Information Quality in Fusion-Driven Human-Machine Environments

  • Galina L. RogovaEmail author
Part of the Information Fusion and Data Science book series (IFDS)


Effective decision making in complex dynamic situations calls for designing a fusion-based human-machine information system requiring gathering and fusing a large amount of heterogeneous multimedia and multispectral information of variable quality coming from geographically distributed sources. Successful collection and processing of such information strongly depend on the success of being aware of, and compensating for, insufficient information quality at each step of information exchange. Designing methods of representing and incorporating information quality into fusion processing is a relatively new and rather difficult problem. The chapter discusses major challenges and suggests some approaches to address this problem.


Information fusion Quality ontology Meta-data Subjective quality Quality control Higher level quality 


  1. 1.
    L. Wald, Data Fusion: Definitions and Architectures: Fusion of Images of Different Spatial Resolution (Les Presses, Ecole des Mines de Paris, Paris, 2002)Google Scholar
  2. 2.
    E. Benoit, M-Ph. Huget, M. Patrice, and P. Olivier, Reconfiguration of a distributed information fusion system, Workshop on Dependable Control of Discrete Systems, Bari: Italie, HAL CCSD, Sci. (2009)Google Scholar
  3. 3.
    F. Castanedo, A review of data fusion techniques. Sci. World J. 2013, 704504 (2013). CrossRefGoogle Scholar
  4. 4.
    Y. Lee, L. Pipino, J. Frank, R. Wang, Journey to Data Quality (MIT Press, Cambridge, 2006)Google Scholar
  5. 5.
    M. Helfert, Managing and measuring data quality in data warehousing, in Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics, pp. 55–65, 2001Google Scholar
  6. 6.
    S.E. Madnick, Y.W. Lee, R.Y. Wang, H. Zhu, Overview and framework for data and information quality research. ACM J. Data Inf. Qual. 1(1), 2 (2009)Google Scholar
  7. 7.
    F. White, A model for data fusion, in Proceedings of the 1st National Symposium on Sensor Fusion, 1988Google Scholar
  8. 8.
    E.P. Blasch, S. Plano, Level 5: user refinement to aid the fusion process, in Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications, ed. by B. Dasarathy, Proceedings of the SPIE, vol. 5099 (2003)Google Scholar
  9. 9.
    A.N. Steinberg, C.L. Bowman, Rethinking the JDL data fusion model. In: Proceedings of the MSS National Symposium on Sensor and Data Fusion, vol. 1, June 2004Google Scholar
  10. 10.
    L. Llinas, C.L. Bowman, G.L. Rogova, A.N. Steinberg, E. Waltz, F. White, Revisions to the JDL Data Fusion Model II, in Proceedings of the FUSION’2004-7th Conference on Multisource Information Fusion, Stockholm, 2004Google Scholar
  11. 11.
    S. Schreiber-Ehle, W. Koch, The JDL model of data fusion applied to cyber-defense—A review paper, in IEEE Workshop on Sensor Data Fusion: Trends Solutions Applications (SDF) (2012), pp. 116–119Google Scholar
  12. 12.
    E. Blasch, A. Steinberg, S. Das, L. Llinas, C. Chong, O. Kessler, F. White, Revisiting the JDL model for information exploitation, in Proceedings of the 16th International Conference on Information Fusion, pp 129–136, 2013Google Scholar
  13. 13.
    B. Dasarathy, Sensor fusion potential exploitation- innovative architectures and illustrative applications. IEEE Proc. 85(1), 24 (1997)CrossRefGoogle Scholar
  14. 14.
    M. Bedworth, J. O’Brien, The omnibus model: a new model of data fusion? IEEE Aerosp. Electron. Syst. Mag. 15(4), 30–36 (2000)Google Scholar
  15. 15.
    J. Boyd, A Discourse on Winning and Losing (Maxwell AFB Lecture, 1987)Google Scholar
  16. 16.
    M. Markin, C. Harris, M. Bernhardt, J. Austin, M. Bedworth, P. Greenway, R. Johnston, A. Little, D. Lowe, Technology Foresight on Data Fusion and Data Processing (The Royal Aeronautical Society, London, England 1997)Google Scholar
  17. 17.
