Data Credence in IoT: Vision and Challenges

As the Internet of Things permeates every aspect of human life, assessing the credence or integrity of the data generated by "things" becomes a central exercise for making decisions or in auditing events. In this paper, we present a vision of this exercise that includes the notion of data credence, assessing data credence in an efficient manner, and the use of technologies that are on the horizon for the very large scale Internet of Things.

[1]  Zongge Liu,et al.  H-Fuse: Efficient Fusion of Aggregated Historical Data , 2017, SDM.

[2]  Vladimir Zadorozhny,et al.  Collaborative for Historical Information and Analysis: Vision and Work Plan , 2013 .

[3]  Laura M. Haas,et al.  Beauty and the Beast: The Theory and Practice of Information Integration , 2007, ICDT.

[4]  Özgür Erçetin,et al.  On Security and Reliability Using Cooperative Transmissions in Sensor Networks , 2010, Mobile Networks and Applications.

[5]  Mikhail J. Atallah,et al.  Indexing Information for Data Forensics , 2005, ACNS.

[6]  François Bry,et al.  Query Answering in Information Systems with Integrity Constraints , 1997, IICIS.

[7]  Divesh Srivastava,et al.  Truth Finding on the Deep Web: Is the Problem Solved? , 2012, Proc. VLDB Endow..

[8]  Divesh Srivastava,et al.  Less is More: Selecting Sources Wisely for Integration , 2012, Proc. VLDB Endow..

[9]  Boris Skoric,et al.  Flow-based reputation with uncertainty: evidence-based subjective logic , 2014, International Journal of Information Security.

[10]  Weiru Liu,et al.  A Syntax-based approach to measuring the degree of inconsistency for belief bases , 2011, Int. J. Approx. Reason..

[11]  Mohamed-Slim Alouini,et al.  Optimal Design of Dual-Hop VLC/RF Communication System With Energy Harvesting , 2016, IEEE Communications Letters.

[12]  Audun Jøsang,et al.  A Logic for Uncertain Probabilities , 2001, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[13]  Serge Abiteboul,et al.  Corroborating information from disagreeing views , 2010, WSDM '10.

[14]  Divesh Srivastava,et al.  Fusing data with correlations , 2014, SIGMOD Conference.

[15]  Vladimir Zadorozhny,et al.  Automatic evaluation of information provider reliability and expertise , 2013, World Wide Web.

[16]  森川 幸治,et al.  Information processing system, information processing apparatus and method , 2006 .

[17]  Jan Chomicki,et al.  Query Answering in Inconsistent Databases , 2003, Logics for Emerging Applications of Databases.

[18]  Rajeev Rastogi,et al.  A cost-based model and effective heuristic for repairing constraints by value modification , 2005, SIGMOD '05.

[19]  Kathleen M. Carley,et al.  Clearing the FOG: Fuzzy, overlapping groups for social networks , 2008, Soc. Networks.

[20]  Anthony Hunter,et al.  Approaches to Measuring Inconsistent Information , 2005, Inconsistency Tolerance.

[21]  Shahriar Mirabbasi,et al.  Wireless Energy Harvesting for Internet of Things , 2014 .

[22]  Jan Chomicki,et al.  Consistent query answers in the presence of universal constraints , 2008, Inf. Syst..

[23]  Weiru Liu,et al.  Under Consideration for Publication in Knowledge and Information Systems a General Framework for Measuring Inconsistency through Minimal Inconsistent Sets , 2022 .

[24]  Philip S. Yu,et al.  Truth Discovery with Multiple Conflicting Information Providers on the Web , 2007, IEEE Transactions on Knowledge and Data Engineering.

[25]  John Grant,et al.  Measuring Consistency Gain and Information Loss in Stepwise Inconsistency Resolution , 2011, ECSQARU.

[26]  Felix Naumann,et al.  Data Fusion – Resolving Data Conflicts for Integration , 2009 .

[27]  Leopoldo E. Bertossi,et al.  Consistent query answering in databases , 2006, SGMD.

[28]  Michael L. Brodie.  Data Integration at Scale: From Relational Data Integration to Information Ecosystems , 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.

[29]  Tomasz Imielinski,et al.  Incomplete Information in Relational Databases , 1984, JACM.

[30]  Anthony Hunter,et al.  On the measure of conflicts: Shapley Inconsistency Values , 2010, Artif. Intell..

[31]  Vladimir Zadorozhny,et al.  A systematic approach to reliability assessment in integrated databases , 2015, Journal of Intelligent Information Systems.

[32]  Maciej Ceglowski,et al.  Semantic Search of Unstructured Data using Contextual Network Graphs , 2003 .

[33]  Dan Roth,et al.  Knowing What to Believe (when you already know something) , 2010, COLING.

[34]  John Grant,et al.  Distance-Based Measures of Inconsistency , 2013, ECSQARU.

[35]  Frank Y. Li,et al.  A Novel Approach to Trust Management in Unattended Wireless Sensor Networks , 2014, IEEE Transactions on Mobile Computing.

[36]  Felix Naumann,et al.  Data fusion , 2009, CSUR.

[37]  Xiaoxin Yin,et al.  Semi-supervised truth discovery , 2011, WWW.

[38]  John Grant,et al.  Classifications for inconsistent theories , 1978, Notre Dame J. Formal Log..

[39]  Junyi Li,et al.  Visible light communication: opportunities, challenges and the path to market , 2013, IEEE Communications Magazine.

[40]  Divesh Srivastava,et al.  Integrating Conflicting Data: The Role of Source Dependence , 2009, Proc. VLDB Endow..

[41]  Gio Wiederhold,et al.  Flexible relation: an approach for integrating data from multiple, possibly inconsistent databases , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[42]  Vladimir Zadorozhny,et al.  Conflict-Aware Historical Data Fusion , 2011, SUM.

[43]  Jef Wijsen,et al.  Consistent query answering under primary keys: a characterization of tractable queries , 2009, ICDT '09.