<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Antigoni-Maria Founta</style></author><author><style face="normal" font="default" size="100%">Constantinos Djouvas</style></author><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Ilias Leontiadis</style></author><author><style face="normal" font="default" size="100%">Jeremy Blackburn</style></author><author><style face="normal" font="default" size="100%">Gianluca Stringhini</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Michael Sirivianos</style></author><author><style face="normal" font="default" size="100%">Nicolas Kourtellis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior</style></title><tertiary-title><style face="normal" font="default" size="100%">ICWSM-18</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">AAAI</style></publisher><pub-location><style face="normal" font="default" size="100%">Stanford, California</style></pub-location><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In recent years, offensive, abusive and hateful language, sexism, racism and other types of aggressive and cyberbullying behavior have been manifesting with increased frequency, and in many online social media platforms. In fact, past scientific work focused on studying these forms in popular media, such as Facebook and Twitter. Building on such work, we present an 8-month study of the various forms of abusive behavior on Twitter, in a holistic fashion. Departing from past work, we examine a wide variety of labeling schemes, which cover different forms of abusive behavior, at the same time. We propose an incremental and iterative methodology, that utilizes the power of crowdsourcing to annotate a large scale collection of tweets with a set of abuse-related labels. In fact, by applying our methodology including statistical analysis for label merging or elimination, we identify a reduced but robust set of labels. Finally, we offer a first overview and findings of our collected and annotated dataset of 100 thousand tweets, which we make publicly available for further scientific exploration.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Nicolas Kourtellis</style></author><author><style face="normal" font="default" size="100%">Jeremy Blackburn</style></author><author><style face="normal" font="default" size="100%">Emiliano De Cristofaro</style></author><author><style face="normal" font="default" size="100%">Gianluca Stringhini</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%">Detecting Aggressors and Bullies on Twitter</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 26th International Conference on World Wide Web Companion</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">WWW '17 Companion</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">crowdsourcing</style></keyword><keyword><style  face="normal" font="default" size="100%">cyber-aggression</style></keyword><keyword><style  face="normal" font="default" size="100%">cyberbullying</style></keyword><keyword><style  face="normal" font="default" size="100%">Twitter</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dl.acm.org/citation.cfm?id=3054211</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">Perth, Australia</style></pub-location><pages><style face="normal" font="default" size="100%">767--768</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Online social networks constitute an integral part of people's every day social activity and the existence of aggressive and bullying phenomena in such spaces is inevitable. In this work, we analyze user behavior on Twitter in an effort to detect cyberbullies and cuber-aggressors by considering specific attributes of their online activity using machine learning classifiers.&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%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Konstantinos Kafetsios</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Detecting Variation of Emotions in Online Activities</style></title><secondary-title><style face="normal" font="default" size="100%">Expert Systems with Applications</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Emotion detection</style></keyword><keyword><style  face="normal" font="default" size="100%">Hybrid process</style></keyword><keyword><style  face="normal" font="default" size="100%">Lexicon-based approach</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine learning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0957417417305213</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">89</style></volume><pages><style face="normal" font="default" size="100%">318 - 332</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Online text sources form evolving large scale data repositories out of which valuable knowledge about human emotions can be derived. Beyond the primary emotions which refer to the global emotional signals, deeper understanding of a wider spectrum of emotions is important to detect online public views and attitudes. The present work is motivated by the need to test and provide a system that categorizes emotion in online activities. Such a system can be beneficial for online services, companies recommendations, and social support communities. The main contributions of this work are to: (a) detect primary emotions, social ones, and those that characterize general affective states from online text sources, (b) compare and validate different emotional analysis processes to highlight the most efficient, and (c) provide a proof of concept case study to monitor and validate online activity, both explicitly and implicitly. The proposed approaches are tested on three datasets collected from different sources, i.e., news agencies, Twitter, and Facebook, and on different languages, i.e., English and Greek. Study results demonstrate that the methodologies at hand succeed to detect a wider spectrum of emotions out of text sources.