis</style></author><author><style face="normal" font="default" size="100%">Manolopoulos, Yannis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CDNs Content Outsourcing via Generalized Communities</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Trans. Knowl. Data Eng.</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">caching</style></keyword><keyword><style  face="normal" font="default" size="100%">content distribution networks</style></keyword><keyword><style  face="normal" font="default" size="100%">replication</style></keyword><keyword><style  face="normal" font="default" size="100%">social network analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">web communities</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">137-151</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Content distribution networks (CDNs) balance costs and quality in services related to content delivery. Devising an efficientcontent outsourcing policy is crucial since, based on such policies, CDN providers can provide client-tailored content, improveperformance, and result in significant economical gains. Earlier content outsourcing approaches may often prove ineffective since theydrive prefetching decisions by assuming knowledge of content popularity statistics, which are not always available and are extremelyvolatile. This work addresses this issue, by proposing a novel self-adaptive technique under a CDN framework on which outsourcedcontent is identified with no a priori knowledge of (earlier) request statistics. This is employed by using a structure-based approachidentifying coherent clusters of â€ścorrelatedâ€ť Web server content objects, the so-called Web page communities. These communities arethe core outsourcing unit, and in this paper, a detailed simulation experimentation has shown that the proposed technique is robust andeffective in reducing user-perceived latency as compared with competing approaches, i.e., two communities-based approaches, Webcaching, and non-CDN.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dikaiakos, Marios D.</style></author><author><style face="normal" font="default" size="100%">Katsaros, Dimitrios</style></author><author><style face="normal" font="default" size="100%">Mehra, Pankaj</style></author><author><style face="normal" font="default" size="100%">Pallis, George</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cloud Computing: Distributed Internet Computing for IT and Scientific Research</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Internet Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">13</style></volume><pages><style face="normal" font="default" size="100%">10-13</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Cloud computing is a recent trend in informationtechnology and networking that has the potentialto change radically the way computer servicesare constructed, managed, and delivered. The key drivingforces behind the emergence of cloud computing includethe overcapacity of todayâ€™s large corporate data centers,the ubiquity of broadband and wireless networking, thefalling cost of storage, and progressive improvements innetworking technologies. Cloud computing opens new perspectiveswith profound implications in the area of communicationnetworks, raising new issues in their architecture,design, and implementation.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stamos, Konstantinos</style></author><author><style face="normal" font="default" size="100%">Pallis, George</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Dikaiakos, Marios D.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Fortino, Giancarlo</style></author><author><style face="normal" font="default" size="100%">Mastroianni, Carlo</style></author><author><style face="normal" font="default" size="100%">Al-Mukaddim Khan Pathan</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluating the utility of content delivery networks</style></title><secondary-title><style face="normal" font="default" size="100%">UPGRADE-CN</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CDN pricing</style></keyword><keyword><style  face="normal" font="default" size="100%">Content Delivery</style></keyword><keyword><style  face="normal" font="default" size="100%">network utility</style></keyword><keyword><style  face="normal" font="default" size="100%">networks</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pages><style face="normal" font="default" size="100%">11-20</style></pages><isbn><style face="normal" font="default" size="100%">978-1-60558-591-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Content Delivery Networks (CDNs) balance costs and qualityin services related to content delivery. This has urgedmany Web entrepreneurs to make contracts with CDNs. Inthe literature, a wide range of techniques has been developed,implemented and standardized for improving the performanceof CDNs. The ultimate goal of all the approachesis to improve the utility of CDN surrogate servers. In thispaper we define a metric which measures the utility of CDNsurrogate servers, called CDN utility. This metric capturesthe traffic activity in a CDN, expressing the usefulness ofsurrogate servers in terms of data circulation in the network.Through an extensive simulation testbed, we identifythe parameters that affect the CDN utility in such infrastructures.We evaluate the utility of surrogate servers undervarious parameters and provide insightful comments.