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Refereed Papers
| Browsers and User Interfaces |
Data Mining |
Industrial Practice and Experience |
| Internet Monetization |
Mobility |
Performance and Scalability |
Rich Media |
Search |
| Security and Privacy |
Semantic / Data Web |
Social Networks and Web 2.0 |
| Technology for Developing Regions |
Web Engineering |
WWW in China |
XML and Web Data |
Developers Track |
Panels |
Posters |
Tutorials |
Workshops
Data Mining
The explosive growth of the web has led to an ever-increasing volume of data and information being published in web-accessible formats. Research in web data mining aims to develop new techniques to derive actionable knowledge and information from these sources. Due to heterogeneity and lack of structure in web data, automated discovery of targeted or unexpected knowledge is a challenging task. It calls for novel methods that draw from a wide range of fields such as algorithms, data mining, machine learning, natural language processing, statistics, databases, and information retrieval.
For the data mining track, we invite original and high quality submissions addressing all aspects of web data mining. The relevant topics in the context of web data include, but are not restricted to, the following:
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Web content and link mining
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Query logs, clicks, and web traffic analysis
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Classification/clustering methods
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User modeling
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Spatio-temporal mining
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Change detection and monitoring methods
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Statistical and machine-learning methods
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Efficient algorithms for large-scale analysis
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Privacy issues in web mining
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Data integration and data cleaning
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Entity and relationship extraction and disambiguation
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Integrating linguistic and domain knowledge
Paper formatting requirements will be provided on the submissions page.
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Program Committee:
Alex Ntoulas (Microsoft)
Andreas Hotho (University of Kassel)
Aristides Gionis (Yahoo! Research Barcelona)
Beng Chin Ooi (National University of Singapore)
Bing Liu (University of Illinois at Chicago)
Charu Aggarwal (IBM)
ChengXiang Zhai (UIUC)
Christos Faloutsos (Carnegie Mellon University)
Corinna Cortes (Google Research)
Daniel Kifer (Cornell University)
Deepayan Chakrabarti (Yahoo! Research)
Dongwon Lee (The Pennsylvania State University)
Dou Shen (Hong Kong University of Science and Technology)
Dunja Mladenic (J.Stefan Institute)
Heikki Mannila (University of Helsinki)
Inderjit Dhillon (University of Texas)
Jeffrey Xu Yu (Chinese University of Hong Kong)
Jian Pei (Simon Fraser University)
Jiawei Han (University of Illinois at Urbana-Champaign)
Ji-Rong Wen (Microsoft Research Asia)
Jure Leskovec (Carnegie Mellon University)
Kevin Lang (Yahoo! Research)
Kunal Punera (University of Texas at Austin)
Kyuseok Shim (Seoul National University)
Mark Manasse (Microsoft Research)
Mark Sandler (Google)
Masashi Toyoda (University of Tokyo)
Matthew Richardson (Microsoft Research)
Mayur Datar
Michael Wurst (University of Dortmund, AI Unit)
Myra Spiliopoulou (U. Magdeburg)
Qiaozhu Mei (University of Illinois at Urbana-Champaign)
Rajeev Rastogi (Bell Labs Research India)
Rayid Ghani (Accenture Technology Labs)
Roberto Bayardo (Google)
Ronen Feldman (Bar-Ilan University)
Shinichi Morishita (University of Tokyo)
Srinivasan Parthasarathy (Dept. of CS&E., The Ohio State University)
Srujana Merugu (Yahoo! Research)
Thomas Hofmann (Google)
Vitor Carvalho (Carnegie Mellon University)
Wei Fan (IBM Watson)
William Cohen (Carnegie Mellon University)
