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|a 9783030036430
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1 |
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|a Bossé, Éloi
|e [editor]
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| 245 |
0 |
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|a Information Quality in Information Fusion and Decision Making
|h Elektronische Ressource
|c edited by Éloi Bossé, Galina L. Rogova
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| 250 |
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|a 1st ed. 2019
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2019, 2019
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| 300 |
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|a XVI, 620 p. 167 illus., 127 illus. in color
|b online resource
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| 505 |
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|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 --
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|a Chapter12: AnalyzingUncertain 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 --
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| 505 |
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|a Chapter23: Quantify: An Information Fusion Model based on Syntactic and Semantic Analysis and Quality Assessments to Enhance Situation Awareness -- Chapter24: Adaptive fusion
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| 653 |
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|a Data Analysis and Big Data
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| 653 |
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|a Complex Systems
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| 653 |
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|a Operations research
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| 653 |
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|a Computational intelligence
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Quantitative research
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| 653 |
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|a Data mining
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| 653 |
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|a Computational Intelligence
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| 653 |
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|a System theory
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Data Mining and Knowledge Discovery
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| 653 |
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|a Operations Research and Decision Theory
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| 700 |
1 |
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|a Rogova, Galina L.
|e [editor]
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
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|a Information Fusion and Data Science
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| 028 |
5 |
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|a 10.1007/978-3-030-03643-0
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-030-03643-0?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 006.312
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| 520 |
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|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
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