<?xml version="1.0" encoding="UTF-8"?><xml><records><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%">Maaradji, Abderrahmane</style></author><author><style face="normal" font="default" size="100%">Hacid, Hakim</style></author><author><style face="normal" font="default" size="100%">Skraba, Ryan</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%">Social Web Mashups Full Completion via Frequent Sequence Mining</style></title><secondary-title><style face="normal" font="default" size="100%">SERVICES</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Mashups</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence mining</style></keyword><keyword><style  face="normal" font="default" size="100%">Social networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Web services</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%">9-16</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4577-0879-4</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 address the problem of WebMashups full completion which consists of predicting themost suitable set of (combined) services that successfully meetthe goals of an end-user Mashup, given the current service(or composition of services) initially supplied. We model fullcompletion as a frequent sequence mining problem and weshow how existing algorithms can be applied in this context.To overcome some limitations of the frequent sequence miningalgorithms, e.g., efficiency and recommendation granularity,we propose FESMA, a new and efficient algorithm for computingfrequent sequences of services and recommending completions.FESMA also integrates a social dimension, extractedfrom the transformation of user ? service interactions intouser ? user interactions, building an implicit graph thathelps to better predict completions of services in a fashiontailored to individual users. Evaluations show that FESMAis more efficient outperforming the existing algorithms evenwith the consideration of the social dimension. Our proposalhas been implemented in a prototype, SoCo, developed at BellLabs.&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%">Vassiliki A. Koutsonikola</style></author><author><style face="normal" font="default" size="100%">Petridou, Sophia G.</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Hacid, Hakim</style></author><author><style face="normal" font="default" size="100%">Benatallah, Boualem</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Bailey, James</style></author><author><style face="normal" font="default" size="100%">Maier, David</style></author><author><style face="normal" font="default" size="100%">Schewe, Klaus-Dieter</style></author><author><style face="normal" font="default" size="100%">Thalheim, Bernhard</style></author><author><style face="normal" font="default" size="100%">Wang, Xiaoyang Sean</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Correlating Time-Related Data Sources with Co-clustering</style></title><secondary-title><style face="normal" font="default" size="100%">WISE</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5175</style></volume><pages><style face="normal" font="default" size="100%">264-279</style></pages><isbn><style face="normal" font="default" size="100%">978-3-540-85480-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A huge amount of data is circulated and collected every dayon a regular time basis. Given a pair of such datasets, it might be possibleto reveal hidden dependencies between them since the presence of the onedataset elements may influence the elements of the other dataset and viceversa. Furthermore, the impact of these relations may last during a periodinstead of the time point of their co-occurrence. Mining such relationsunder those assumptions is a challenging problem. In this paper, we studytwo time-related datasets whose elements are bilaterally affected overtime. We employ a co-clustering approach to identify groups of similarelements on the basis of two distinct criteria: the direction and durationof their impact. The proposed approach is evaluated using time-relatednews and stockâ€™s market real datasets.&lt;/p&gt;
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