Efficient Processing of Top-k Queries in Uncertain Databases with x-Relations
This work introduces novel polynomial algorithms for processing top-k queries in uncertain databases under the generally adopted model of x-relations. An x-relation consists of a number of x-tuples, and each x-tuple randomly instantiates into one tuple from one or more alternatives. Our results sign...
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| Vydané v: | IEEE transactions on knowledge and data engineering Ročník 20; číslo 12; s. 1669 - 1682 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York, NY
IEEE
01.12.2008
IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1041-4347, 1558-2191 |
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| Abstract | This work introduces novel polynomial algorithms for processing top-k queries in uncertain databases under the generally adopted model of x-relations. An x-relation consists of a number of x-tuples, and each x-tuple randomly instantiates into one tuple from one or more alternatives. Our results significantly improve the best known algorithms for top-k query processing in uncertain databases, in terms of both runtime and memory usage. In the single-alternative case, the new algorithms are 2 to 3 orders of magnitude faster than the previous algorithms. In the multialternative case, we introduce the first-known polynomial algorithms, while the current best algorithms have exponential complexity in both time and space. Our algorithms run in near linear or low polynomial time and cover both types of top-k queries in uncertain databases. We provide both the theoretical analysis and an extensive experimental evaluation to demonstrate the superiority of the new approaches over existing solutions. |
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| AbstractList | This work introduces novel polynomial algorithms for processing top-k queries in uncertain databases under the generally adopted model of x-relations. An x-relation consists of a number of x-tuples, and each x-tuple randomly instantiates into one tuple from one or more alternatives. Our results significantly improve the best known algorithms for top-k query processing in uncertain databases, in terms of both runtime and memory usage. In the single-alternative case, the new algorithms are 2 to 3 orders of magnitude faster than the previous algorithms. In the multialternative case, we introduce the first-known polynomial algorithms, while the current best algorithms have exponential complexity in both time and space. Our algorithms run in near linear or low polynomial time and cover both types of top-k queries in uncertain databases. We provide both the theoretical analysis and an extensive experimental evaluation to demonstrate the superiority of the new approaches over existing solutions. |
| Author | Ke Yi Srivastava, D. Kollios, G. Feifei Li |
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| Keywords | Top-k Query Analysis of Algorithms and Problem Complexity Uncertain Database Database design Query design and implementation languages x-Relation Model Database Management Theory of Computation Information Technology and Systems modeling and management Probabilistic approach Database query Hierarchical classification top-k Relational database Linear time probabilistic data Polynomial method Modeling Algorithm Polynomial time Uncertain system Query processing Algorithm complexity Database x-relation uncertain database Time complexity |
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| SubjectTerms | Algorithmics. Computability. Computer arithmetics Algorithms Analysis of Algorithms and Problem Complexity Applied sciences Cleaning Complexity Computer science; control theory; systems Data models Data processing. List processing. Character string processing Database design Database Management Exact sciences and technology Information systems. Data bases Information Technology and Systems Mathematical models Memory organisation. Data processing modeling and management Polynomials Power system modeling Probability distribution Queries Query design and implementation languages Query processing Runtime Software Theoretical computing Theory of Computation Top-k Query Uncertain Database Uncertainty Web server x-Relation Model |
| Title | Efficient Processing of Top-k Queries in Uncertain Databases with x-Relations |
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