&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%">Vasiliki Gkatziaki</style></author><author><style face="normal" font="default" size="100%">Maria Giatsoglou</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></contributors><titles><title><style face="normal" font="default" size="100%">DynamiCITY : Revealing city dynamics from citizens social media broadcasts</style></title><secondary-title><style face="normal" font="default" size="100%">Information Systems</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">crowdsourcing</style></keyword><keyword><style  face="normal" font="default" size="100%">Data Mining</style></keyword><keyword><style  face="normal" font="default" size="100%">Smart City Applications</style></keyword><keyword><style  face="normal" font="default" size="100%">Social Data Mining</style></keyword><keyword><style  face="normal" font="default" size="100%">Urban Dynamics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0306437917300650</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">-</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%">Konstantinos Kafetsios</style></author><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Nikolaos Tsigilis</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%">Experience of emotion in face to face and computer-mediated social interactions: An event sampling study</style></title><secondary-title><style face="normal" font="default" size="100%">Computers in Human Behavior</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computer-mediated communication</style></keyword><keyword><style  face="normal" font="default" size="100%">Emotion</style></keyword><keyword><style  face="normal" font="default" size="100%">FtF</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Social interaction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0747563217304557</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">76</style></volume><pages><style face="normal" font="default" size="100%">287 - 293</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 present study compared the experience of emotion in social interactions that take place face to face (FtF), co-presently, and those that take place online, in computer-mediated communications (CMC). For a period of ten days participants reported how intensely they experienced positive and negative emotions in CMC and in FtF interactions they had with persons from their social network. Results from factor analyses discerned a three factor emotion structure (positive, negative, and anxious emotions) that was largely shared between CMC and FtF social interactions. Multilevel analyses of emotion across modes of interaction found that in FtF social encounters participants experienced more positive and less negative emotion and higher satisfaction than in CMC; there was no difference in anxious emotion. Positive, but not negative emotions or anxiety partially mediated levels of satisfaction differences between interactions in CMC and those taking place FtF. The results point to similarities and differences in emotion experience in FtF and CMC, underlining in particular the affiliative function of positive emotion in peoples' encounters.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Nicolas Kourtellis</style></author><author><style face="normal" font="default" size="100%">Jeremy Blackburn</style></author><author><style face="normal" font="default" size="100%">Emiliano De Cristofaro</style></author><author><style face="normal" font="default" size="100%">Gianluca Stringhini</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%">Hate is not Binary: Studying Abusive Behavior of #GamerGate on Twitter</style></title><tertiary-title><style face="normal" font="default" size="100%">HT '17</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">Prague, Czech Republic</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Over the past few years, online bullying and aggression have become increasingly prominent, and manifested in many different forms on social media. However, there is little work analyzing the characteristics of abusive users and what distinguishes them from typical social media users. In this paper, we start addressing this gap by analyzing tweets containing a great amount of abusiveness. We focus on a Twitter dataset revolving around the Gamergate controversy, which led to many incidents of cyberbullying and cyberaggression on various gaming and social media platforms. We study the properties of the users tweeting about Gamergate, the content they post, and the differences in their behavior compared to typical Twitter users.&lt;/p&gt;

&lt;p&gt;We find that while their tweets are often seemingly about aggressive and hateful subjects, ``Gamergaters'' do not exhibit common expressions of online anger, and in fact primarily differ from typical users in that their tweets are less joyful. They are also more engaged than typical Twitter users, which is an indication as to how and why this controversy is still ongoing. Surprisingly, we find that Gamergaters are less likely to be suspended by Twitter, thus we analyze their properties to identify differences from typical users and what may have led to their suspension. We perform an unsupervised machine learning analysis to detect clusters of users who, though currently active, could be considered for suspension since they exhibit similar behaviors with suspended users. Finally, we confirm the usefulness of our analyzed features by emulating the Twitter suspension mechanism with a supervised learning method, achieving very good precision and recall.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Nicolas Kourtellis</style></author><author><style face="normal" font="default" size="100%">Jeremy Blackburn</style></author><author><style face="normal" font="default" size="100%">Emiliano De Cristofaro</style></author><author><style face="normal" font="default" size="100%">Gianluca Stringhini</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%">Mean Birds: Detecting Aggression and Bullying on Twitter</style></title><tertiary-title><style face="normal" font="default" size="100%">WebSci '17</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/1702.06877</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">Troy, NY, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In recent years, bullying and aggression against users on social media have grown significantly, causing serious consequences to victims of all demographics. In particular, cyberbullying affects more than half of young social media users worldwide, and has also led to teenage suicides, prompted by prolonged and/or coordinated digital harassment. Nonetheless, tools and technologies for understanding and mitigating it are scarce and mostly ineffective. In this paper, we present a principled and scalable approach to detect bullying and aggressive behavior on Twitter. We propose a robust methodology for extracting text, user, and network-based attributes, studying the properties of cyberbullies and aggressors, and what features distinguish them from regular users. We find that bully users post less, participate in fewer online communities, and are less popular than normal users, while aggressors are quite popular and tend to include more negativity in their posts. We evaluate our methodology using a corpus of 1.6M tweets posted over 3 months, and show that machine learning classification algorithms can accurately detect users exhibiting bullying and aggressive behavior, achieving over 90% AUC.