    M. Endsley, Toward a theory of situation awareness in dynamic systems. Hum. Factors J Hum Factors Ergon Soc 37(1), 32–64 (1995)CrossRefGoogle Scholar
  18. 18.
    G. Rogova, Information quality in information fusion and decision making with applications to crisis management, in Fusion Methodology in Crisis Management: Higher Level Fusion and Decision Making, ed. by G. Rogova, P. Scott, pp. 65–86, (Springer, Cham, 2016)Google Scholar
  19. 19.
    T. Buchholz, A. Kupper, M. Schiffers, Quality of context information: what it is and why we need it, in Proceedings of the 10th International Workshop of the HP Open View University Association (HPOVUA), vol. 200, Geneva, Switzerland, 2003Google Scholar
  20. 20.
    G. Rogova, L. Snidaro, Considerations of context and quality in information fusion, in Proceedings of the 21st International Conference on Information Fusion, (IEEE, Cambridge, UK, 2018), pp. 1929–1936Google Scholar
  21. 21.
    Standard 8402, 3. I, International organization of standards, 1986Google Scholar
  22. 22.
    J.A. O'Brien, G. Marakas, Introduction to Information Systems (McGraw-Hill/Irwin, New York City, US, 2005)Google Scholar
  23. 23.
    R. Wang, D. Strong, Beyond accuracy: what data quality means to data consumers. J. Manag. Inf. Syst. 12, 5–34 (1996)CrossRefGoogle Scholar
  24. 24.
    J.B. Juran, A.B. Godfrey, Juran’s Quality Handbook, 5th edn. (McGraw-Hill, New York, 1988)Google Scholar
  25. 25.
    C. Bisdikian, L. Kaplan, M. Srivastava, D. Thornley D. Verma, R. Young, Building principles for a quality of information, specification for sensor information, in: Proceedings of the 12th International Conference on Information Fusion, Seattle, WA, USA, pp. 1370–1377, 6–9 July 2009Google Scholar
  26. 26.
    M. Bovee, R.P. Srivastava, B. Mak, A conceptual framework and belief-function approach to assessing overall information quality. Int. J. Intell. Syst. 18, 51–74 (2003)CrossRefGoogle Scholar
  27. 27.
    C.A. O’Reilly III, Variations in decision makers’ use of information source: the impact of quality and accessibility of information. Acad. Manag. J. 25(4) (1982)Google Scholar
  28. 28.
    P. Smets, Imperfect information: imprecision – uncertainty, in Uncertainty Management in Information Systems: From Needs to Solutions, ed. by A. Motro, P. Smets, (Kluwer, Boston, 1997), pp. 225–254Google Scholar
  29. 29.
    G. Rogova, E. Bosse, Information quality in information fusion, in Proceedings of the 13th International Conference on Information Fusion, Edinburg, Scotland, July 2010Google Scholar
  30. 30.
    A. Y. Tawfik, E. M. Neufeld, Irrelevance in uncertain temporal reasoning, in Proceedings of the Third International IEEE Workshop on Temporal Representation and Reasoning, pp. 196–202, 1996Google Scholar
  31. 31.
    P. Gardenfors, On the logic of relevance. Synthese 37(3), 351 (1978)MathSciNetCrossRefGoogle Scholar
  32. 32.
    M.-S. Zhong, L. Liu, R.-Z. Lu, A new method of relevance measure and its applications, in Proceedingsof the IEEE Sixth International Conference on Advanced Language Processing and Web Information Technology, (2007), pp. 595–600Google Scholar
  33. 33.
    G. Rogova, V. Nimier, Reliability in information fusion: literature survey, in Proceedings of the FUSION’2004-7th Conference on Multisource- Information Fusion, (2004), pp. 1158–1165Google Scholar
  34. 34.
    P. Bosc, H. Prade, An introduction to the fuzzy set and possibility theory-based treatment of flexible queries and uncertain or imprecise databases, in Uncertainty in Information Systems: From Needs to Solutions, ed. by A. Motro, P. Smets, (Kluwer, Boston, 1997), pp. 285–324Google Scholar
  35. 35.
    M. Smithson, Ignorance and Uncertainty: Emerging Paradigms (Springer, New York, 1989)CrossRefGoogle Scholar
  36. 36.
    E. Bossé, J. Roy, S. Wark, Concepts, models, and tools for information fusion (Artech House, Norwood, 2007)zbMATHGoogle Scholar
  37. 37.
