Probabilistic Ranking Techniques in Relational Databases (Record no. 85469)

000 -LEADER
fixed length control field 03630nam a22005055i 4500
001 - CONTROL NUMBER
control field 978-3-031-01846-6
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730164232.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220601s2011 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031018466
-- 978-3-031-01846-6
082 04 - CLASSIFICATION NUMBER
Call Number 004.6
100 1# - AUTHOR NAME
Author Ilyas, Ihab.
245 10 - TITLE STATEMENT
Title Probabilistic Ranking Techniques in Relational Databases
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2011.
300 ## - PHYSICAL DESCRIPTION
Number of Pages VIII, 71 p.
490 1# - SERIES STATEMENT
Series statement Synthesis Lectures on Data Management,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Introduction -- Uncertainty Models -- Query Semantics -- Methodologies -- Uncertain Rank Join -- Conclusion.
520 ## - SUMMARY, ETC.
Summary, etc 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: Introduction / Uncertainty Models / Query Semantics / Methodologies / Uncertain Rank Join / Conclusion.
700 1# - AUTHOR 2
Author 2 Soliman, Mohamed.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-031-01846-6
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Koha item type eBooks
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-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2011.
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-- txt
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-- computer
-- c
-- rdamedia
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-- online resource
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-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer networks .
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data structures (Computer science).
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Information theory.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer Communication Networks.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data Structures and Information Theory.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2153-5426
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