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Nicolas Kourtellis</style></author><author><style face="normal" font="default" size="100%">Jeremy Blackburn</style></author><author><style face="normal" font="default" size="100%">Emiliano De Cristofaro</style></author><author><style face="normal" font="default" size="100%">Gianluca Stringhini</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%">Measuring #GamerGate: A Tale of Hate, Sexism, and Bullying</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 26th International Conference on World Wide Web Companion</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">WWW '17 Companion</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dl.acm.org/citation.cfm?id=3053890</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">Perth, Australia</style></pub-location><pages><style face="normal" font="default" size="100%">1285-1290</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Over the past few years, online aggression and abusive behaviors have occurred in many different forms and on a variety of platforms. In extreme cases, these incidents have evolved into hate, discrimination, and bullying, and even materialized into real-world threats and attacks against individuals or groups. In this paper, we study the Gamergate controversy. Started in August 2014 in the online gaming world, it quickly spread across various social networking platforms, ultimately leading to many incidents of cyberbullying and cyberaggression. We focus on Twitter, presenting a measurement study of a dataset of 340k unique users and 1.6M tweets to study the properties of these users, the content they post, and how they differ from random Twitter users. We find that users involved in this &quot;Twitter war&quot; tend to have more friends and followers, are generally more engaged and post tweets with negative sentiment, less joy, and more hate than random users. We also perform preliminary measurements on how the Twitter suspension mechanism deals with such abusive behaviors. While we focus on Gamergate, our methodology to collect and analyze tweets related to aggressive and bullying activities is of independent interest.&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%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Manolis G. Vozalis</style></author><author><style face="normal" font="default" size="100%">Konstantinos Diamantaras</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">George Sarigiannidis</style></author><author><style face="normal" font="default" size="100%">Konstantinos Ch. Chatzisavvas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sentiment analysis leveraging emotions and word embeddings</style></title><secondary-title><style face="normal" font="default" size="100%">Expert Systems with Applications</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Online user reviews</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S095741741630584X</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">69</style></volume><pages><style face="normal" font="default" size="100%">214 - 224</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Abstract Sentiment analysis and opinion mining are valuable for extraction of useful subjective information out of text documents. These tasks have become of great importance, especially for business and marketing professionals, since online posted products and services reviews impact markets and consumers shifts. This work is motivated by the fact that automating retrieval and detection of sentiments expressed for certain products and services embeds complex processes and pose research challenges, due to the textual phenomena and the language specific expression variations. This paper proposes a fast, flexible, generic methodology for sentiment detection out of textual snippets which express people’s opinions in different languages. The proposed methodology adopts a machine learning approach with which textual documents are represented by vectors and are used for training a polarity classification model. Several documents’ vector representation approaches have been studied, including lexicon-based, word embedding-based and hybrid vectorizations. The competence of these feature representations for the sentiment classification task is assessed through experiments on four datasets containing online user reviews in both Greek and English languages, in order to represent high and weak inflection language groups. The proposed methodology requires minimal computational resources, thus, it might have impact in real world scenarios where limited resources is the case.&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%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Gkatziaki, Vasiliki</style></author><author><style face="normal" font="default" size="100%">Vakali, Athena</style></author><author><style face="normal" font="default" size="100%">Anthopoulos, Leonidas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CityPulse: A platform prototype for smart city social data mining</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of the Knowledge Economy</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">344–372</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%">Evangelos Chatzicharalampous</style></author><author><style face="normal" font="default" size="100%">Christos Zigkolis</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%">Exploriometer: Leveraging Personality Traits for Coverage and Diversity Aware Recommendations</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 24th International Conference on World Wide Web Companion, WWW 2015, Florence, Italy, May 18-22, 2015 - Companion Volume</style></secondary-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://doi.acm.org/10.1145/2740908.2742140</style></url></web-urls></urls><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%">Despoina Chatzakou</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%">Harvesting Opinions and Emotions from Social Media Textual Resources</style></title><secondary-title><style face="normal" font="default" size="100%">Internet Computing, IEEE</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adaptation