Similarity Joins in Relational Database Systems (Record no. 84611)

000 -LEADER
fixed length control field 03679nam a22005055i 4500
001 - CONTROL NUMBER
control field 978-3-031-01851-0
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730163437.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220601s2014 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031018510
-- 978-3-031-01851-0
082 04 - CLASSIFICATION NUMBER
Call Number 004.6
100 1# - AUTHOR NAME
Author Augsten, Nikolaus.
245 10 - TITLE STATEMENT
Title Similarity Joins in Relational Database Systems
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2014.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XVII, 106 p.
490 1# - SERIES STATEMENT
Series statement Synthesis Lectures on Data Management,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Preface -- Acknowledgments -- Introduction -- Data Types -- Edit-Based Distances -- Token-Based Distances -- Query Processing Techniques -- Filters for Token Equality Joins -- Conclusion -- Bibliography -- Authors' Biographies -- Index.
520 ## - SUMMARY, ETC.
Summary, etc State-of-the-art database systems manage and process a variety of complex objects, including strings and trees. For such objects equality comparisons are often not meaningful and must be replaced by similarity comparisons. This book describes the concepts and techniques to incorporate similarity into database systems. We start out by discussing the properties of strings and trees, and identify the edit distance as the de facto standard for comparing complex objects. Since the edit distance is computationally expensive, token-based distances have been introduced to speed up edit distance computations. The basic idea is to decompose complex objects into sets of tokens that can be compared efficiently. Token-based distances are used to compute an approximation of the edit distance and prune expensive edit distance calculations. A key observation when computing similarity joins is that many of the object pairs, for which the similarity is computed, are very different from each other. Filters exploit this property to improve the performance of similarity joins. A filter preprocesses the input data sets and produces a set of candidate pairs. The distance function is evaluated on the candidate pairs only. We describe the essential query processing techniques for filters based on lower and upper bounds. For token equality joins we describe prefix, size, positional and partitioning filters, which can be used to avoid the computation of small intersections that are not needed since the similarity would be too low.
700 1# - AUTHOR 2
Author 2 Bohlen, Michael.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-031-01851-0
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2014.
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
347 ## -
-- 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
912 ## -
-- ZDB-2-SXSC

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