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Extracting predicates from mining models for efficient query evaluation

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Title Extracting predicates from mining models for efficient query evaluation
 
Creator CHAUDHURI, S
NARASAYYA, V
SARAWAGI, S
 
Subject performance
algorithms
complex predicate optimization
simpler rules from complex predictive functions
 
Description Modern relational database systems are beginning to support ad hoc queries on mining models. In this article, we explore novel techniques for optimizing queries that contain predicates on the results of application of mining models to relational data. For such queries, we use the internal structure of the mining model to automatically derive traditional database predicates. We present algorithms for deriving such predicates for a large class of popular discrete mining models: decision trees, naive Bayes, clustering and linear support vector machines. Our experiments on Microsoft SQL Server demonstrate that these derived predicates can significantly reduce the cost of evaluating such queries.
 
Publisher ASSOC COMPUTING MACHINERY
 
Date 2011-07-18T20:34:57Z
2011-12-26T12:50:49Z
2011-12-27T05:36:56Z
2011-07-18T20:34:57Z
2011-12-26T12:50:49Z
2011-12-27T05:36:56Z
2004
 
Type Article
 
Identifier ACM TRANSACTIONS ON DATABASE SYSTEMS, 29(3), 508-544
0362-5915
http://dx.doi.org/10.1145/1016028.1016031
http://dspace.library.iitb.ac.in/xmlui/handle/10054/5057
http://hdl.handle.net/10054/5057
 
Language en