Data Mining Using Relational Database Management Systems

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Proceedings titleAdvances in knowledge discovery and data mining : 10th Pacific-Asia conference, PAKDD 2006, Singapore, April 9-12, 2006: proceedings
Series titleLecture Notes in Computer Science; Volume 3918
Conference10th Pacific-Asia conference, PAKDD 2006, Singapore, April 9-12, 2006
Pages657667; # of pages: 11
AbstractSoftware packages providing a whole set of data mining and machine learning algorithms are attractive because they allow experimentation with many kinds of algorithms in an easy setup. However, these packages are often based on main-memory data structures, limiting the amount of data they can handle. In this paper we use a relational database as secondary storage in order to eliminate this limitation. Unlike existing approaches, which often focus on optimizing a single algorithm to work with a database backend, we propose a general approach, which provides a database interface for several algorithms at once. We have taken a popular machine learning software package, Weka, and added a relational storage manager as back-tier to the system. The extension is transparent to the algorithms implemented in Weka, since it is hidden behind Weka’s standard main-memory data structure interface. Furthermore, some general mining tasks are transfered into the database system to speed up execution. We tested the extended system, refered to as WekaDB, and our results show that it achieves a much higher scalability than Weka, while providing the same output and maintaining good computation time.
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AffiliationNRC Canada Institute for Scientific and Technical Information; National Research Council Canada
Peer reviewedNo
NPARC number8898538
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Record identifier7045e8b3-4428-4583-aa5d-f6431c666240
Record created2009-04-22
Record modified2016-05-09
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