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>34</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Leveraging Collective Intelligence through Community Detection in Tag Networks</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">collective intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">community detection</style></keyword><keyword><style  face="normal" font="default" size="100%">tag networks</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The paper studies the problem of community detectionin tag networks, i.e. networks consisting of associationsbetween tags that are used within Social Tagging Systems(STS) to annotate online resources (e.g. bookmarks,pictures, videos, etc.). Community detectionmethods aim at uncovering densely connected groupsof tags, which can reveal the topic structure emergingin the STS. In this way, community detection in tagnetworks leverages Collective Intelligence (CI), that isthe intelligence that is accumulated as a result of thecollective activities of masses of users.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vassiliki A. Koutsonikola</style></author><author><style face="normal" font="default" size="100%">Petridou, Sophia G.</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Papadimitriou, Georgios I.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A new approach to web users clustering and validation: a divergence-based scheme</style></title><secondary-title><style face="normal" font="default" size="100%">IJWIS</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cluster analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet Data mining</style></keyword><keyword><style  face="normal" font="default" size="100%">User studies</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">348-371</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Purpose â€“ Web usersâ€™ clustering is an important mining task since it contributes in identifying usagepatterns, a beneficial task for a wide range of applications that rely on the web. The purpose of thispaper is to examine the usage of Kullback-Leibler (KL) divergence, an information theoretic distance,as an alternative option for measuring distances in web users clustering.Design/methodology/approach â€“ KL-divergence is compared with other well-known distancemeasures and clustering results are evaluated using a criterion function, validity indices, andgraphical representations. Furthermore, the impact of noise (i.e. occasional or mistaken page visits) isevaluated, since it is imperative to assess whether a clustering process exhibits tolerance in noisyenvironments such as the web.Findings â€“ The proposed KL clustering approach is of similar performance when compared withother distance measures under both synthetic and real data workloads. Moreover, imposing extranoise on real data, the approach shows minimum deterioration among most of the other conventionaldistance measures.Practical implications â€“ The experimental results show that a probabilistic measure such asKL-divergence has proven to be quite efficient in noisy environments and thus constitute a goodalternative, the web users clustering problem.Originality/value â€“ This work is inspired by the usage of divergence in clustering of biological dataand it is introduced by the authors in the area of web clustering. According to the experimental resultspresented in this paper, KL-divergence can be considered as a good alternative for measuringdistances in noisy environments such as the web.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fortino, Giancarlo</style></author><author><style face="normal" font="default" size="100%">Mastroianni, Carlo</style></author><author><style face="normal" font="default" size="100%">Al-Mukaddim Khan Pathan</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Fortino, Giancarlo</style></author><author><style face="normal" font="default" size="100%">Mastroianni, Carlo</style></author><author><style face="normal" font="default" size="100%">Al-Mukaddim Khan Pathan</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Next generation content networks: trends and challenges</style></title><secondary-title><style face="normal" font="default" size="100%">UPGRADE-CN</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pages><style face="normal" font="default" size="100%">49</style></pages><isbn><style face="normal" font="default" size="100%">978-1-60558-591-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Fortino, Giancarlo</style></author><author><style face="normal" font="default" size="100%">Mastroianni, Carlo</style></author><author><style face="normal" font="default" size="100%">Al-Mukaddim Khan Pathan</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Proceedings of the 4th Workshop on the Use of P2P, GRID and Agents for the Development of Content Networks, UPGRADE-CNâ€™09, jointly held with the 18th International Symposium on High-Performance Distributed Computing (HPDC-18 2009), 10 June 2009, Ga</style></title><secondary-title><style face="normal" font="default" size="100%">UPGRADE-CN</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><isbn><style face="normal" font="default" size="100%">978-1-60558-591-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pallis, George</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Pokorny, Jaroslav</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A clustering-based prefetching scheme on a Web cache environment</style></title><secondary-title><style face="normal" font="default" size="100%">Computers &amp; Electrical Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">309-323</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Buyya, Rajkumar</style></author><author><style face="normal" font="default" size="100%">Al-Mukaddim Khan Pathan</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Content Delivery Networks (Lecture Notes Electrical Engineering)</style></title><secondary-title><style face="normal" font="default" size="100%">Content Delivery