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Summary of the first ACM SIGKDD workshop on knowledge discovery from uncertain data (U'09)

Summary of the first ACM SIGKDD workshop on knowledge discovery from uncertain data (U'09) Summary of the First ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data (U ™09) Simon Fraser University, Canada Jian Pei University of Maryland College Park, USA Lise Getoor University of Twente, Netherlands Ander de Keijzer jpei@cs.sfu.ca getoor@cs.umd.edu a.dekeijzer@utwente.nl The importance of uncertain data is growing quickly in many essential applications such as environmental monitoring, mobile object tracking and data integration. Recently, storing, collecting, processing, and analyzing uncertain data has attracted increasing attention from both academia and industry. Analyzing and mining uncertain data needs collaboration and joint e €ort from multiple research communities. Based on this motivation, we ran the First ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data (U ™09) in conjunction with the 2009 SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ™09) at Paris. The focus of this workshop was to bring together and bridge research in reasoning under uncertainty, probabilistic databases and mining uncertain data. Work in statistics and probabilistic reasoning can provide support with models for representing uncertainty, work in the probabilistic database community can provide methods for storing and managing uncertain data, while work in the mining uncertain data can de ne data analysis tasks and methods. It is http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGKDD Explorations Newsletter Association for Computing Machinery

Summary of the first ACM SIGKDD workshop on knowledge discovery from uncertain data (U'09)

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Publisher
Association for Computing Machinery
Copyright
The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc.
ISSN
1931-0145
DOI
10.1145/1809400.1809419
Publisher site
See Article on Publisher Site

Abstract

Summary of the First ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data (U ™09) Simon Fraser University, Canada Jian Pei University of Maryland College Park, USA Lise Getoor University of Twente, Netherlands Ander de Keijzer jpei@cs.sfu.ca getoor@cs.umd.edu a.dekeijzer@utwente.nl The importance of uncertain data is growing quickly in many essential applications such as environmental monitoring, mobile object tracking and data integration. Recently, storing, collecting, processing, and analyzing uncertain data has attracted increasing attention from both academia and industry. Analyzing and mining uncertain data needs collaboration and joint e €ort from multiple research communities. Based on this motivation, we ran the First ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data (U ™09) in conjunction with the 2009 SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ™09) at Paris. The focus of this workshop was to bring together and bridge research in reasoning under uncertainty, probabilistic databases and mining uncertain data. Work in statistics and probabilistic reasoning can provide support with models for representing uncertainty, work in the probabilistic database community can provide methods for storing and managing uncertain data, while work in the mining uncertain data can de ne data analysis tasks and methods. It is

Journal

ACM SIGKDD Explorations Newsletter – Association for Computing Machinery

Published: May 27, 2010

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