AQUAGP: Approximate QUery Answers Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Jason B. Peltzer and Ankur M. Teredesai and 
                 Garrett Reinard",
  title =        "{AQUAGP:} Approximate QUery Answers Using Genetic
  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
  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,",
  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