Networks</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">cdn</style></keyword><keyword><style  face="normal" font="default" size="100%">content</style></keyword><keyword><style  face="normal" font="default" size="100%">lnee</style></keyword><keyword><style  face="normal" font="default" size="100%">networks</style></keyword><keyword><style  face="normal" font="default" size="100%">placement</style></keyword><keyword><style  face="normal" font="default" size="100%">qos</style></keyword><keyword><style  face="normal" font="default" size="100%">replacement</style></keyword><keyword><style  face="normal" font="default" size="100%">replica</style></keyword><keyword><style  face="normal" font="default" size="100%">search</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><edition><style face="normal" font="default" size="100%">1</style></edition><publisher><style face="normal" font="default" size="100%">Springer-Verlag Gmbh</style></publisher><isbn><style face="normal" font="default" size="100%">3540778861</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">**Content Delivery Networks** enables the readers to understand the basics, to identify the underlying technology, to summarize their knowledge on concepts, ideas, principles and various paradigms which span on broad CDNs areas. Therefore, aspects of CDNs in terms of basics, design process, practice, techniques, performances, platforms, applications, and experimental results have been presented in a proper order. Fundamental methods, initiatives, significant research results, as well as references for further study have also been provided. Comparison of different design and development approaches are described at the appropriate places so that new researchers as well as advanced practitioners can use the CDNs evaluation as a research roadmap. All the contributions have been reviewed, edited, processed, and placed in the appropriate order to maintain consistency so that any reader irrespective of their level of knowledge and technological skills in CDNs would get the most out of it. The book is organized into three parts, namely, Part I: CDN Fundamentals; Part II: CDN Modeling and Performance; and Part III: Advanced CDN Platforms and Applications. The organization ensures the smooth flow of material as successive chapters build on prior ones.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vassiliki A. Koutsonikola</style></author><author><style face="normal" font="default" size="100%">Petridou, Sophia G.</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Hacid, Hakim</style></author><author><style face="normal" font="default" size="100%">Benatallah, Boualem</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Bailey, James</style></author><author><style face="normal" font="default" size="100%">Maier, David</style></author><author><style face="normal" font="default" size="100%">Schewe, Klaus-Dieter</style></author><author><style face="normal" font="default" size="100%">Thalheim, Bernhard</style></author><author><style face="normal" font="default" size="100%">Wang, Xiaoyang Sean</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Correlating Time-Related Data Sources with Co-clustering</style></title><secondary-title><style face="normal" font="default" size="100%">WISE</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5175</style></volume><pages><style face="normal" font="default" size="100%">264-279</style></pages><isbn><style face="normal" font="default" size="100%">978-3-540-85480-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A huge amount of data is circulated and collected every dayon a regular time basis. Given a pair of such datasets, it might be possibleto reveal hidden dependencies between them since the presence of the onedataset elements may influence the elements of the other dataset and viceversa. Furthermore, the impact of these relations may last during a periodinstead of the time point of their co-occurrence. Mining such relationsunder those assumptions is a challenging problem. In this paper, we studytwo time-related datasets whose elements are bilaterally affected overtime. We employ a co-clustering approach to identify groups of similarelements on the basis of two distinct criteria: the direction and durationof their impact. The proposed approach is evaluated using time-relatednews and stockâ€™s market real datasets.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pallis, George</style></author><author><style face="normal" font="default" size="100%">Stamos, Konstantinos</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Thomos, Charilaos</style></author><author><style face="normal" font="default" size="100%">Andreadis, George</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Integrating Caching Techniques in CDNs using a Classification Approach</style></title><secondary-title><style face="normal" font="default" size="100%">IJBDCN</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">1-12</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Content Delivery Networks (CDNs) provide an efficient support for serving â€śresourcehungryâ€ťapplications while minimizing the network impact of content delivery as well asshifting the traffic away from overloaded origin servers. However, their performance gain islimited since the storage space in CDNâ€™s servers is not used optimally. In order to managetheir storage capacity in an efficient way, we integrate caching techniques in CDNs. Thechallenge is to decide which objects would be devoted to caching so as the CDNâ€™s server maybe used both as a replicator and as a proxy server. In this paper we propose a nonlinear nonparametricmodel which classifies the CDNâ€™s server cache into two parts. Through a detailedsimulation environment, we show that the proposed technique can yield significant reductionin user-perceived latency as compared with other heuristic schemes.