Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
An overview of query optimization in relational systems
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Optimization techniques for queries with expensive methods
ACM Transactions on Database Systems (TODS)
The Aqua approximate query answering system
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Query Optimization in Database Systems
ACM Computing Surveys (CSUR)
Heuristic and randomized optimization for the join ordering problem
The VLDB Journal — The International Journal on Very Large Data Bases
Structure choices for two-dimensional histogram construction
CASCON '04 Proceedings of the 2004 conference of the Centre for Advanced Studies on Collaborative research
Genetic programming in database query optimization
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Probabilistic model for accuracy estimation in approximate monodimensional analyses
WSEAS Transactions on Computers
Accuracy estimation in approximate query processing
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
Metadata for approximate query answering systems
Advances in Software Engineering
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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 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 minimized, 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.