    P. Krause, D. Clark, Representing Uncertain Knowledge: An Artificial Intelligence Approach (Kluwer Academic Publishers, Dordrecht, 1993)CrossRefGoogle Scholar
  38. 38.
    G.J. Klir, M.J. Wierman, Uncertainty-based information, in Studies in Fuzziness in Soft Computing, vol. 15, 2nd edn., (Physica-Verlag, Heidelberg, New York, 1999)Google Scholar
  39. 39.
    V. Dragas, An ontological analysis of uncertainty in soft data, in Proceedings of the 16th International Conference on Information Fusion, Istanbul, Turkey, pp. 1566–1573, 2013Google Scholar
  40. 40.
    P. Smets, Data fusion in the transferable belief model, in: Proceedings of the FUSION’2000-Third Conference on Multisource- Multisensor Information Fusion, pp. 21–33, 2002Google Scholar
  41. 41.
    G. Shafer, A Mathematical Theory of Evidence (Princeton University Press, Princeton, 1976)zbMATHGoogle Scholar
  42. 42.
    D. Dubois, H. Prade, Possibility Theory: An Approach to Computerized Processing of Uncertainty (Plenum, New York, 1988)CrossRefGoogle Scholar
  43. 43.
    R. Yager, Conditional approach to possibility-probability fusion. IEEE Trans. Fuzzy Syst. 20(1), 46–55 (2012)CrossRefGoogle Scholar
  44. 44.
    F. Delmotte, P. Smets, Target identification based on the transferable belief model interpretation of Dempster-Shafer model. IEEE Trans. Syst. Man Cybern. A 34, 457–471 (2004)CrossRefGoogle Scholar
  45. 45.
    G. Rogova, P. Scott, C. Lollett, R. Mudiyanur, Reasoning about situations in the early post-disaster response environment, in Proceedings of the FUSION’2006-9th Conference on Multisource Information Fusion, (2006)Google Scholar
  46. 46.
    G. Rogova, M. Hadrazagic, M-O. St-Hilaire, M. Florea, P. Valin, Context-based information quality for sequential decision making, in Proceedings of the 2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), 2013Google Scholar
  47. 47.
    G. Rogova, Adaptive real-time threat assessment under uncertainty and conflict, in Proceedings of the 4th IEEE Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), San Antonio, TX, 2014Google Scholar
  48. 48.
    H. Hexmoor, S. Wilson, S. Bhattaram, A theoretical inter-organizational trust-based security model. Knowl. Eng. Rev. 21(2), 127–161 (2006)CrossRefGoogle Scholar
  49. 49.
    J. Huang, M.S. Fox, Trust judgment in knowledge provenance, in Proceedings of the 16th International Workshop on Database and Expert Systems Applications (DEXA’05), 2005Google Scholar
  50. 50.
    T. Saracevic, Relevance: a review of the literature and a framework for thinking on the notion in information science. Part II. J. Am. Soc. Inf. Sci. Technol. 58(3), 1915–1933 (2006)Google Scholar
  51. 51.
    H.D. White, Relevance theory and citations. J. Pragmat. 43(14), 3345–3361 (2011)CrossRefGoogle Scholar
  52. 52.
    Y. Wang, R. Wang, Anchoring data quality dimensions in ontological foundations. Commun. ACM 39(11), 86–95 (1996)CrossRefGoogle Scholar
  53. 53.
    I. Chengalur–Smith, D. Ballou, H. Pazer, The impact of data quality information on decision making: an exploratory analysis. IEEE Trans. Knowl. Data Eng. 11(6), 853–864 (1999)CrossRefGoogle Scholar
  54. 54.
    C.W. Fisher, I. Chengalur–Smith, D.P. Ballou, The impact of experience and time on the use of data quality information in decision making. Inf. Syst. Res. 14(2), 170–188 (2003)CrossRefGoogle Scholar
  55. 55.
    S. Fabre, A. Appriou, X. Briottet, Presentation and description of two classification methods using data fusion based on sensor management. Inf. Fusion 2, 47–71 (2001)CrossRefGoogle Scholar
  56. 56.
    F. Kobayashi, F. Avai, T. Fucuda, Sensor selection by reliability based on possibility measure, in Proceedings of the International Conference on Robotics and Automation, Detroit, MI, pp. 2614–2619, 1999Google Scholar
  57. 57.