models</style></keyword><keyword><style  face="normal" font="default" size="100%">Analytical models</style></keyword><keyword><style  face="normal" font="default" size="100%">Filtering</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet/Web technologies</style></keyword><keyword><style  face="normal" font="default" size="100%">Media</style></keyword><keyword><style  face="normal" font="default" size="100%">Sentiment analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Text processing</style></keyword><keyword><style  face="normal" font="default" size="100%">textual resources</style></keyword><keyword><style  face="normal" font="default" size="100%">Web 2.0</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">July</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">46-50</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>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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Neil Shah</style></author><author><style face="normal" font="default" size="100%">Alex Beutel</style></author><author><style face="normal" font="default" size="100%">Christos Faloutsos</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%">ND-SYNC: Detecting Synchronized Fraud Activities</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Knowledge Discovery and Data Mining, 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</style></secondary-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/10.1007/978-3-319-18032-8_16</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">201â€“214</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%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Neil Shah</style></author><author><style face="normal" font="default" size="100%">Christos Faloutsos</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%">Cao, Tru</style></author><author><style face="normal" font="default" size="100%">Lim, Ee-Peng</style></author><author><style face="normal" font="default" size="100%">Zhou, Zhi-Hua</style></author><author><style face="normal" font="default" size="100%">Ho, Tu-Bao</style></author><author><style face="normal" font="default" size="100%">Cheung, David</style></author><author><style face="normal" font="default" size="100%">Motoda, Hiroshi</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Retweeting Activity on Twitter: Signs of Deception</style></title><secondary-title><style face="normal" font="default" size="100%">PAKDD (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%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">9077</style></volume><pages><style face="normal" font="default" size="100%">122-134</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-18037-3</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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maria Giatsoglou</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></contributors><titles><title><style face="normal" font="default" size="100%">User communities evolution in microblogs: A public awareness barometer for real world events</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%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer US</style></publisher><pages><style face="normal" font="default" size="100%">1269-1299</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In social media, users' interactions are affected by real-world events which influence emergence and shifts of opinions and topics. Interactions around an event-related topic can be captured in a weighted network, while identification of connectivity and intensity patterns can improve understanding of users' interest on the topic. Community detection is studied here as a means to reveal groups of social media users with common interaction patterns in such networks. The proposed community detection approach identifies communities exploiting both structural properties and intensity patterns, while dynamics of communities' evolution around an event are revealed based on an iterative community detection and mapping scheme. We investigate the importance of considering interactions' intensity for community detection via a benchmarking process on synthetic graphs and propose a generic framework for: i) modeling user interactions, ii) identifying static and evolving communities around events, iii) extracting quantitative and qualitative measurements from the communities' timeline, iv) leveraging measurements to understand the events' impact. Two real-world case studies based on Twitter interactions demonstrate the framework's potential for capturing and interpreting associations among communities and events.&lt;/p&gt;
</style></abstract></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%">Boualem Benatallah</style></author><author><style face="normal" font="default" size="100%">Azer Bestavros</style></author><author><style face="normal" font="default" size="100%">Barbara Catania</style></author><author><style face="normal" font="default" size="100%">Armin Haller</style></author><author><style face="normal" font="default" size="100%">Yannis Manolopoulos</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Yanchun Zhang</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Web Information Systems Engineering - WISE 2014 Workshops - 15th International Workshops IWCSN 2014, Org2 2014, PCS 2014, and QUAT 2014, Thessaloniki, Greece, October 12-14, 2014, Revised Selected Papers</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</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/10.1007/978-3-319-20370-6</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">9051</style></volume><isbn><style face="normal" font="default" size="100%">978-3-319-20369-0</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%">Arvanitidis, Alexandros</style></author><author><style face="normal" font="default" size="100%">Serafi, Anna</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Tsoumakas, Grigorios</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Calders, Toon</style></author><author><style face="normal" font="default" size="100%">Esposito, Floriana</style></author><author><style face="normal" font="default" size="100%">Hullermeier, Eyke</style></author><author><style face="normal" font="default" size="100%">Meo, Rosa</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Branty: A Social Media Ranking Tool for Brands</style></title><secondary-title><style face="normal" font="default" size="100%">ECML/PKDD (3)</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%">8726</style></volume><pages><style face="normal" font="default" size="100%">432-435</style></pages><isbn><style face="normal" font="default" size="100%">978-3-662-44844-1</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%">Kakarontzas, George</style></author><author><style face="normal" font="default" size="100%">Anthopoulos, Leonidas G.