Decomposing data mining by a process-oriented execution plan

Authors:
Yan Zhang;Honghui Li;Alexander Wöhrer;Peter Brezany;Gang Dai
Affiliations:
School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China;School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China;Institute of Scientific Computing, University of Vienna, Vienna, Austria;Institute of Scientific Computing, University of Vienna, Vienna, Austria;School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
Venue:
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Year:
2010

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Abstract

Data mining deals with the extraction of hidden knowledge from large amounts of data. Nowadays, coarse-grained data mining modules are used. This traditional black box approach focuses on specific algorithm improvements and is not flexible enough to be used for more general optimization and beneficial component reuse. The work presented in this paper elaborates on decomposing data mining tasks as data mining execution process plans which are composed of finer-grained data mining operators. The cost of an operator can be analyzed and provides means for more holistic optimizations. This process-based data mining concept is evaluated via an OGSA-DAI based implementations for association rule mining which show the feasibility of our approach as well as the re-usability of some of the data mining operators.