Speed, cost, and accuracy are crucial performance parameters while evaluating the quality of a query using any Database Management System (DBMS). For some queries it may be possible to approximate the answer using an approximate query answering algorithm or tool. Also, for certain queries, it may not be critical to determine the perfect/exact results so long as 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 a significant savings in terms of the overall query cost and retrieval time. In this paper we describe a novel approach for approximate query answering using the Genetic Programming (GP) paradigms. We develop an evolutionary computing based query space exploration framework. Given an input query and the database schema, our framework uses tree-based GP to automatically generate and evaluate approximate query candidates. We highlight and discuss different avenues we explored. We evaluate the success of our experiments based on the speed, the cost, and the 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.

Library of Congress Subject Headings

Database management; Database searching--Data processing; Genetic programming (Computer science)

Publication Date


Document Type


Student Type


Degree Name

Computer Science (MS)

Department, Program, or Center

Computer Science (GCCIS)


Ankur M. Teredesai

Advisor/Committee Member

Peter G. Anderson

Advisor/Committee Member

Rajendra K. Raj


Physical copy available from RIT's Wallace Library at QA76.9.D3 P45 2005


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