國立虎尾科技大學 |

Information Quality in Information Fusion and Decision Making

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Information Quality in Information Fusion and Decision Making/ edited by Éloi Bossé, Galina L. Rogova.
other author:	Bossé, Éloi.
Description:	XVI, 620 p. 167 illus., 127 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Data mining. -
Online resource:	https://doi.org/10.1007/978-3-030-03643-0
ISBN:	9783030036430

Information Quality in Information Fusion and Decision Making
Information Quality in Information Fusion and Decision Making[electronic resource] /edited by Éloi Bossé, Galina L. Rogova. - 1st ed. 2019. - XVI, 620 p. 167 illus., 127 illus. in color.online resource. - Information Fusion and Data Science,2510-1528. - Information Fusion and Data Science,.

PartI: Information Quality: Concepts, Models and Dimensions -- Chapter1: Information Quality in Fusion Driven Human-Machine Environments -- Chapter2: Quality of Information Sources in Information Fusion -- Chapter3: Using Quality Measures in the Intelligent Fusion of Probabilistic Information -- Chapter4: Conﬂict management in information fusion with belief functions -- Chapter5: Requirements for total uncertainty measures in the theory of evidence.-Chapter6: Uncertainty Characterization and Fusion of Information from Unreliable Sources -- Chapter7: Assessing the usefulness of information in the context of coalition operations -- Chapter8: Fact, Conjecture, Hearsay and Lies: Issues of Uncertainty in Natural Language Communications -- Chapter9: Fake or Fact? Theoretical and Practical Aspects of Fake News -- Chapter10: Information quality and social networks -- Chapter11: Quality, Context, and Information Fusion -- Chapter12: Analyzing Uncertain Tabular Data. Chapter13: Evaluation of information in the context of decision-making -- Chapter14: Evaluating and Improving Data Fusion Accuracy -- PartII: Aspects of Information Quality in various domains of application -- Chapter15: Decision-Aid Methods based on Belief Function Theory with Application to Torrent Protection -- Chapter16: An Epistemological Model for a Data Analysis Process in Support of Verification and Validation -- Chapter17: Data and Information Quality in Remote Sensing -- Chapter18: Reliability-Aware and Robust Multi-Sensor Fusion Towards Ego-Lane Estimation Using Artiﬁcial Neural Networks -- Chapter19: Analytics and Quality in Medical Encoding Systems -- Chapter20: Information Quality: The Nexus of Actionable Intelligence -- Chapter21: Ranking Algorithms: Application for Patent Citation Network -- Chapter22: Conﬂict Measures and Importance Weighting for Information Fusion applied to Industry 4.0 -- Chapter23: Quantify: An Information Fusion Model based on Syntactic and Semantic Analysis and Quality Assessments to Enhance Situation Awareness -- Chapter24: Adaptive fusion.

This book presents a contemporary view of the role of information quality in information fusion and decision making, and provides a formal foundation and the implementation strategies required for dealing with insufficient information quality in building fusion systems for decision making. Information fusion is the process of gathering, processing, and combining large amounts of information from multiple and diverse sources, including physical sensors to human intelligence reports and social media. That data and information may be unreliable, of low fidelity, insufficient resolution, contradictory, fake and/or redundant. Sources may provide unverified reports obtained from other sources resulting in correlations and biases. The success of the fusion processing depends on how well knowledge produced by the processing chain represents reality, which in turn depends on how adequate data are, how good and adequate are the models used, and how accurate, appropriate or applicable prior and contextual knowledge is. By offering contributions by leading experts, this book provides an unparalleled understanding of the problem of information quality in information fusion and decision-making for researchers and professionals in the field.

