<div dir="ltr"><div><div>************************************************************************************************************</div><div> CFP: International Workshop On Scalable Data Analytics: Theory & Applications (SDATA)</div><div> In conjunction with WSDM 2015, Shanghai, China, Feb 6, 2015</div><div> <a href="http://sdata-wsdm-shanghai2015.weebly.com/">http://sdata-wsdm-shanghai2015.weebly.com/</a></div><div> Paper Submission Deadline: November 14, 2014</div><div>************************************************************************************************************</div><div><br></div><div>With the fast evolving technology for data collection, data transmission, and data analysis,</div><div>the scientific, biomedical, and engineering research communities are undergoing a profound</div><div>transformation where discoveries and innovations increasingly rely on massive amounts of data.</div><div>New prediction techniques, including novel statistical, mathematical, and modeling techniques </div><div>are enabling a paradigm shift in scientific and biomedical investigation. Data become the fourth </div><div>pillar of science and engineering, offering complementary insights in addition to theory, experiments, </div><div>and computer simulation. Advances in machine learning, data mining, and visualization are enabling </div><div>new ways of extracting useful information from massive data sets. The characteristics of volume, </div><div>velocity, variety and veracity bring challenges to current data analytics techniques. It is desirable to </div><div>scale up data analytics techniques for modeling and analyzing big data from various domains. The </div><div>workshop aims to provide professionals, researchers, and technologists with a single forum where </div><div>they can discuss and share the state-of-the-art theories and applications of scalable data analytics </div><div>technologies.</div><div><br></div><div>Topic of Interest</div><div>=============</div><div>Topics of interest include, but not limited to, the following aspects:</div><div><br></div><div>*Distributed data analytics architectures</div><div>- Data analytics algorithms for GPUs</div><div>- Data analytics algorithms for clouds</div><div>- Data analytics algorithms for clusters</div><div><br></div><div>* Theory and algorithms for scalable descriptive statistical modeling</div><div>- Structured, semi-structured, unstructured data preprocessing</div><div>- Effective data sampling and feature engineering</div><div>- Data calibration and transformation</div><div>- Data qualitative quantitative measurement and validation</div><div><br></div><div>* Theory and algorithms of scalable predictive statistical modeling</div><div>- Association analysis</div><div>- Data approximation, dimensional reduction, clustering</div><div>- Liner / non-linear models for classification, regression, and ranking</div><div>- Multiview learning, multitask learning, transfer learning, semi-supervised learning, active</div><div> learning techniques for multimodal data</div><div><br></div><div>* Scalable analytics techniques for temporal and spatial data</div><div>- Real time analysis for data stream</div><div>- Trend prediction in financial data</div><div>- Topic detection in instant message systems</div><div>- Real time modeling of events in dynamic networks</div><div>- Spatial modeling on maps</div><div><br></div><div>* Scalable data analytics algorithms in large graphs</div><div>- Communities discovery and analysis in social networks</div><div>- Link prediction in networks</div><div>- Anomaly detection in social networks</div><div>- Authority identification and influence measurement in social networks</div><div>- Fusion of information from multiple blogs, rating systems, and social networks</div><div>- Integration of text, videos, images, sounds in social media</div><div>- Recommender systems</div><div><br></div><div>* Novel applications of scalable machine learning in big data</div><div> - Decision making with big data</div><div> - Counterfactual reasoning with big data</div><div> - Medical / health informatics big data analysis</div><div> - Security big data analysis</div><div> - Astronomy big data analysis</div><div> - Biological big data analysis</div><div> - Urban / smart city big data analysis</div><div> - Education big data analysis</div><div><br></div><div>Paper Submission</div><div>=============</div><div>Submissions must represent new and original work. Concurrent submissions are not allowed. Submissions</div><div>that have been previously presented in venues with no formal proceedings or as posters are allowed, but</div><div>must be so indicated on the first page of the submission. Papers must be formatted for US Letter size</div><div>according to ACM guidelines and style files, must fit within 10 pages (with a font size no smaller than</div><div>9pt), including references, diagrams, and appendices if any. A submitted paper must be self-contained</div><div>and in English. Submit papers via the link <a href="https://www.easychair.org/conferences/?conf=sdata2015">https://www.easychair.org/conferences/?conf=sdata2015</a>.</div><div><br></div><div>Important Dates</div><div>=============</div><div>* November 14, 2014: Due date for full workshop papers submission</div><div>* December 5, 2014: Notification of paper acceptance to authors</div><div>* December 19, 2014: Camera-ready & registration of accepted papers</div><div>* February 6, 2015: Workshops</div><div><br></div><div><br></div><div>Organizers</div><div>=============</div><div>Kaizhu HUANG, Xi'an Jiaotong-Liverpool University</div><div>Haiqin YANG, The Chinese University of Hong Kong</div><div>Irwin KING, The Chinese University of Hong Kong</div><div>Michael LYU, The Chinese University of Hong Kong</div></div><div><br></div><div><div class="gmail_signature">--------------<br>Best Regards,<br>Haiqin<br></div></div>
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