<html><head></head><body dir="auto" style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;"><br><blockquote type="cite"><br>** Please forward to anyone who might be interested ** <br>--------------------------------------------------------------<br> CALL FOR PAPERS<br>WWW2015 Workshop on Web Data Science and Smart Cities<br><br> to be held on May 18, 2015, Florence, Italy<br> co-located with World Wide Web Conference 2015<br><br> http://www.data2.cn/WebDataScience4SC/<br>--------------------------------------------------------------<br><br>Important Dates:<br>================<br>** Submission Deadline: Jan 17, 2015 (23:59 Hawaii Standard Time)<br>** Notification of Acceptance: Feb 15, 2015<br>** Camera-Ready Versions Due: Mar 1, 2015<br>** Workshop Date: May 18, 2015<br><br>Workshop Organizers:<br>====================<br>Jeff. Z Pan, Aberdeen University, UK. <br>Huajun Chen, Zhejiang University, China. <br>Freddy Lecue, IBM Research Ireland, Smarter Cities Technology Centre, Ireland. <br>Achille Fokoue, IBM Thomas J. Watson Research Center, USA.<br>Daning Hu, University of Zurich, Switzerland.<br><br>The Web has been developing rapidly to be the largest data repository in the world ever since before. Typical types of Web data include social web data, semantic web data, sensor web data, mobile web data and many other forms of open data on the Web. These types of data sets are being collected and posted onto the Web through various technologies such as Web services, social media, Wikipedia, open linked data, the sensor Web, etc. Additionally, significant advancements have been achieved in web technologies for publishing collecting, storing, transferring, analyzing and visualizing all kinds of Web data. These include web mining, the Semantic Web, mobile web, web of services, web of things, etc. <br><br>Data Science is the study and practice of extracting additional knowledge and deriving valuable insights from data. We believe the Web could and should become the largest data sources and open media for all kinds of Data Science approaches. Developing Web-based theories, methods and right analytics tools for data science will enable data scientist to utilize the Web data better in various applications such as Smart Cities. <br><br>In Smarter Cities, available resources are harnessed safely, sustainably and efficiently to achieve positive, measurable economic and societal outcomes. Data (and then information) from people, systems and things in cities is the single most scalable resource available to Cities stakeholders but difficult to publish, organize, discover, interpret, combine, analyze, reason and consume, especially in such a heterogeneous environment. <br>Indeed, the Web is becoming one of the major media for city data exposed from heterogeneous environments such as water, energy, traffic or building. Enabling Cities data as a utility, through the Web, which is a robust (expressive, dynamic, scalable) and (critically) a sustainable technology and socially synergistic ecosystem, and develop web-based data analytics and data science approaches, could drive significant benefits and opportunities for a number of smart cities applications. <br><br>The workshop will accept papers on foundational aspects and common technologies for web data science, as well as papers on smart cities applications to showcase the benefits of Web data or Web-based data analytics approaches in domains as diverse as smart mobility, smart urban planning, smart energy, smart data, urban disaster monitoring, etc. will also be considered. The topics are listed but not restricted as below: <br><span class="Apple-tab-span" style="white-space:pre"> </span>•City Data as Utility over the Web<br><span class="Apple-tab-span" style="white-space:pre"> </span>•City Data Publishing and Management over the Web <br><span class="Apple-tab-span" style="white-space:pre"> </span>•Social Web Data Analytics for Smart Cities <br><span class="Apple-tab-span" style="white-space:pre"> </span>•Web Ontologies and Linked Data for Smart Cities <br><span class="Apple-tab-span" style="white-space:pre"> </span>•Web Mining / Learning and Data Analytics for Smart Cities <br><span class="Apple-tab-span" style="white-space:pre"> </span>•Web of Things and Sensor Data for Smart Cities <br><span class="Apple-tab-span" style="white-space:pre"> </span>•Ensemble of open Web data and Enterprise Internal Data for Smart Cities <br><br>We particularly welcome those papers making use of Web Data or Web-based data science <br>approaches (at least partly) for the following Smart Cities Applications at large scale: <br><br><span class="Apple-tab-span" style="white-space:pre"> </span>•Web Data Science for Smart mobility <br><span class="Apple-tab-span" style="white-space:pre"> </span>•Web Data Science for Smart urban planning <br><span class="Apple-tab-span" style="white-space:pre"> </span>•Web Data Science for Smart energy <br><span class="Apple-tab-span" style="white-space:pre"> </span>•Web Data Science for Smart urban disaster monitoring <br><span class="Apple-tab-span" style="white-space:pre"> </span>•Web Data Science for Smart parking <br><span class="Apple-tab-span" style="white-space:pre"> </span>•Web Data Science for Smart traffic management <br><span class="Apple-tab-span" style="white-space:pre"> </span>•Web Data Science for Smart building <br><span class="Apple-tab-span" style="white-space:pre"> </span>•Web Data Science for Smart Data & Smart System <br><br><br>Proceedings:<br>============<br>Contributions will be included in the Companion volume of the ACM<br>WWW2015 conference, which will be published by ACM and included<br>in the ACM Digital Library.<br>However, to make that happen at least one author of the accepted<br>paper has to register. At the time of submission of the final<br>camera-ready copy, authors will have to indicate the already<br>registered person for that publication.<br><br>Any paper published by the ACM, IEEE, etc. which can be properly<br>cited constitutes research which must be considered in judging the<br>novelty of a WWW submission, whether the published paper was in a<br>conference, journal, or workshop. Therefore, any paper previously<br>published as part of a WWW workshop must be referenced and suitably<br>extended with new content to qualify as a new submission to the<br>Research Track at the WWW conference.<br><br>Submission guidelines:<br>======================<br>All submitted papers must<br> * be written in English;<br> * contain author names, affiliations, and email addresses;<br> * be formatted according to the ACM SIG Proceedings template<br><span class="Apple-tab-span" style="white-space:pre"> </span>(http://www.acm.org/sigs/publications/proceedings-templates)<br><span class="Apple-tab-span" style="white-space:pre"> </span>with a font size no smaller than 9pt;<br> * be in PDF (make sure that the PDF can be viewed on any<br><span class="Apple-tab-span" style="white-space:pre"> </span>platform), and formatted for US Letter size;<br> * occupy no more than six pages, including the abstract,<br><span class="Apple-tab-span" style="white-space:pre"> </span>references, and appendices.<br><br>It is the authors responsibility to ensure that their submissions<br>adhere strictly to the required format.<br>Submissions that do not comply with the above guidelines may be<br>rejected without review.<br><br>All submissions must be entered into the reviewing system:<br>https://easychair.org/conferences/?conf=wds4sc<br><br>Extended version of accepted articles are possible to be selected and included in a special issue with relevant theme of Elsevier Journal of Big Data Research, or some other journals. <br><br>Contact:<br>========<br>Huajun Chen - huajunsir@zju.edu.cn<br>Jeff Pan - jeff.z.pan@abdn.ac.uk <br><br>_______________________________________________<br>Please do not post msgs that are not relevant to the database community at large. 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