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sidiropoulos, Antonis</style></author><author><style face="normal" font="default" size="100%">Pallis, George</style></author><author><style face="normal" font="default" size="100%">Katsaros, Dimitrios</style></author><author><style face="normal" font="default" size="100%">Stamos, Konstantinos</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Manolopoulos, Yannis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Prefetching in Content Distribution Networks via Web Communities Identification and Outsourcing</style></title><secondary-title><style face="normal" font="default" size="100%">World Wide Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">39-70</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Petridou, Sophia G.</style></author><author><style face="normal" font="default" size="100%">Vassiliki A. Koutsonikola</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Papadimitriou, Georgios I.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Time-Aware Web Users’ Clustering</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Trans. Knowl. Data Eng.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">653-667</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pallis, George</style></author><author><style face="normal" font="default" size="100%">Angelis, Lefteris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Validation and interpretation of Web users’ sessions clusters</style></title><secondary-title><style face="normal" font="default" size="100%">Inf. Process. Manage.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">43</style></volume><pages><style face="normal" font="default" size="100%">1348-1367</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Petridou, Sophia G.</style></author><author><style face="normal" font="default" size="100%">Vassiliki A. Koutsonikola</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Papadimitriou, Georgios I.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Gavrilova, Marina L.</style></author><author><style face="normal" font="default" size="100%">Gervasi, Osvaldo</style></author><author><style face="normal" font="default" size="100%">Kumar, Vipin</style></author><author><style face="normal" font="default" size="100%">Tan, Chih Jeng Kenneth</style></author><author><style face="normal" font="default" size="100%">Taniar, David</style></author><author><style face="normal" font="default" size="100%">LaganĂ , Antonio</style></author><author><style face="normal" font="default" size="100%">Mun, Youngsong</style></author><author><style face="normal" font="default" size="100%">Choo, Hyunseung</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Divergence-Oriented Approach for Web Users Clustering</style></title><secondary-title><style face="normal" font="default" size="100%">ICCSA (2)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">3981</style></volume><pages><style face="normal" font="default" size="100%">1229-1238</style></pages><isbn><style face="normal" font="default" size="100%">3-540-34072-6</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Clustering web users based on their access patterns is a quite significanttask in Web Usage Mining. Further to clustering it is important to evaluatethe resulted clusters in order to choose the best clustering for a particular framework.This paper examines the usage of Kullback-Leibler divergence, aninformation theoretic distance, in conjuction with the k-means clusteringalgorithm. It compares KL-divergence with other well known distance measures(Euclidean, Standardized Euclidean and Manhattan) and evaluates clusteringresults using both objective functionâ€™s value and Davies-Bouldin index.Since it is imperative to assess whether the results of a clustering process aresusceptible to noise, especially in noisy environments such as Web environment,our approach takes the impact of noise into account. The clusters obtainedwith KL approach seem to be superior to those obtained with the otherdistance measures in case our data have been corrupted by noise.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pallis, George</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Insight and Perspectives for Content Delivery Networks</style></title><secondary-title><style face="normal" font="default" size="100%">Commun. ACM</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">imported</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">January</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">1</style></number><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">New York, NY, USA</style></pub-location><volume><style face="normal" font="default" size="100%">49</style></volume><pages><style face="normal" font="default" size="100%">101–106</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stamos, Konstantinos</style></author><author><style face="normal" font="default" size="100%">Pallis, George</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Manolopoulos, Yannis</style></author><author><style face="normal" font="default" size="100%">Pokorny, Jaroslav</style></author><author><style face="normal" font="default" size="100%">Sellis, Timos K.