-------- Original-Nachricht -------- Betreff: [computational.science] CFP: ACM SAC 2007 Track on Mining Data Streams (March 11-15, 2007, Seoul, Korea) Datum: Fri, 4 Aug 2006 15:31:53 +0200 Von: Franciso Ferrer ferrer@lsi.us.es Organisation: "OptimaNumerics" An: Computational Science Mailing List computational.science@lists.optimanumerics.com

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C A L L F O R P A P E R S

Special Track on MINING DATA STREAMS

ACM SAC 2007 The 22nd ACM Symposium on Applied Computing,

March 11-15, 2007, Seoul, Korea

http://www.acm.org/conferences/sac/sac2007/ http://www.niaad.liacc.up.pt/%7Ejgama/SAC07/ws.html

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IMPORTANT DATES

Full paper submission: 8 September 2006 Author notification: 16 October 2006 Camera-ready copy: 30 November 2006

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THE MINING DATA STREAMS TRACK

For the past twenty-one years, the ACM Symposium on Applied Computing has been a primary gathering forum for applied computer scientists, computer engineers, software engineers, and application developers from around the world. The ACM SAC conference is solely sponsored by the ACM Special Interest Group on Applied Computing (SIGAPP). The 22nd Annual SAC will be held in Seoul, Korea, between 11 and 15 April 2007 and is hosted by Seoul National University in Seoul and The Suwon University in Gyeonggi-do.

The rapid growth in information science and technology in general and the complexity and volume of data in particular have introduced new challenges for the research community. Databases are growing incessantly and, in many cases, we need to extract some sort of knowledge from this continuous stream of data. The goal of this workshop is to convene researchers who deal with decision rules, decision trees, association rules, clustering, filtering, preprocessing, post processing, feature selection, visualization techniques, etc. from data streams and related themes. Many sources produce data continuously. Examples include customer click streams, telephone records, large sets of web pages, multimedia data, and sets of retail chain transactions. These sources are called data streams. If the process is not strictly stationary (as most of real world applications), the target concept could gradually change over time. This is an incremental task that requires incremental learning algorithms that take drift into account.

Data streams are increasingly important in the research community, as new algorithms are needed to process this streaming data in reasonable time. Many researchers coming from different areas (data mining, machine learning, OLAP, databases, etc.) are designing new approaches or adapting some of the traditional algorithms to data streams. The number of researchers in this field also is growing considerably, and in many conferences data streams are becoming a consolidated topic (ICML, KDD, IJCAI, ICDM, SAC, ECML, etc).

The goal of the track is to convene researchers who deal with decision rules, decision trees, association rules, clustering, filtering, pre-processing, post-processing, feature selection, visualization techniques, etc. from data streams.

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TOPICS

- Association Rules from Data Streams

- Concept drift / Change Detection from Data Streams

- Clustering from Data Streams

- Data Stream Models

- Decision Rules from Data Streams

- Decision Trees from Data Streams

- Feature Selection from Data Streams

- Real-World Applications involving incremental, on-line, or real-time learning

- Visualization Techniques from Data Streams

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PAPERS

All papers must represent original and unpublished work and can only be submitted to one ACM SAC track. Accepted papers will be published in the yearly ACM SAC printed proceedings and in the ACM's Digital Library. The ACM SAC is a refereed conference. This means each paper will be blindly reviewed by at least three referees. Papers will be evaluated according to their significance, originality, technical content, style and clarity. The acceptance rate was around 30% in the previous editions of this track.

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SUBMISSION GUIDELINES

- The final paper should be five pages long using the two-column ACM SIG template (around 5,000 words). Authors have the option to add up to three more pages to the paper at additional expense. The camera-ready paper can not exceed eight pages.

- To facilitate the blind review process, the authors names and contacts must not appear anywhere in the paper, self-reference should be in the third person, attribution to the author(s) must be in the form of "author", and bibliographical entries by the author(s) must also be in the form of "author". In this way, authors must submit two files:

- A one-page cover sheet that lists the title of the paper, the name(s) and affiliation(s) of the author(s), and the address (including e-mail address and fax number) to which correspondence should be sent.

- The paper itself in PDF format, with authors and affiliations omitted.

Do not compress files in any way.

- The paper must be formatted using the two-column ACM SIG format (tighter alternate style). Document templates are available at http://www.acm.org/sigs/pubs/proceed/template.html

- Quality papers that are not accepted due to space limitations will be invited for the poster session. These will be included in the printed proceedings and ACM's Digital Library as short papers.

- Please submit papers electronically by e-mail to the track chair Joao Gama [jgama@liacc.up.pt] with subject "DS-SAC07"

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TRACK CHAIRS

Joao Gama LIACC, University of Porto, Portugal http://www.niaad.liacc.up.pt/~jgama/

Jesús S. Aguilar-Ruiz School of Engineering, Pablo de Olavide University, Spain http://www.upo.es/eps/aguilar/

Ralf Klinkenberg Artificial Intelligence Unit, University of Dortmund, Germany http://www-ai.cs.uni-dortmund.de/PERSONAL/klinkenberg.html

----------------------------------------------------------- PROGRAM COMMITTEE

Pedro Domingos, University of Washington, USA Jeffrey S. Vitter, Purdue University, US Philip S. Yu, IBM Watson Research Center, US Rajeev Rastogi, Bell Labs, Lucent, US Minos Garofalakis, Bell Labs, US Hillol Kargupta, University of Maryland, Baltimore County, US Daniel Barbará, George Mason University, ISE Dept., US Jiong Yang, University of Illinois at Urbana Champaign, US Jian Pei, State University of New York at Buffalo, US Wei Wang, University of North Carolina, Chapel Hill, US S. Muthukrishnan, Google, US X. Sean Wang, University of Vermont, US Min Wang, IBM Watson Research Center, US Venkatesh Ganti, Microsoft Research, US Nick Koudas, AT&T Research, US Dimitrios Gunopulos, University of California, Riverside, US. Rosa Meo, University of Torino, Italy Mark Last, Ben-Gurion University, Israel Bernhard Seeger, University Marburg, German Francisco Ferrer, University of Seville, Spain Pedro Rodrigues, University of Porto, Portugal Josep Roure i Alcobe, Polytechnic of Catalunya, Spain Joao Gama, University of Porto, Portugal Jesus S. Aguilar-Ruiz, University of Seville, Spain Ralf Klinkenberg, University of Dortmund, Germany

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