Equi-depth multidimensional histograms
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Statistical profile estimation in database systems
ACM Computing Surveys (CSUR)
Learning automata: an introduction
Learning automata: an introduction
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
How computers play chess
On the propagation of errors in the size of join results
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Histogram-based estimation techniques in database systems
Histogram-based estimation techniques in database systems
Artificial intelligence: a new synthesis
Artificial intelligence: a new synthesis
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Tabu Search
Interactive Linear Algebra with Maple V
Interactive Linear Algebra with Maple V
Artificial Intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Accurate estimation of the number of tuples satisfying a condition
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Graph Partitioning Using Learning Automata
IEEE Transactions on Computers
Query Result Size Estimation Using the Trapezoidal Attribute Cardinality Map
IDEAS '00 Proceedings of the 2000 International Symposium on Database Engineering & Applications
IDEAS '99 Proceedings of the 1999 International Symposium on Database Engineering & Applications
The optimization of queries in relational databases
The optimization of queries in relational databases
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hill-Climbing Approach for Automatic Gridding of cDNA Microarray Images
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
The averaged mappings problem: statement, applications, and approximate solution
Proceedings of the 44th annual Southeast regional conference
Goal-oriented optimal subset selection of correlated multimedia streams
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Association-based dynamic computation of reputation in web services
International Journal of Web and Grid Services
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Many optimization problems in computer science have been proven to be NP-hard, and it is unlikely that polynomial-time algorithms that solve these problems exist unless P = NP. Alternatively, they are solved using heuristics algorithms, which provide a sub-optimal solution that, hopefully, is arbitrarily close to the optimal. Such problems are found in a wide range of applications, including artificial intelligence, game theory, graph partitioning, database query optimization, etc. Consider a heuristic algorithm, A. Suppose that A could invoke one of two possible heuristic functions. The question of determining which heuristic function is superior, has typically demanded a yes/no answer--one which is often substantiated by empirical evidence. In this paper, by using Pattern Classification Techniques (PCT), we propose a formal, rigorous theoretical model that provides a stochastic answer to this problem. We prove that given a heuristic algorithm, A, that could utilize either of two heuristic functions H1 or H2 used to find the solution to a particular problem, if the accuracy of evaluating the cost of the optimal solution by using H1 is greater than the accuracy of evaluating the cost using H2, then H1 has a higher probability than H2 of leading to the optimal solution. This unproven conjecture has been the basis for designing numerous algorithms such as the A* algorithm, and its variants. Apart from formally proving the result, we also address the corresponding database query optimization problem that has been open for at least two decades. To validate our proofs, we report empirical results on database query optimization techniques involving a few well-known histogram estimation methods.