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Integrating Caching Techniques on a Content Distribution Network</style></title><secondary-title><style face="normal" font="default" size="100%">ADBIS</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">4152</style></volume><pages><style face="normal" font="default" size="100%">200-215</style></pages><isbn><style face="normal" font="default" size="100%">3-540-37899-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Web caching and replication tune capacity with performance and theyhave become essential components of the Web. In practice, caching and replicationtechniques have been applied in proxy servers and Content DistributionNetworks (CDNs) respectively. In this paper, we investigate the benefits of integratingcaching policies on a CDNâ€™ s infrastructure. Using a simulation testbed,our results indicate that there is much room for performance improvement interms of perceived latency, hit ratio and byte hit ratio. Moreover, we show thatthe combination of caching with replication fortifies CDNs against flash crowdevents.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pallis, George</style></author><author><style face="normal" font="default" size="100%">Stamos, Konstantinos</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Katsaros, Dimitrios</style></author><author><style face="normal" font="default" size="100%">Sidiropoulos, Antonis</style></author><author><style face="normal" font="default" size="100%">Manolopoulos, Yannis</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Barga, Roger S.</style></author><author><style face="normal" font="default" size="100%">Zhou, Xiaofang</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Replication Based on Objects Load under a Content Distribution Network</style></title><secondary-title><style face="normal" font="default" size="100%">ICDE Workshops</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE Computer Society</style></publisher><pages><style face="normal" font="default" size="100%">53</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stamos, Konstantinos</style></author><author><style face="normal" font="default" size="100%">Pallis, George</style></author><author><style face="normal" font="default" size="100%">Thomos, Charilaos</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Desai, Bipin C.</style></author><author><style face="normal" font="default" size="100%">Gupta, Shyam K.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A similarity based approach for integrated Web caching and content replication in CDNs</style></title><secondary-title><style face="normal" font="default" size="100%">IDEAS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE Computer Society</style></publisher><pages><style face="normal" font="default" size="100%">239-242</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Web caching and content replication techniques emergedto solve performance problems related to the Web. We proposea generic non-parametric heuristic method that integratesboth techniques under a CDN. We provide experimentationshowing that our method outperforms the so farseparate implementations of Web caching and content replication.Moreover, we show that the performance improvementcompared with an existing algorithm is significant. Wetest all these techniques in a simulation environment undera flash crowd event and a workload of a typical lightweightedCDN operation.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pallis, George</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Sidiropoulos, Eythimis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">FRES-CAR: An Adaptive Cache Replacement Policy</style></title><secondary-title><style face="normal" font="default" size="100%">WIRI</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE Computer Society</style></publisher><pages><style face="normal" font="default" size="100%">74-81</style></pages><isbn><style face="normal" font="default" size="100%">0-7695-2414-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Caching Web objects has become a common practicetowards improving content delivery and usersâ€™ servicing.A Web caching framework is characterized by its cachereplacement policy, which identifies the objects (i.e. theelements on a Web page, which include text, graphics,and scripts) to be replaced in a cache upon a requestarrival. In this paper, we present a cache replacementalgorithm (so-called FRES-CAR), which identifies theobjects that should be evicted by considering togetherthree important criteria: objectâ€™s frequency, recency andsize. Experimentation under synthetic workloads hasshown that FRES-CAR achieves higher hit rates whencompared with the most popular and existing algorithms.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pallis, George</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Stamos, Konstantinos</style></author><author><style face="normal" font="default" size="100%">Sidiropoulos, Antonis</style></author><author><style face="normal" font="default" size="100%">Katsaros, Dimitrios</style></author><author><style face="normal" font="default" size="100%">Manolopoulos, Yannis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Latency-Based Object Placement Approach in Content Distribution Networks</style></title><secondary-title><style face="normal" font="default" size="100%">LA-WEB</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE Computer Society</style></publisher><pages><style face="normal" font="default" size="100%">140-147</style></pages><isbn><style face="normal" font="default" size="100%">0-7695-2471-0</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pallis, George</style></author><author><style face="normal" font="default" size="100%">Angelis, Lefteris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Hacid, Mohand-Said</style></author><author><style face="normal" font="default" size="100%">Murray, Neil V.</style></author><author><style face="normal" font="default" size="100%">Ras, Zbigniew W.