ISBN: 9783030036430

Standard No.: 10.1007/978-3-030-03643-0doiSubjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Information Quality in Information Fusion and Decision Making
LDR:04743nam a22004095i 4500 001 1013429
003 DE-He213
005 20200704183739.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783030036430 $9 978-3-030-03643-0
024 7 $a 10.1007/978-3-030-03643-0 $2 doi
035 $a 978-3-030-03643-0
050 4 $a QA76.9.D343
072 7 $a UNF $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 006.312 $2 23
245 1 0 $a Information Quality in Information Fusion and Decision Making $h [electronic resource] / $c edited by Éloi Bossé, Galina L. Rogova.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a XVI, 620 p. 167 illus., 127 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Information Fusion and Data Science, $x 2510-1528
505 0 $a PartI: Information Quality: Concepts, Models and Dimensions -- Chapter1: Information Quality in Fusion Driven Human-Machine Environments -- Chapter2: Quality of Information Sources in Information Fusion -- Chapter3: Using Quality Measures in the Intelligent Fusion of Probabilistic Information -- Chapter4: Conﬂict management in information fusion with belief functions -- Chapter5: Requirements for total uncertainty measures in the theory of evidence.-Chapter6: Uncertainty Characterization and Fusion of Information from Unreliable Sources -- Chapter7: Assessing the usefulness of information in the context of coalition operations -- Chapter8: Fact, Conjecture, Hearsay and Lies: Issues of Uncertainty in Natural Language Communications -- Chapter9: Fake or Fact? Theoretical and Practical Aspects of Fake News -- Chapter10: Information quality and social networks -- Chapter11: Quality, Context, and Information Fusion -- Chapter12: Analyzing Uncertain Tabular Data. Chapter13: Evaluation of information in the context of decision-making -- Chapter14: Evaluating and Improving Data Fusion Accuracy -- PartII: Aspects of Information Quality in various domains of application -- Chapter15: Decision-Aid Methods based on Belief Function Theory with Application to Torrent Protection -- Chapter16: An Epistemological Model for a Data Analysis Process in Support of Verification and Validation -- Chapter17: Data and Information Quality in Remote Sensing -- Chapter18: Reliability-Aware and Robust Multi-Sensor Fusion Towards Ego-Lane Estimation Using Artiﬁcial Neural Networks -- Chapter19: Analytics and Quality in Medical Encoding Systems -- Chapter20: Information Quality: The Nexus of Actionable Intelligence -- Chapter21: Ranking Algorithms: Application for Patent Citation Network -- Chapter22: Conﬂict Measures and Importance Weighting for Information Fusion applied to Industry 4.0 -- Chapter23: Quantify: An Information Fusion Model based on Syntactic and Semantic Analysis and Quality Assessments to Enhance Situation Awareness -- Chapter24: Adaptive fusion.
520 $a This book presents a contemporary view of the role of information quality in information fusion and decision making, and provides a formal foundation and the implementation strategies required for dealing with insufficient information quality in building fusion systems for decision making. Information fusion is the process of gathering, processing, and combining large amounts of information from multiple and diverse sources, including physical sensors to human intelligence reports and social media. That data and information may be unreliable, of low fidelity, insufficient resolution, contradictory, fake and/or redundant. Sources may provide unverified reports obtained from other sources resulting in correlations and biases. The success of the fusion processing depends on how well knowledge produced by the processing chain represents reality, which in turn depends on how adequate data are, how good and adequate are the models used, and how accurate, appropriate or applicable prior and contextual knowledge is. By offering contributions by leading experts, this book provides an unparalleled understanding of the problem of information quality in information fusion and decision-making for researchers and professionals in the field.
650 0 $a Data mining. $3 528622
650 0 $a Big data. $3 981821
650 0 $a Artificial intelligence. $3 559380
650 0 $a Operations research. $3 573517
650 0 $a Decision making. $3 528319
650 0 $a Computational intelligence. $3 568984
650 0 $a Sociophysics. $3 890761
650 0 $a Econophysics. $3 796705
650 1 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Big Data/Analytics. $3 1106909
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Operations Research/Decision Theory. $3 669176
650 2 4 $a Computational Intelligence. $3 768837
650 2 4 $a Data-driven Science, Modeling and Theory Building. $3 1112983
700 1 $a Bossé, Éloi. $e author. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1300733
700 1 $a Rogova, Galina L. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1307699
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030036423
776 0 8 $i Printed edition: $z 9783030036447
830 0 $a Information Fusion and Data Science, $x 2510-1528 $3 1280373
856 4 0 $u https://doi.org/10.1007/978-3-030-03643-0
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)