</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%">Obaidat, Mohammad S.</style></author><author><style face="normal" font="default" size="100%">Holzinger, Andreas</style></author><author><style face="normal" font="default" size="100%">van Sinderen, Marten</style></author><author><style face="normal" font="default" size="100%">Dolog, Peter</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Conceptual Enterprise Architecture Framework for Smart Cities - A Survey Based Approach</style></title><secondary-title><style face="normal" font="default" size="100%">ICE-B</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%">SciTePress</style></publisher><pages><style face="normal" font="default" size="100%">47-54</style></pages><isbn><style face="normal" font="default" size="100%">978-989-758-043-7</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>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%">Maria Giatsoglou</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%">Lin, Xuemin</style></author><author><style face="normal" font="default" size="100%">Manolopoulos, Yannis</style></author><author><style face="normal" font="default" size="100%">Srivastava, Divesh</style></author><author><style face="normal" font="default" size="100%">Huang, Guangyan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Community Detection in Social Media by Leveraging Interactions and Intensities</style></title><secondary-title><style face="normal" font="default" size="100%">WISE (2)</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%">community detection</style></keyword><keyword><style  face="normal" font="default" size="100%">user weighted interaction networks</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%">8181</style></volume><pages><style face="normal" font="default" size="100%">57-72</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-41153-3</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%">Christos Zigkolis</style></author><author><style face="normal" font="default" size="100%">Karagiannidis, Savvas</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%">Wei Ding</style></author><author><style face="normal" font="default" size="100%">Washio, Takashi</style></author><author><style face="normal" font="default" size="100%">Xiong, Hui</style></author><author><style face="normal" font="default" size="100%">Karypis, George</style></author><author><style face="normal" font="default" size="100%">Thuraisingham, Bhavani M.</style></author><author><style face="normal" font="default" size="100%">Cook, Diane J.</style></author><author><style face="normal" font="default" size="100%">Wu, Xindong</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Dissimilarity Features in Recommender Systems</style></title><secondary-title><style face="normal" font="default" size="100%">ICDM Workshops</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE Computer Society</style></publisher><pages><style face="normal" font="default" size="100%">825-832</style></pages><isbn><style face="normal" font="default" size="100%">978-0-7695-5109-8</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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Despoina Chatzakou</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%">Konstantinos Kafetsios</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Micro-blogging Content Analysis via Emotionally-Driven Clustering</style></title><secondary-title><style face="normal" font="default" size="100%">Affective Computing and Intelligent Interaction (ACII), 2013 Humaine Association Conference on</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">affective analysis methodology</style></keyword><keyword><style  face="normal" font="default" size="100%">Clustering algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">content management</style></keyword><keyword><style  face="normal" font="default" size="100%">content sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">Dictionaries</style></keyword><keyword><style  face="normal" font="default" size="100%">emotion intensity monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">emotionally-driven clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">Equations</style></keyword><keyword><style  face="normal" font="default" size="100%">human emotion states</style></keyword><keyword><style  face="normal" font="default" size="100%">information sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">lexicon-based technique</style></keyword><keyword><style  face="normal" font="default" size="100%">Mathematical model</style></keyword><keyword><style  face="normal" font="default" size="100%">microblogging content analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">pattern clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">people perception</style></keyword><keyword><style  face="normal" font="default" size="100%">Pragmatics</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantics</style></keyword><keyword><style  face="normal" font="default" size="100%">Sentiment analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">social networking (online)</style></keyword><keyword><style  face="normal" font="default" size="100%">social pulse</style></keyword><keyword><style  face="normal" font="default" size="100%">social relations</style></keyword><keyword><style  face="normal" font="default" size="100%">text analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Twitter</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Sept</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">375-380</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%">Samaras, Christos</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Angelis, Lefteris</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Ketikidis, Panayiotis H.</style></author><author><style face="normal" font="default" size="100%">Margaritis, Konstantinos G.</style></author><author><style face="normal" font="default" size="100%">Vlahavas, Ioannis P.