High Performance Data Mining and Applications Overview
International workshop on High Performance Data Mining and Applications (HPDMA 2007) was held in conjunction with The 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2007), Nanjing, China, May 2007. The workshop aimed at sharing
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Department of Computer Science, Georgia State University, Atlanta, USA [email protected] 2 Department of Computer Science, Southeast University, Nanjing, China [email protected]
Abstract. International workshop on High Performance Data Mining and Applications (HPDMA 2007) was held in conjunction with The 11th PacificAsia Conference on Knowledge Discovery and Data Mining (PAKDD 2007), Nanjing, China, May 2007. The workshop aimed at sharing and comparing experiences on high performance data mining methods and applications from both algorithmic and system perspectives. In summary, the workshop gave a discussion forum for researchers working on both data mining and high performance computing where the attendees discussed various aspects on high performance data mining.
1 Introduction Over the years data is being collected and stored at an unprecedented rate in almost all fields of human endeavor from scientific research to economic activities. To achieve efficient mining of useful information from the data available, parallel hardware platforms, clusters and large-scale distributed computing infrastructures, such as computational grid and peer-to-peer systems, are widely used by data mining communities. This also poses challenges on the design of parallel and distributed algorithms for data mining. This workshop focused on high performance data mining methods and applications from both algorithmic and system perspectives. The workshop brought together researchers who are interested in both of the areas of data mining and high performance computing, where the attendees discussed various aspects on high performance data mining The topics of the workshop in call for papers included: • • • • •
Parallel or distributed mining Cluster-based data mining algorithms and systems Grid-based data mining algorithms and systems Peer-to-Peer based data mining algorithms and systems Data mining algorithms and systems based on parallel hardware platforms, including shared-memory systems (SMPs), distributed-memory systems, etc. • Resource and location aware data mining algorithms and systems T. Washio et al. (Eds.): PAKDD 2007 Workshops, LNAI 4819, pp. 229–230, 2007. © Springer-Verlag Berlin Heidelberg 2007
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C. Xie and J. He
Data mining in mobile and ad hoc environments Data mining in sensor networks Data mining in distributed security and privacy High performance stream data mining and management Integration of mining with databases and data warehousing Applications of parallel and distributed data mining in business, science, engineering, medicine, and other disciplines
2 Workshop Overview All submitted papers were carefully peer reviewed by program committee members. We accepted 29 papers (25 regular papers and 4 posters) out of 119 submissions. The acceptance rate is approximately 25%. We would like to thank all the authors who submitted papers to the workshop and participated in the interesting discussions at the workshop. We would also like to thank the all active program committee members for their efforts
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