06671cam a2200757Ii 4500 ocn909802602 OCoLC 20190328114811.0 m o d cr unu|||||||| 150521s2015 maua ob 001 0 eng d UMI eng rda pn UMI IDEBK OPELS N$T YDXCP DEBBG OCLCF DEBSZ UAB EBLCP E7B COO OCLCQ MERUC U3W D6H CEF OCLCQ INT OCLCQ CUY ZCU ICG DKC 907942398 908074254 913618826 9780128011317 0128011319 0128008679 9780128008676 9780128008676 (OCoLC)909802602 (OCoLC)907942398 (OCoLC)908074254 (OCoLC)913618826 QA76.585 COM 013000 bisacsh COM 014000 bisacsh COM 018000 bisacsh COM 067000 bisacsh COM 032000 bisacsh COM 037000 bisacsh COM 052000 bisacsh 004.7 23 Wang, Dong, author. Social sensing : building reliable systems on unreliable data / [electronic resource] Dong Wang, Tarek Abdelzaher, Lance Kaplan. Building reliable systems on unreliable data 1st ed. Waltham, MA : Morgan Kaufmann, 2015 �2015 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from title page (Safari, viewed May 8, 2015). Includes bibliographical references and index. Increasingly, 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. Front 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. 2.5 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. 4.3.2 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. 6.2 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. 7.1 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. Social media. Data mining. Big data. COMPUTERS Computer Literacy. bisacsh COMPUTERS Computer Science. bisacsh COMPUTERS Data Processing. bisacsh COMPUTERS Hardware General. bisacsh COMPUTERS Information Technology. bisacsh COMPUTERS Machine Theory. bisacsh COMPUTERS Reference. bisacsh Big data. fast (OCoLC)fst01892965 Data mining. fast (OCoLC)fst00887946 Social media. fast (OCoLC)fst01741098 Electronic books. Electronic books. lcgft Abdelzaher, Tarek, author. Kaplan, Lance, author. Print version: Wang, Dong. Social Sensing : Building Reliable Systems on Unreliable Data. Burlington : Elsevier Science, �2015 9780128008676 ScienceDirect http://www.sciencedirect.com/science/book/9780128008676 247077 247077