www 2009 Madrid

General CFP
| Data Mining | Industrial Practice and Experience | Internet Monetization |
Performance, Scalability and Availability | Rich Media | Search | Security and Privacy |
| Semantic / Data Web | Social Networks and Web 2.0 | User Interfaces and Mobile Web |
|Web Engineering | WWW in Ibero-America | 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:

  • Web content and link mining
  • Query logs, clicks, and web traffic analysis
  • Classification/clustering methods
  • User modeling
  • Spatio-temporal mining
  • Uncertain data mining
  • Change detection and monitoring methods
  • Statistical and machine-learning methods
  • Efficient algorithms for large-scale analysis
  • Privacy issues in web mining
  • Data integration and data cleaning
  • Entity and relationship extraction and disambiguation
  • Integrating linguistic and domain knowledge

Paper formatting requirements are provided on the Submission page.

Track Chairs

  • Ravi Kumar, Yahoo! inc., USA
  • Charu Aggarwal, IBM Watson Research Center, USA

Program Committee

  • Lars Backstrom (Cornell University, USA)
  • Roberto Bayardo (Google, USA)
  • Corinna Cortes (Google, USA)
  • Deepayan Chakrabarti (Yahoo, USA)
  • Yun Chi (NEC Labs, USA)
  • Beng Chin Ooi (National University of Singapore, Singapore)
  • Mayur Datar (Google, USA)
  • Martin Ester (Simon Fraser University, Canada)
  • Christos Faloutsos (CMU, USA)
  • Wei Fan (IBM T.J. Watson Research Center, USA)
  • Ronen Feldman (Hebrew University, Israel)
  • Johannes Gehrke (Cornell University, USA)
  • Srinivas Gollapudi (Microsoft Research, USA)
  • Jiawei Han (University of Illinois at Urbana-Champaign, USA)
  • Andreas Hotho (University of Kassel, Germany)
  • Masaru Kitsuregawa (Universit of Tokyo, Japan)
  • Peer Kroeger (University of Munich, Germany)
  • Kevin Lang (Yahoo, USA)
  • Dongwon Lee (Penn State University, USA)
  • Jure Leskovec (CMU, USA)
  • Bing Liu (University of Illinois at Chicago, USA)
  • Heikki Mannila (University of Helsinki, Finland)
  • Qiaozhu Mei (UIUC, USA)
  • Dunja Mladenic (J. Stefan Institute, Slovenia)
  • Shinichi Morishita (University of Tokyo, Japan)
  • Rajeev Motwani (Stanford University, USA)
  • Alex Ntoulas (Microsoft, USA)
  • Srinivasan Parthasarathy (Ohio State, USA)
  • Jian Pei (Simon Fraser University, Canada)
  • Kunal Punera (Yahoo, USA)
  • Srikant Ramakrishnan (Google, USA)
  • Matthew Richardson (Microsoft, USA)
  • Mark Sandler (Google, USA)
  • Tamas Sarlos (Yahoo, USA)
  • Dou Shen (Microsoft, USA)
  • Kyuseok Shim (Seoul National University, Korea)
  • D Sivakumar (Google, USA)
  • Padhraic Smyth (UC Irvine, USA)
  • Myra Spiliopoulou (Universitat Magdeburg, Germany)
  • Masashi Toyoda (University of Tokyo, Japan)
  • Sergei Vassilvitskii (Yahoo, USA)
  • Haixun Wang (IBM T. J. Watson, USA)
  • Jianyong Wang (Tsinghua University, China)
  • Ji-Rong Wen (Microsoft Research Asia, China)
  • Ryen White (Microsoft, USA)
  • Michael Wurst (IBM Research, Germany)
  • Cheng Xiang Zhai (UIUC, USA)
  • Jeffrey Xu Yu (The Chinese University of Hong Kong, Hong Kong)