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Probabilistic ranking techniques in relational databases 1 edition

Probabilistic ranking techniques in relational databases
Ihab F. Ilyas

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Probabilistic ranking techniques in relational databases
Ihab F. Ilyas, Mohamed A. Soliman

Published 2011 by Morgan & Claypool in San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) .
Written in English.

About the Book

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.

Table of Contents

1. Introduction
Tuple level uncertainty
Attribute level uncertainty
Challenges
State-of-the-art
2. Uncertainty models
Tuple uncertainty models
Attribute uncertainty models
Discrete uncertain scores
Continuous uncertain scores
3. Query semantics
Mode-based semantics
Aggregation-based semantics
Applications
4. Methodologies
Branch and bound search
UTop-prefix under Tuple uncertainty
UTop-prefix under attribute uncertainty
Monte-Carlo simulation
Computing UTop-rank query
Computing UTop-prefix and UTop-set queries
Dynamic programming
UTop-rank query under independence
Generating functions
Probabilistic threshold
Typical top-k answers
Other methodologies
Expected ranks
Uncertain rank aggregation
5. Uncertain rank join
Uncertain rank join problem
Computing the top-k join results
Ranking the top-k join results
Join-aware sampling
Incremental ranking
6. Conclusion
Bibliography
Authors' biographies.

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.

Series
Synthesis lectures on data management -- # 14
Other Titles
Synthesis digital library of engineering and computer science.

Classifications

Dewey Decimal Class
519.5
Library of Congress
QA278.75 .I496 2011

The Physical Object

Format
[electronic resource] /

ID Numbers

Open Library
OL25567200M
Internet Archive
probabilisticran00ilya
ISBN 13
9781608455683, 9781608455676

History Created July 30, 2014 · 1 revision Download catalog record: RDF / JSON

July 30, 2014 Created by ImportBot import new book