</style></author><author><style face="normal" font="default" size="100%">Chatzigeorgiou, Alexander</style></author><author><style face="normal" font="default" size="100%">Eleftherakis, George</style></author><author><style face="normal" font="default" size="100%">Stamelos, Ioannis</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Requirements and architecture design principles for a smart city experiment with sensor and social networks integration</style></title><secondary-title><style face="normal" font="default" size="100%">Panhellenic Conference on Informatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pages><style face="normal" font="default" size="100%">327-334</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4503-1969-0</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%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Vassiliki A. Koutsonikola</style></author><author><style face="normal" font="default" size="100%">Andreadis, George</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Helfert, Markus</style></author><author><style face="normal" font="default" size="100%">Francalanci, Chiara</style></author><author><style face="normal" font="default" size="100%">Filipe, Joaquim</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Social Data Sentiment Analysis in Smart Environments - Extending Dual Polarities for Crowd Pulse Capturing</style></title><secondary-title><style face="normal" font="default" size="100%">DATA</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">SciTePress</style></publisher><pages><style face="normal" font="default" size="100%">175-182</style></pages><isbn><style face="normal" font="default" size="100%">978-989-8565-67-9</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%">Srivastava, Lara</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%">Alvarez, Federico</style></author><author><style face="normal" font="default" size="100%">Cleary, Frances</style></author><author><style face="normal" font="default" size="100%">Daras, Petros</style></author><author><style face="normal" font="default" size="100%">Domingue, John</style></author><author><style face="normal" font="default" size="100%">Galis, Alex</style></author><author><style face="normal" font="default" size="100%">Garcia, Ana</style></author><author><style face="normal" font="default" size="100%">Gavras, Anastasius</style></author><author><style face="normal" font="default" size="100%">Karnouskos, Stamatis</style></author><author><style face="normal" font="default" size="100%">Krco, Srdjan</style></author><author><style face="normal" font="default" size="100%">Li, Man-Sze</style></author><author><style face="normal" font="default" size="100%">Lotz, Volkmar</style></author><author><style face="normal" font="default" size="100%">Müller, Henning</style></author><author><style face="normal" font="default" size="100%">Salvadori, Elio</style></author><author><style face="normal" font="default" size="100%">Sassen, Anne-Marie</style></author><author><style face="normal" font="default" size="100%">Schaffers, Hans</style></author><author><style face="normal" font="default" size="100%">Stiller, Burkhard</style></author><author><style face="normal" font="default" size="100%">Tselentis, Georgios</style></author><author><style face="normal" font="default" size="100%">Turkama, Petra</style></author><author><style face="normal" font="default" size="100%">Zahariadis, Theodore B.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards a Narrative-Aware Design Framework for Smart Urban Environments</style></title><secondary-title><style face="normal" font="default" size="100%">Future Internet Assembly</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%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">7281</style></volume><pages><style face="normal" font="default" size="100%">166-177</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-30240-4</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%">Anthopoulos, Leonidas G.</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%">Alvarez, Federico</style></author><author><style face="normal" font="default" size="100%">Cleary, Frances</style></author><author><style face="normal" font="default" size="100%">Daras, Petros</style></author><author><style face="normal" font="default" size="100%">Domingue, John</style></author><author><style face="normal" font="default" size="100%">Galis, Alex</style></author><author><style face="normal" font="default" size="100%">Garcia, Ana</style></author><author><style face="normal" font="default" size="100%">Gavras, Anastasius</style></author><author><style face="normal" font="default" size="100%">Karnouskos, Stamatis</style></author><author><style face="normal" font="default" size="100%">Krco, Srdjan</style></author><author><style face="normal" font="default" size="100%">Li, Man-Sze</style></author><author><style face="normal" font="default" size="100%">Lotz, Volkmar</style></author><author><style face="normal" font="default" size="100%">Müller, Henning</style></author><author><style face="normal" font="default" size="100%">Salvadori, Elio</style></author><author><style face="normal" font="default" size="100%">Sassen, Anne-Marie</style></author><author><style face="normal" font="default" size="100%">Schaffers, Hans</style></author><author><style face="normal" font="default" size="100%">Stiller, Burkhard</style></author><author><style face="normal" font="default" size="100%">Tselentis, Georgios</style></author><author><style face="normal" font="default" size="100%">Turkama, Petra</style></author><author><style face="normal" font="default" size="100%">Zahariadis, Theodore B.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Urban Planning and Smart Cities: Interrelations and Reciprocities</style></title><secondary-title><style face="normal" font="default" size="100%">Future Internet Assembly</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%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">7281</style></volume><pages><style face="normal" font="default" size="100%">178-189</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-30240-4</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%">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>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%">Vassiliki A. Koutsonikola</style></author><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Karagiannidis, Savvas</style></author><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Kosmatopoulos, Andreas</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%">Liarokapis, Fotis</style></author><author><style face="normal" font="default" size="100%">Doulamis, Anastasios D.