AQUAGP: Approximate QUery Answers Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{eurogp06:PeltzerTeredesauReinard,

• author = "Jason B. Peltzer and Ankur M. Teredesai and Garrett Reinard",
• title = "{AQUAGP:} Approximate QUery Answers Using Genetic Programming",
• editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art",
• booktitle = "Proceedings of the 9th European Conference on Genetic Programming",
• publisher = "Springer",
• series = "Lecture Notes in Computer Science",
• volume = "3905",
• year = "2006",
• address = "Budapest, Hungary",
• month = "10 - 12 " # apr,
• organisation = "EvoNet",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-33143-3",
• pages = "49--60",
• DOI = "doi:10.1007/11729976_5",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• 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.",
• 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

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