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Learning Transformation Rules for Semantic Query Optimization: A Data-Driven Approach (1993)
| Venue: | IEEE Transactions on Knowledge and Data Engineering |
| Citations: | 41 - 1 self |
BibTeX
@ARTICLE{Shekhar93learningtransformation,
author = {Shashi Shekhar and Shashi Shekhar and Babak Hamidzadeh and Babak Hamidzadeh and Ashim Kohli and Ashim Kohli and Mark Coyle and Mark Coyle},
title = {Learning Transformation Rules for Semantic Query Optimization: A Data-Driven Approach},
journal = {IEEE Transactions on Knowledge and Data Engineering},
year = {1993},
volume = {5},
pages = {950--964}
}
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Abstract
Learning query transformation rules is vital for the success of semantic query optimization in domains where the user cannot provide a comprehensive set of integrity constraints. Finding these rules is a discovery task because of the lack of target. Previous approaches to learning query transformation rules have been based on analyzing past queries. We propose a new approach to learning query transformation rules based on analyzing the existing data in the database. This paper describes a framework and a closure algorithm to learning rules from a given data-distribution. We characterize the correctness, completeness and complexity of the proposed algorithm and provide a detailed example to illustrate the framework. Keywords: Rule discovery, semantic query optimization, discovery in data. Areas Addressed: Learning and Discovery in Database, Data Engineering Tools, Highlevel Query Answering, Applications in Query Optimization. Postal Address 4-192 EE/CS Bldg., 200 Union Stree...
Keyphrases
semantic query optimization data-driven approach query transformation rule transformation rule union stree detailed example closure algorithm query optimization data engineering tool past query discovery task new approach previous approach user cannot integrity constraint highlevel query answering rule discovery comprehensive set
