Learning transformation rules for semantic query optimization: a data-driven approach

Lecture Notes in Computer Science

2000

Abstract. In 'Associative Caching' each client computer keeps a copy of its query result sets in its own local database. The purpose is to reduce the size and frequency of queries to the remote data server accessed by wide-area network. For each new query the client tries to find some or all of the required data in its local collection of result sets. This is done by syntactic comparison of the new query with each previous query, to detect overlapping data sets. Attribute-Pair Range Rules are subset descriptors which the server derives from its data, for its own use in query optimisation. These subset descriptors can be further utilized to provide descriptors for each query result set. This new information enables clients to exploit their cached data for syntactically unrelated queries.

Section: Discussionmentioning

confidence: 99%

Extending the Re-use of Query Results at Remote Client Sites

Lecture Notes in Computer Science

2000

“…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. Shekhar [ 5 ] used a classification grid to examine the distribution of data values and derive rules with a wide range of structures, whose applicability to a wide range of queries, robustness against data changes, ease of maintenance, and access speed is poor compared with attribute-pair rules.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

Data analysis for query processing

Advances in Intelligent Data Analysis Reasoning About Data

1997

Abstract. Data analysis is needed in connection with query processing, to produce data summary information in the form of rules or assertions that allow semantic query optimisation or direct query answering without consulting the data itself. The goal of an intelligent analyser in this context is to produce robust rules, stable in the presence of data changes, which allow easy rule maintenance as data changes, and provide rapid query reformulation, refutation or answering. It must also limit the rule set to rules useful for query processing.

“…Let us assume that n 11 , n 22 and n 33 have values 3, 4 and 2 respectively and all other cells contain a zero. The traditional way of calculating Chi-square would be:…”

Section: Algorithm Design and Memory Limitationmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

A statistical approach to rule selection in semantic query optimisation

Lecture Notes in Computer Science

1999

Abstract. Semantic Query Optimisation makes use of the semantic knowledge of a database (rules) to perform query transformation. Rules are normally learned from former queries fired by the user. Over time, however, this can result in the rule set becoming very large thereby degrading the efficiency of the system as a whole. Such a problem is known as the utility problem. This paper seeks to provide a solution to the utility problem through the use of statistical techniques in selecting and maintaining an optimal rule set. Statistical methods have, in fact, been used widely in the field of Knowledge Discovery to identify and measure relationships between attributes. Here we extend the approach to Semantic Query Optimisation using the Chi-square statistical method which is integrated into a prototype query optimiser developed by the authors. We also present a new technique for calculating Chi-square, which is faster and more efficient than the traditional method in this situation.