An adaptive mixed relation decomposition algorithm for conjunctive retrieval queries
The paper proposes an adaptive relation decomposition algorithm for conjunctive retrieval queries and provides some hard experimental data for its performance analysis. Our algorithm performs horizontal relation partitioning first, followed by vertical partitioning of every horizontal cut created in...
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| Published in | Information sciences Vol. 53; no. 1; pp. 35 - 60 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
1991
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| Online Access | Get full text |
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| Abstract | The paper proposes an adaptive relation decomposition algorithm for conjunctive retrieval queries and provides some hard experimental data for its performance analysis. Our algorithm performs horizontal relation partitioning first, followed by vertical partitioning of every horizontal cut created in the first step. Since the problem of the optimal relation decomposition is NP-hard, the proposed algorithm, as based on heuristics, is not optimal. Defining the relative savings as the ratio between the cost of answering a set of queries in the relation without decomposition and the cost of answering the same set of queries in the decomposed relation, the algorithm gives significant relative savings (250–300%) for realistic test relations and query patterns. These savings can be obtained only for an application with a query pattern that is stable over a time long enough to sense the pattern and propose a relation decomposition, to actually decompose the relation (or modify its decomposition), and to pay back for the decomposition cost. A surprising result is that the relative savings do not decrease even when the pages (blocks) on which a relation is stored are small. |
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| AbstractList | The paper proposes an adaptive relation decomposition algorithm for conjunctive retrieval queries and provides some hard experimental data for its performance analysis. The algorithm performs horizontal relation partitioning first, followed by vertical partitioning of every horizontal cut created in the first step. Since the problem of the optimal relation decomposition is NP-hard, the proposed algorithm, as based on heuristics, is not optimal. Defining the relative savings as the ratio between the cost of answering a set of queries in the relation without decomposition and the cost of answering the same set of queries in the decomposed relation, the algorithm gives significant relative savings (250-300%) for realistic test relations and query patterns. The paper proposes an adaptive relation decomposition algorithm for conjunctive retrieval queries and provides some hard experimental data for its performance analysis. Our algorithm performs horizontal relation partitioning first, followed by vertical partitioning of every horizontal cut created in the first step. Since the problem of the optimal relation decomposition is NP-hard, the proposed algorithm, as based on heuristics, is not optimal. Defining the relative savings as the ratio between the cost of answering a set of queries in the relation without decomposition and the cost of answering the same set of queries in the decomposed relation, the algorithm gives significant relative savings (250–300%) for realistic test relations and query patterns. These savings can be obtained only for an application with a query pattern that is stable over a time long enough to sense the pattern and propose a relation decomposition, to actually decompose the relation (or modify its decomposition), and to pay back for the decomposition cost. A surprising result is that the relative savings do not decrease even when the pages (blocks) on which a relation is stored are small. |
| Author | Kozaczynski, Wojtek Yu, Clement T. Lilien, Leszek |
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| Cites_doi | 10.1145/1270.1481 10.1093/biomet/53.3-4.325 10.1145/3148.3161 10.1145/319758.319760 10.1145/1994.2209 10.1145/321978.321982 10.1145/359461.359475 10.1145/319628.319650 10.1109/TSE.1980.230471 10.1145/3857.3863 10.1145/320557.320562 10.1145/3857.3861 |
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Database Systems doi: 10.1145/320557.320562 contributor: fullname: Wong – year: 1983 ident: 10.1016/0020-0255(91)90057-2_BIB15_1 article-title: A Global Approach to Record Clustering and File Reorganization contributor: fullname: Omiecinski – volume: 10 start-page: 180 issue: 2 year: 1985 ident: 10.1016/0020-0255(91)90057-2_BIB25 article-title: Adaptive record clustering publication-title: ACM Trans. Database Systems doi: 10.1145/3857.3861 contributor: fullname: Yu – year: 1981 ident: 10.1016/0020-0255(91)90057-2_BIB17 contributor: fullname: Prather |
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