</style></author><author><style face="normal" font="default" size="100%">Vescoukis, Vassilios</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards a User-Aware Virtual Museum</style></title><secondary-title><style face="normal" font="default" size="100%">VS-GAMES</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">user groups</style></keyword><keyword><style  face="normal" font="default" size="100%">user preferences</style></keyword><keyword><style  face="normal" font="default" size="100%">virtual museum</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%">228-235</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4577-0316-4</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%">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>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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Moussiades, 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%">Flory, Andre</style></author><author><style face="normal" font="default" size="100%">Collard, Martine</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Benchmark graphs for the evaluation of Clustering Algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">RCIS</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Artificial graph</style></keyword><keyword><style  face="normal" font="default" size="100%">Community structure</style></keyword><keyword><style  face="normal" font="default" size="100%">Graph clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">Intra linkage ratio</style></keyword><keyword><style  face="normal" font="default" size="100%">Modularity</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">197-206</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4244-2864-9</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Artificial graphs are commonly used for theevaluation of community mining and clustering algorithms. Eachartificial graph is assigned a pre-specified clustering, which iscompared to clustering solutions obtained by the algorithmsunder evaluation. Hence, the pre-specified clustering shouldcomply with specifications that are assumed to delimit a goodclustering. However, existing construction processes for artificialgraphs do not set explicit specifications for the pre-specifiedclustering. We call these graphs, randomly clustered graphs.Here, we introduce a new class of benchmark graphs which areclustered according to explicit specifications. We call themoptimally clustered graphs. We present the basic properties ofoptimally clustered graphs and propose algorithms for theirconstruction. Experimentally, we compare two communitymining algorithms using both randomly and optimally clusteredgraphs. Results of this evaluation reveal interesting insights bothfor the algorithms and the artificial graphs.&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%">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%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Barbara Catania</style></author><author><style face="normal" font="default" size="100%">Anna Maddalena</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">XML Data Stores: Emerging Practices</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%">2005</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">62-69</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%">Barbara Catania</style></author><author><style face="normal" font="default" size="100%">Anna Maddalena</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%">XML document indexes: a classification</style></title><secondary-title><style face="normal" font="default" size="100%">Internet Computing, IEEE</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">documents indexing</style></keyword><keyword><style  face="normal" font="default" size="100%">xml</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">64–71</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;XML’s increasing diffusion makes efficient XML query processing and indexing all the more critical. Given the semistructured nature of XML documents, however, general query processing techniques won’t work. Researchers have proposed several specialized indexing methods that offer query processors efficient access to XML documents, although none are yet fully implemented in commercial products. In this article the classification of XML indexing techniques identifies current practices and trends, offering insight into how developers can improve query processing and select the best solution for particular contexts.&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%">Barbara Catania</style></author><author><style face="normal" font="default" size="100%">Anna Maddalena</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%">XML Document Indexes: A Classification</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%">2005</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">64-71</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%">Hammiche, Samira</style></author><author><style face="normal" font="default" size="100%">Benbernou, Salima</style></author><author><style face="normal" font="default" size="100%">Hacid, Mohand-Said</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%">Chen, Shu-Ching</style></author><author><style face="normal" font="default" size="100%">Shyu, Mei-Ling</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic retrieval of multimedia data</style></title><secondary-title><style face="normal" font="default" size="100%">MMDB</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Approximation Ontologies</style></keyword><keyword><style  face="normal" font="default" size="100%">MPEG-7</style></keyword><keyword><style  face="normal" font="default" size="100%">Multimedia Data</style></keyword><keyword><style  face="normal" font="default" size="100%">Tree embedding</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pages><style face="normal" font="default" size="100%">36-44</style></pages><isbn><style face="normal" font="default" size="100%">1-58113-975-6</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper deals with the problem of finding multimediadata that fulfill the requirements of user queries. We assumeboth the user query and the multimedia data are expressedby MPEG-7 standard. The MPEG-7 formalism lacks thesemantics and reasoning support in many ways. For example,the search of the implicit data can not be achieved,due to its description based on XML schema. We propose aframework for querying multimedia data based on a tree embeddingapproximation algorithm, combining the MPEG-7standard and an ontology&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></records></xml>