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Ranking queries are widely used in data exploration, data analysis and decision making scenarios. While most of the currently proposed ranking techniques focus on deterministic data, several emerging applications involve data that are imprecise or uncertain. Ranking uncertain data raises new challenges in query semantics and processing, making conventional methods inapplicable. Furthermore, the interplay between ranking and uncertainty models introduces new dimensions for ordering query results that do not exist in the traditional settings. This lecture describes new formulations and processing techniques for ranking queries on uncertain data. The formulations are based on marriage of traditional ranking semantics with possible worlds semantics under widely-adopted uncertainty models. In particular, we focus on discussing the impact of tuple-level and attribute-level uncertainty on the semantics and processing techniques of ranking queries. Under the tuple-level uncertainty model, we describe new processing techniques leveraging the capabilities of relational database systems to recognize and handle data uncertainty in score-based ranking. Under the attribute-level uncertainty model, we describe new probabilistic ranking models and a set of query evaluation algorithms, including sampling-based techniques. We also discuss supporting rank join queries on uncertain data, and we show how to extend current rank join methods to handle uncertainty in scoring attributes.
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Probabilistic ranking techniques in relational databases
2011, Morgan & Claypool, Morgan & Claypool Publishers
electronic resource /
in English
1608455688 9781608455683
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San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA)
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Edition Notes
Part of: Synthesis digital library of engineering and computer science.
Series from website.
Includes bibliographical references (p. 59-62).
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
Also available in print.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
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- Created July 30, 2014
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July 29, 2023 | Edited by ImportBot | import existing book |
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