</style></author><author><style face="normal" font="default" size="100%">Tsumoto, Shusaku</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Model-Based Cluster Analysis for Web Users Sessions</style></title><secondary-title><style face="normal" font="default" size="100%">ISMIS</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Model-Based Cluster Analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">3488</style></volume><pages><style face="normal" font="default" size="100%">219-227</style></pages><isbn><style face="normal" font="default" size="100%">3-540-25878-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">One of the main issues in Web usage mining is the discovery of patternsin the navigational behavior of Web users. Standard approaches, such as clusteringof usersâ€™sessions and discovering association rules or frequent navigational paths,do not generally allow to characterize or quantify the unobservable factors that leadto common navigational patterns. Therefore, it is necessary to develop techniquesthat can discover hidden and useful relationships among users as well as betweenusers and Web objects.Correspondence Analysis(CO-AN) is particularly useful inthis context, since it can uncover meaningful associations among users and pages.We present a model-based cluster analysis for Web users sessions including anovel visualization and interpretation approach which is based on CO-AN.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pallis, George</style></author><author><style face="normal" font="default" size="100%">Stoupa, Konstantina</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Khosrow-Pour, Mehdi</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Storage and Access Control Issues for XML Documents</style></title><secondary-title><style face="normal" font="default" size="100%">Encyclopedia of Information Science and Technology (V)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><publisher><style face="normal" font="default" size="100%">Idea Group</style></publisher><pages><style face="normal" font="default" size="100%">2616-2621</style></pages><isbn><style face="normal" font="default" size="100%">1-59140-553-X</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Papadimitriou, Georgios I.</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Pomportsis, Andreas S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A learning-automata-based controller for client/server systems</style></title><secondary-title><style face="normal" font="default" size="100%">Neurocomputing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">client/server systems</style></keyword><keyword><style  face="normal" font="default" size="100%">learning automata</style></keyword><keyword><style  face="normal" font="default" size="100%">polling policies</style></keyword><keyword><style  face="normal" font="default" size="100%">throughput improvement</style></keyword><keyword><style  face="normal" font="default" size="100%">time-delay</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><volume><style face="normal" font="default" size="100%">61</style></volume><pages><style face="normal" font="default" size="100%">381-394</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Polling policies have been introduced to simplifythe accessing process in client/server systems by acentralized control access scheme. This paper considers aclient/server model which employs a polling policy as itsaccess strategy. We propose a learning-automata-based approachfor polling in order to improve the throughput-delayperformance of the system. Each client has an associatedqueue and the server performs selective polling such thatthe next client to be served is identified by a learning automaton.The learning automaton updates each clientâ€™schoice probability according to the feedback information.Under the considered approach, a clientâ€™s choice probabilityasymptotically tends to be proportional to the probabilitythat this client is ready. Simulation results have shown thatthe proposed polling policy is beneficial in comparison tothe conventional round-robin polling when operating underbursty traffic conditions. The benefits are significant for thedelay reduction in the considered client/server system.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Pokorny, Jaroslav</style></author><author><style face="normal" font="default" size="100%">Dalamagas, Theodore</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Lindner, Wolfgang</style></author><author><style face="normal" font="default" size="100%">Mesiti, Marco</style></author><author><style face="normal" font="default" size="100%">Türker, Can</style></author><author><style face="normal" font="default" size="100%">Tzitzikas, Yannis</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">An Overview of Web Data Clustering Practices</style></title><secondary-title><style face="normal" font="default" size="100%">EDBT Workshops</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Web Data Clustering</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">3268</style></volume><pages><style face="normal" font="default" size="100%">597-606</style></pages><isbn><style face="normal" font="default" size="100%">3-540-23305-9</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Clustering is a challenging topic in the area of Web data management.Various forms of clustering are required in a wide range of applications, includingfinding mirrored Web pages, detecting copyright violations, and reporting searchresults in a structured way. Clustering can either be performed once offline, (independentlyto search queries), or online (on the results of search queries). Importantefforts have focused on mining Web access logs and to cluster search engine resultson the fly. Online methods based on link structure and text have been appliedsuccessfully to finding pages on related topics. This paper presents an overview ofthe most popular methodologies and implementations in terms of clustering eitherWeb users or Web sources and presents a survey about current status and futuretrends in clustering employed over the Web.