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ABSTRACT
In new application areas of relational database systems, such as artificial intelligence, the join operator is used more extensively than in conventional applications. In this paper, we propose a simple data structure, called a join index, for improving the performance of joins in the context of complex queries. For most of the joins, updates to join indices incur very little overhead. Some properties of a join index are (i) its efficient use of memory and adaptiveness to parallel execution, (ii) its compatibility with other operations (including select and union), (iii) its support for abstract data type join predicates, (iv) its support for multirelation clustering, and (v) its use in representing directed graphs and in evaluating recursive queries. Finally, the analysis of the join algorithm using join indices shows its excellent performance.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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CITED BY 120
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REVIEW
"Robert J. Tufts : Reviewer"
Since its inception, relational database technology has been plagued with
operational efficiency problems for large amounts of data and for complex
queries. Much of the operational inefficiency can be attributed to the
so-called 80-20 rule (80 p
more...
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