    W. Jiang, A. Zhang, Q. Yang A new method to determine evidence discounting coefficient. In Advanced Intelligent Computing Theories and Applications with Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, ed. by D.S. Huang, D.C. Wunsch, D.S. Levine, K.H. Jo, vol. 5226, (Springer, Berlin, Heidelberg, 2008)Google Scholar
  58. 58.
    J. Besombes, V. Nimier, L. Cholvy, Information evaluation in fusion using information correlation, in: Proceedings of the 12th International Conference on Information Fusion, Seattle, WA, pp. 264–269, July 2009Google Scholar
  59. 59.
    G. Rogova, J. Kasturi, Reinforcement learning neural network for distributed decision making, in Proceedings of the Forth Conference on Information Fusion, August 2001, Montreal, CanadaGoogle Scholar
  60. 60.
    F. Pichon, D. Dubois, T. Denoeux, Relevance and truthfulness in information correction and fusion. Int. J. Approx. Reason. 53(2), 159–175 (2012)MathSciNetCrossRefGoogle Scholar
  61. 61.
    D.N. Walton, Appeal to Expert Opinion: Arguments from Authority (Penn State University Press, University Park, 1997)Google Scholar
  62. 62.
    J. Lang, M. Spear, S.F. Wu, Social manipulation of online recommender systems, in Proceedings of the 2nd International Conference on Social Informatics, 2010Google Scholar
  63. 63.
    Y. Wang, C.W. Hang, M.P. Singh, A probabilistic approach for maintaining trust based on evidence. J. Artif. Intell. Res. 40(1), 221–226 (2011)CrossRefGoogle Scholar
  64. 64.
    S. Parsons, K. Atkinson, Z. Li, P. McBurney, E. Sklar, M. Singh, J. Rowe, Argument schemes for reasoning about trust. Argument Comput. 5(2–3), 160–190 (2014)CrossRefGoogle Scholar
  65. 65.
    X. L. Dong, L. Berti-Equille, D. Srivastava, Integrating conflicting data: the role of source dependence, in Proceedings of the 35th International Conference on Very Large Databases, 2009Google Scholar
  66. 66.
    PROV-DM: the PROV data model,
  67. 67.
    A. Jøsang, R. Ismail, C. Boyd, A survey of trust and reputation systems for online service provision. Decis. Support. Syst. 43(2), 618–644 (2007)CrossRefGoogle Scholar
  68. 68.
    D. Koller, N. Friedman, Probabilistic Graphical Models: Principles and Techniques (MIT Press, Cambridge, MA, 2009)zbMATHGoogle Scholar
  69. 69.
    H. Prade, A Qualitative Bipolar Argumentative view of trust, in Scalable Uncertainty Management. SUM 2007, Lecture Notes in Computer Science, ed. by H. Prade, V.S. Subrahmanian, vol. 4772, (Springer, Berlin, Heidelberg, 2007)Google Scholar
  70. 70.
    M. Uddin, M. Amin, H. Le, T. Abdelzaher, B. Szymanski, T. Nguyen, On diversifying source selection in social sensing, in Proceedings of the 9th International Conference on Networked Sensing Systems (INSS), pp. 1–8, 2012,Google Scholar
  71. 71.
    R. Haenni, Shedding new light on Zadeh’s criticism of Dempster’s rule, in Proceedings of the 7th International Conference on Information Fusion (FUSION2005), pp. 879–884, 2005Google Scholar
  72. 72.
    J. Schubert, Conflict management in Dempster-Shafer theory by sequential discounting using the degree of falsity, in ed. by L. Magdalena, M. Ojeda-Aciego, J.L. Verdegay Proceedings of IPMU’08, Torremolinos (Malaga), pp. 298–305, 22–27 June 2008Google Scholar
  73. 73.
    D. Dubois, H. Prade, Combination of fuzzy information in the framework of possibility theory, in Data Fusion in Robotics and Machine Intelligence, ed. by M. A. Abidi, R. C. Gonzalez (Eds), (Academic Press, 1992), pp. 481–505Google Scholar
  74. 74.
    J. von Neuman, O. Morgenstern, Theory of Games and Economic Behavior (Princeton University Press, Princeton, 1947)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.The State University of New York at BuffaloBuffaloUSA

Personalised recommendations