A method for automatic rule derivation to support semantic query optimization

Abstract

to The use of inference rules to support intelligent data processing is an increasingly important tool in many areas of computer science. In database systems, rules are used in semantic query optimization as a method for reducing query processing costs. The savings is dependent on the ability of experts to supply a set of useful rules and the ability of the optimizer to quickly find the appropriate transformations generated by these rules. Unfortunately, the most useful rules are not always those that would or could be specified by an expert. This paper describes the architecture of a system having two interrelated components: a combined conventional/semantic query optimizer, and an automatic rule deriver.Our automatic rule derivation method uses intermediate results from the optimization process to direct the search for learning new rules. Unlike a system employing only user-specified rules, a system with an automatic capability can derive rules that may be true only in the current state of the database and can modify the rule set to reflect changes in the database and its usage pattern.This system has been implemented as an extension of the EXODUS conventional query optimizer generator. We describe the implementation, and show how semantic query optimization is an extension of conventional optimization in this context.

Data analysis for query processing

“…We [ 3 ] and Hsu [ 1 ] used machine learning to generate rules that would have been useful if they had existed before the query that triggered their discovery. Siegel [ 7 ] used queries in a similar way, to suggest rules that would have been useful, but examines the data directly to test whether the rules are supported by the data. We now generalise the queryrecommended rule to a pattern for a set of rules to be obtained by data analysis, which then examines and shortlists the most probably useful subset for future queries.…”

“…Attribute-pair rules, called 'Simple Rules' in [ 7 ], are particularly suitable for query processing. The single condition structure of antecedent and consequent optimises rule usability, (all conditions in a rule must be matched).…”

“…Analysis produces summary information that can either answer a query without consulting the data itself, or else modify the query to a form the data server will be able to process more quickly. This query-modification operation using knowledge of the data is known as Semantic Query Optimisation (SQO) [1,3,5,7]. Relational data is discussed in this paper.…”

A statistical approach to rule selection in semantic query optimisation

“…Generally, rules can be supplied by the database engineer or derived automatically. Automatic rule generation methods include heuristic based systems [13], logic based systems [1], graph based systems [12] and data driven systems [7,11].…”

A Fast Method for Ensuring the Consistency of Integrity Constraints

“…Thus recent developments in query optimisation [1,9,10,11,14] make use of the semantic information inherent in integrity constraints, together with rules derived from the data itself, to transform user queries into semantically equivalent alternatives which execute in far less time. Also many systems, such as taxation and social security, now incorporate facilities for interrogating the constraint base to provide answers to hypothetical 'what if' questions, e.g.…”