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pallis, George</style></author><author><style face="normal" font="default" size="100%">Angelis, Lefteris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Pokorny, Jaroslav</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A probabilistic validation algorithm for Web users’ clusters</style></title><secondary-title><style face="normal" font="default" size="100%">SMC (5)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">4129-4134</style></pages><isbn><style face="normal" font="default" size="100%">0-7803-8566-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Pallis, George</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Content Delivery Networks: Status and Trends</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Internet Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><number><style face="normal" font="default" size="100%">6</style></number><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">68-74</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pallis, George</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Angelis, Lefteris</style></author><author><style face="normal" font="default" size="100%">Hacid, Mohand-Said</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Hamza, M. H.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Study on Workload Characterization for a Web Proxy Server</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Informatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Web Caching</style></keyword><keyword><style  face="normal" font="default" size="100%">Web Data Workload Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Web Technologies</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">IASTED/ACTA Press</style></publisher><pages><style face="normal" font="default" size="100%">779-784</style></pages><isbn><style face="normal" font="default" size="100%">0-88986-345-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The popularity of the World-Wide-Web has increaseddramatically in the past few years. Web proxy servershave an important role in reducing server loads, networktraffic, and client request latencies. This paper presentsa detailed workload characterization study of a busyWeb proxy server. The study aims in identifying themajor characteristics which will improve modelling ofWeb proxy accessing. A set of log files is processed forworkload characterization. Throughout the study,emphasis is given on identifying the criteria for a Webcaching model. A statistical analysis, based on theprevious criteria, is presented in order to characterizethe major workload parameters. Results of this analysisare presented and the paper concludes with a discussionabout workload characterization and content deliveryissues.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aref, Walid G.</style></author><author><style face="normal" font="default" size="100%">Catlin, Ann Christine</style></author><author><style face="normal" font="default" size="100%">Elmagarmid, Ahmed K.</style></author><author><style face="normal" font="default" size="100%">Fan, Jianping</style></author><author><style face="normal" font="default" size="100%">Guo, J.</style></author><author><style face="normal" font="default" size="100%">Hammad, Moustafa A.</style></author><author><style face="normal" font="default" size="100%">Ilyas, Ihab F.</style></author><author><style face="normal" font="default" size="100%">Marzouk, Mirette S.</style></author><author><style face="normal" font="default" size="100%">Prabhakar, Sunil</style></author><author><style face="normal" font="default" size="100%">Rezgui, Abdelmounaam</style></author><author><style face="normal" font="default" size="100%">Teoh, S.</style></author><author><style face="normal" font="default" size="100%">Terzi, Evimaria</style></author><author><style face="normal" font="default" size="100%">Tu, Yi-Cheng</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Zhu, Xingquan</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Agrawal, Rakesh</style></author><author><style face="normal" font="default" size="100%">Dittrich, Klaus R.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Distributed Database Server for Continuous Media</style></title><secondary-title><style face="normal" font="default" size="100%">ICDE</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE Computer Society</style></publisher><pages><style face="normal" font="default" size="100%">490-491</style></pages><isbn><style face="normal" font="default" size="100%">0-7695-1531-2</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In our project, we adopt a new approach for handlingvideo data. We view the video as a well-defined datatype with its own description, parameters, and applicablemethods. The system is based on PREDATOR, the opensource object relational DBMS. PREDATOR uses Shoreas the underlying storage manager (SM). Supporting videooperations (storing, searching by content, and streaming)and new query types (query by examples and multi-featuressimilarity search) requires major changes in many ofthe traditional system components. More specifically,the storage and buffer manager will have to deal withhuge volumes of data with real time constraints. Queryprocessing has to consider the video methods and operatorsin generating, optimizing and executing query plans.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Papadimitriou, Georgios I.