Knowledge Discovery from Multi-Sourced Data (Record no. 176833)

MARC details
000 -LEADER
fixed length control field 04099nam a22005775i 4500
001 - CONTROL NUMBER
control field 978-981-19-1879-7
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240423125332.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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fixed length control field 220613s2022 si | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789811918797
-- 978-981-19-1879-7
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-981-19-1879-7
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA76.9.D343
072 #7 - SUBJECT CATEGORY CODE
Subject category code UNF
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQE
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM021030
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code UNF
Source thema
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQE
Source thema
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.312
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Ye, Chen.
Relator term author.
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
245 10 - TITLE STATEMENT
Title Knowledge Discovery from Multi-Sourced Data
Medium [electronic resource] /
Statement of responsibility, etc by Chen Ye, Hongzhi Wang, Guojun Dai.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2022.
264 #1 -
-- Singapore :
-- Springer Nature Singapore :
-- Imprint: Springer,
-- 2022.
300 ## - PHYSICAL DESCRIPTION
Extent XII, 83 p. 14 illus., 9 illus. in color.
Other physical details online resource.
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-- online resource
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490 1# - SERIES STATEMENT
Series statement SpringerBriefs in Computer Science,
International Standard Serial Number 2191-5776
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 1. Introduction -- 2. Functional-dependency-based truth discovery for isomorphic data -- 3. Denial-constraint-based truth discovery for isomorphic data -- 4. Pattern discovery for heterogeneous data -- 5. Deep fact discovery for text data.
520 ## - SUMMARY, ETC.
Summary, etc This book addresses several knowledge discovery problems on multi-sourced data where the theories, techniques, and methods in data cleaning, data mining, and natural language processing are synthetically used. This book mainly focuses on three data models: the multi-sourced isomorphic data, the multi-sourced heterogeneous data, and the text data. On the basis of three data models, this book studies the knowledge discovery problems including truth discovery and fact discovery on multi-sourced data from four important properties: relevance, inconsistency, sparseness, and heterogeneity, which is useful for specialists as well as graduate students. Data, even describing the same object or event, can come from a variety of sources such as crowd workers and social media users. However, noisy pieces of data or information are unavoidable. Facing the daunting scale of data, it is unrealistic to expect humans to “label” or tell which data source is more reliable.Hence, it is crucial to identify trustworthy information from multiple noisy information sources, referring to the task of knowledge discovery. At present, the knowledge discovery research for multi-sourced data mainly faces two challenges. On the structural level, it is essential to consider the different characteristics of data composition and application scenarios and define the knowledge discovery problem on different occasions. On the algorithm level, the knowledge discovery task needs to consider different levels of information conflicts and design efficient algorithms to mine more valuable information using multiple clues. Existing knowledge discovery methods have defects on both the structural level and the algorithm level, making the knowledge discovery problem far from totally solved.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data mining.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Database management.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial intelligence
General subdivision Data processing.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data Mining and Knowledge Discovery.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Database Management.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data Science.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Wang, Hongzhi.
Relator term author.
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Dai, Guojun.
Relator term author.
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer Nature eBook
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9789811918780
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9789811918803
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title SpringerBriefs in Computer Science,
-- 2191-5776
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/978-981-19-1879-7">https://doi.org/10.1007/978-981-19-1879-7</a>
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks-CSE-Springer

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