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003 OCoLC
005 20190328114811.0
006 m o d
007 cr unu||||||||
008 150521s2015 maua ob 001 0 eng d
040 _aUMI
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019 _a907942398
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020 _a9780128011317
020 _a0128011319
020 _a0128008679
020 _a9780128008676
020 _z9780128008676
035 _a(OCoLC)909802602
_z(OCoLC)907942398
_z(OCoLC)908074254
_z(OCoLC)913618826
050 4 _aQA76.585
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082 0 4 _a004.7
_223
100 1 _aWang, Dong,
_eauthor.
245 1 0 _aSocial sensing : building reliable systems on unreliable data /
_h[electronic resource]
_cDong Wang, Tarek Abdelzaher, Lance Kaplan.
246 3 0 _aBuilding reliable systems on unreliable data
250 _a1st ed.
264 1 _aWaltham, MA :
_bMorgan Kaufmann,
_c2015
264 4 _c�2015
300 _a1 online resource (1 volume) :
_billustrations
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
588 0 _aOnline resource; title from title page (Safari, viewed May 8, 2015).
504 _aIncludes bibliographical references and index.
520 _aIncreasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion.
505 0 _aFront Cover; Front Cover; Social Sensing: Building Reliable Systems on Unreliable Data; Copyright; Dedication; Contents; Acknowledgments; Authors; Dong Wang; Tarek Abdelzaher; Lance M. Kaplan; Foreword; Preface; Chapter 1: A new information age; 1.1 Overview; 1.2 Challenges; 1.3 State of the Art; 1.3.1 Efforts on Discount Fusion; 1.3.2 Efforts on Trust and Reputation Systems; 1.3.3 Efforts on Fact-Finding; 1.4 Organization; Chapter 2: Social Sensing Trends and Applications; 2.1 Information Sharing: The Paradigm Shift; 2.2 An Application Taxonomy; 2.3 Early Research; 2.4 The Present Time.
505 8 _a2.5 ANote on PrivacyChapter 3: Mathematical foundations of social sensing: An introductory tutorial; 3.1 AMultidisciplinary Background; 3.2 Basics of Generic Networks; 3.3 Basics of Bayesian Analysis; 3.4 Basics of Maximum Likelihood Estimation; 3.5 Basics of Expectation Maximization; 3.6 Basics of Confidence Intervals; 3.7 Putting It All Together; Chapter 4: Fact-finding in information networks; 4.1 Facts, Fact-Finders, and the Existence of Ground Truth; 4.2 Overview of Fact-Finders in Information Networks; 4.3 A Bayesian Interpretation of Basic Fact-Finding; 4.3.1 Claim Credibility.
505 8 _a4.3.2 Source Credibility4.4 The Iterative Algorithm; 4.5 Examples and Results; 4.6 Discussion; Appendix; Chapter 5: Social Sensing: A maximum likelihood estimation approach; 5.1 The Social Sensing Problem; 5.2 Expectation Maximization; 5.2.1 Background; 5.2.2 Mathematical Formulation; 5.2.3 Deriving the E-Step and M-Step; 5.3 The EM Fact-Finding Algorithm; 5.4 Examples and Results; 5.4.1 A Simulation Study; 5.4.2 A Geotagging Case Study; 5.4.3 A Real World Application; 5.5 Discussion; Chapter 6: Confidence bounds in social sensing; 6.1 The Reliability Assurance Problem.
505 8 _a6.2 Actual Cramer-Rao Lower Bound6.3 Asymptotic Cramer-Rao Lower Bound; 6.4 Confidence Interval Derivation; 6.5 Examples and Results; 6.5.1 Evaluation of Confidence Interval; 6.5.2 Evaluation of CRLB; Scalability study; Trustworthiness and assertiveness study; Robustness study; 6.5.3 Evaluation of Estimated False Positives/Negatives on Claim Classification; Scalability study; Trustworthiness and assertiveness study; Robustness study; 6.5.4 AReal World Case Study; 6.6 Discussion; Appendix; Chapter 7: Resolving conflicting observations and non-binary claims.
505 8 _a7.1 Handling Conflicting Binary Observations7.1.1 Extended Model; 7.1.2 Re-Derive the E-Step and M-Step; 7.1.3 The Binary Conflict EM Algorithm; 7.2 Handling Non-Binary Claims; 7.2.1 Generalized E and M Steps for Non-Binary Measured Variables; 7.2.2 The Generalized EM Algorithm for Non-Binary Measured Variables; 7.3 Performance Evaluation; 7.3.1 AReal World Application; 7.3.2 ASimulation Study for Conflicting Observations; 7.3.3 ASimulation Study for Non-Binary Claims; 7.4 Discussion; Appendix; Chapter 8: Understanding the social network; 8.1 Information Propagation Cascades.
650 0 _aSocial media.
650 0 _aData mining.
650 0 _aBig data.
650 7 _aCOMPUTERS
_xComputer Literacy.
_2bisacsh
650 7 _aCOMPUTERS
_xComputer Science.
_2bisacsh
650 7 _aCOMPUTERS
_xData Processing.
_2bisacsh
650 7 _aCOMPUTERS
_xHardware
_xGeneral.
_2bisacsh
650 7 _aCOMPUTERS
_xInformation Technology.
_2bisacsh
650 7 _aCOMPUTERS
_xMachine Theory.
_2bisacsh
650 7 _aCOMPUTERS
_xReference.
_2bisacsh
650 7 _aBig data.
_2fast
_0(OCoLC)fst01892965
650 7 _aData mining.
_2fast
_0(OCoLC)fst00887946
650 7 _aSocial media.
_2fast
_0(OCoLC)fst01741098
655 4 _aElectronic books.
655 7 _aElectronic books.
_2lcgft
700 1 _aAbdelzaher, Tarek,
_eauthor.
700 1 _aKaplan, Lance,
_eauthor.
776 0 8 _iPrint version:
_aWang, Dong.
_tSocial Sensing : Building Reliable Systems on Unreliable Data.
_dBurlington : Elsevier Science, �2015
_z9780128008676
856 4 0 _3ScienceDirect
_uhttp://www.sciencedirect.com/science/book/9780128008676
999 _c247077
_d247077