</style></author><author><style face="normal" font="default" size="100%">Pomportsis, Andreas S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A feedback-based model for I/O servicing</style></title><secondary-title><style face="normal" font="default" size="100%">Computers &amp; Electrical Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">309-322</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Angelis, Lefteris</style></author><author><style face="normal" font="default" size="100%">Pournara, Dimitra</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Internet based auctions: a survey on models and applications</style></title><secondary-title><style face="normal" font="default" size="100%">SIGecom Exchanges</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">6-15</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ilioudis, Christos</style></author><author><style face="normal" font="default" size="100%">Pangalos, George</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Security Model for XML Data</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Internet Computing (1)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Role Based Access Control</style></keyword><keyword><style  face="normal" font="default" size="100%">XML Security</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><pages><style face="normal" font="default" size="100%">400-406</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The significance of XML technology for sharing data over the Internet is being rapidly recognised. In this paper, we examine the security problems related to XML data and present our approach, the XML Security model, for enforcing security policies in XML based Information systems. Our methodology has been based on the study of the XML data model, on the identification of the security requirements of XML Information systems and on the survey of security models which have been proposed to support the conventional data models(relational, object-oriented, hypertext etc). The proposed approach takes into account and exploits the specific characteristics of XML data and incorporates the flexibility of Role based Access Control policies.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Bauknecht, Kurt</style></author><author><style face="normal" font="default" size="100%">Sanjay Kumar Madria</style></author><author><style face="normal" font="default" size="100%">Pernul, Günther</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">LRU-based Algorithms for Web Cache Replacement</style></title><secondary-title><style face="normal" font="default" size="100%">EC-Web</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cache consistency</style></keyword><keyword><style  face="normal" font="default" size="100%">Cache replacement algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Web caching and proxies</style></keyword><keyword><style  face="normal" font="default" size="100%">Web-based information systems</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">1875</style></volume><pages><style face="normal" font="default" size="100%">409-418</style></pages><isbn><style face="normal" font="default" size="100%">3-540-67981-2</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Caching has been introduced and applied in prototype andcommercial Web-based information systems in order to reduce the overallbandwidth and increase systemâ€™s fault tolerance. This paper presents atrack of Web cache replacement algorithms based on the Least RecentlyUsed (LRU) idea. We propose an extension to the conventional LRUalgorithm by considering the number of references to Web objects as acritical parameter for the cache content replacement. The proposed algorithmsare validated and experimented under Web cache traces providedby a major Squid proxy cache server installation environment. Cache andbytes hit rates are reported showing that the proposed cache replacementalgorithms improve cache content.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Papadimitriou, Georgios I.</style></author><author><style face="normal" font="default" size="100%">Pomportsis, Andreas S.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Bubak, Marian</style></author><author><style face="normal" font="default" size="100%">Afsarmanesh, Hamideh</style></author><author><style face="normal" font="default" size="100%">Williams, Roy</style></author><author><style face="normal" font="default" size="100%">Hertzberger, Louis O.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A New Approach to the Design of High Performance Multiple Disk Subsystems: Dynamic Load Balancing Schemes</style></title><secondary-title><style face="normal" font="default" size="100%">HPCN Europe</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">1823</style></volume><pages><style face="normal" font="default" size="100%">610-613</style></pages><isbn><style face="normal" font="default" size="100%">3-540-67553-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The performance of storage subsystems has not followed therapid improvements in processors technology, despite the increased capacityand density in storage medium. Here, we introduce a new modelbased on the idea of enhancing the I/O subsystem controller capabilitiesby dynamic load balancing on a storage subsystem of multiple disk drives.The request servicing is modified such that each request is directed to themost appropriate disk drive towards servicing performance improvement.The redirection is performed by a proposed algorithm which considersthe disk drive queues and the disk drives â€śpopularityâ€ť. The proposed requestservicing has been simulated and the load balancing approach hasbeen shown quite effective as compared to conventional request servicing.</style></abstract></record></records></xml>