<?xml version="1.0" encoding="UTF-8"?><xml><records><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%">Moustaka, Vaia</style></author><author><style face="normal" font="default" size="100%">Vakali, Athena</style></author><author><style face="normal" font="default" size="100%">Anthopoulos, Leonidas G.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Systematic Review for Smart City Data Analytics</style></title><secondary-title><style face="normal" font="default" size="100%">Computing Surveys (CSUR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><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%">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>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vakali, Athena</style></author><author><style face="normal" font="default" size="100%">Kitmeridis, Nikolaos</style></author><author><style face="normal" font="default" size="100%">Panourgia, Maria</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Angelov, Plamen</style></author><author><style face="normal" font="default" size="100%">Manolopoulos, Yannis</style></author><author><style face="normal" font="default" size="100%">Iliadis, Lazaros</style></author><author><style face="normal" font="default" size="100%">Roy, Asim</style></author><author><style face="normal" font="default" size="100%">Vellasco, Marley</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Distributed Framework for Early Trending Topics Detection on Big Social Networks Data Threads</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Big Data: Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-47898-2_20</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><pages><style face="normal" font="default" size="100%">186–194</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-47898-2</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Social networks have become big data production engines and their analytics can reveal insightful trending topics, such that hidden knowledge can be utilized in various applications and settings. This paper addresses the problem of popular topics’ and trends’ early prediction out of social networks data streams which demand distributed software architectures. Under an online time series classification model, which is implemented in a flexible and adaptive distributed framework, trending topics are detected. Emphasis is placed on the early detection process and on the performance of the proposed framework. The implemented framework builds on the lambda architecture design and the experimentation carried out highlights the usefulness of the proposed approach in early trends detection with high rates in performance and with a validation aligned with a popular microblogging service.&lt;/p&gt;
</style></abstract></record></records></xml>