Rough Set Based Computation Times Estimation on Knowledge Grid

• Published in: Advances in Grid Computing - EGC 2005 pp. 557 - 566
• Main Authors: Gao, Kun, Ji, Youquan, Liu, Meiqun, Chen, Jiaxun
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Data Mining Task; Data Mining Tool; Data processing. List processing. Character string processing; Decision Attribute; Exact sciences and technology; Execution Plan; Grid Environment; Memory organisation. Data processing; Software; Knowledge engineering; High performance; Data analysis; Formal method; Grid; Web service; Metadata; Information extraction; Scheduling; Distributed system; Data mining; Distributed computing; Interval analysis; Rough set theory; Heuristic method
• ISBN: 3540269185; 9783540269182
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11508380_57

Efficient estimating the application computation times of data mining is a key component of successful scheduling on Knowledge Grid. In this paper, we present a holistic approach to estimation that uses rough sets theory to determine a reduct and then compute a runtime estimate. The heuristic reduct algorithm is based on frequencies of attributes appeared in discernibility matrix. We also present to add dynamic information about the performances of various data mining tools over specific data sources to the Knowledge Grid service for supporting the estimation. This information can be added as additional metadata stored in Knowledge Metadata Repository of Grid. Experimental result validates our solution that rough sets provide a formal framework for the problem of application run times estimation in Grid environment.