AQUAGP: Approximate QUery Answers Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{eurogp06:PeltzerTeredesauReinard,
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author = "Jason B. Peltzer and Ankur M. Teredesai and
Garrett Reinard",
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title = "{AQUAGP:} Approximate QUery Answers Using Genetic
Programming",
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editor = "Pierre Collet and Marco Tomassini and Marc Ebner and
Steven Gustafson and Anik\'o Ek\'art",
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booktitle = "Proceedings of the 9th European Conference on Genetic
Programming",
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publisher = "Springer",
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series = "Lecture Notes in Computer Science",
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volume = "3905",
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year = "2006",
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address = "Budapest, Hungary",
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month = "10 - 12 " # apr,
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organisation = "EvoNet",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "3-540-33143-3",
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pages = "49--60",
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DOI = "doi:10.1007/11729976_5",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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abstract = "Speed, cost, and accuracy are crucial performance
parameters while evaluating the quality of information
and query retrieval within any Database Management
System. For some queries it may be possible to derive a
similar result set using an approximate query answering
algorithm or tool when the \textit{perfect/exact}
results are not required. Query approximation becomes
useful when the following conditions are true: (a) a
high percentage of the relevant data is retrieved
correctly, (b) irrelevant or extra data is minimised,
and (c) an approximate answer (if available) results in
significant (notable) savings in terms of the overall
query cost and retrieval time. In this paper we discuss
a novel approach for approximate query answering using
Genetic Programming (GP) paradigms. We have developed
an evolutionary computing based query space exploration
framework which, given an input query and the database
schema, uses tree-based GP to generate and evaluate
approximate query candidates, automatically. We
highlight and discuss various avenues of exploration
and evaluate the success of our experiments based on
the speed, cost, and accuracy of the results retrieved
by the re-formulated (GP generated) queries and present
the results on a variety of query types for
TPC-benchmark and PKDD-benchmark datasets.",
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notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in
conjunction with EvoCOP2006 and EvoWorkshops2006",
- }
Genetic Programming entries for
Jason B Peltzer
Ankur M Teredesai
Garrett Reinard
Citations