Skip to main content
SpringerLink
Log in
Book cover

Information Quality in Information Fusion and Decision Making pp 3–29Cite as

Information Quality in Fusion-Driven Human-Machine Environments

  • Chapter
  • First Online:

Part of the book series: Information Fusion and Data Science ((IFDS))

Abstract

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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    JDL: Joint Directors of Laboratories, a US DoD government committee overseeing US defense technology R&D; the Data Fusion Group of the JDL created the original JDL Data Fusion Model.

  2. 2.

    In the machine-human system, context “users” can be either humans or automated agents and models.

  3. 3.

    Usually this measure is referred to uncertainty only and is called “higher order uncertainty,” which is treated without relation to the other quality attributes. Here we define this measure for any quality characteristic and consider it with relation to other attributes.

References

  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. 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. F. Castanedo, A review of data fusion techniques. Sci. World J. 2013, 704504 (2013). https://doi.org/10.1155/2013/704504

    Article  Google Scholar 

  4. Y. Lee, L. Pipino, J. Frank, R. Wang, Journey to Data Quality (MIT Press, Cambridge, 2006)

    Google Scholar 

  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, 2001

    Google Scholar 

  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. F. White, A model for data fusion, in Proceedings of the 1st National Symposium on Sensor Fusion, 1988

    Google Scholar 

  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. 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 2004

    Google Scholar 

  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, 2004

    Google Scholar 

  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. 116–119

    Google Scholar 

  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, 2013

    Google Scholar 

  13. B. Dasarathy, Sensor fusion potential exploitation- innovative architectures and illustrative applications. IEEE Proc. 85(1), 24 (1997)

    Article  Google Scholar 

  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. J. Boyd, A Discourse on Winning and Losing (Maxwell AFB Lecture, 1987)

    Google Scholar 

  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. M. Endsley, Toward a theory of situation awareness in dynamic systems. Hum. Factors J Hum Factors Ergon Soc 37(1), 32–64 (1995)

    Article  Google Scholar 

  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. 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, 2003

    Google Scholar 

  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. 1929–1936

    Google Scholar 

  21. Standard 8402, 3. I, International organization of standards, 1986

    Google Scholar 

  22. J.A. O'Brien, G. Marakas, Introduction to Information Systems (McGraw-Hill/Irwin, New York City, US, 2005)

    Google Scholar 

  23. R. Wang, D. Strong, Beyond accuracy: what data quality means to data consumers. J. Manag. Inf. Syst. 12, 5–34 (1996)

    Article  Google Scholar 

  24. J.B. Juran, A.B. Godfrey, Juran’s Quality Handbook, 5th edn. (McGraw-Hill, New York, 1988)

    Google Scholar 

  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 2009

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 225–254

    Google Scholar 

  29. G. Rogova, E. Bosse, Information quality in information fusion, in Proceedings of the 13th International Conference on Information Fusion, Edinburg, Scotland, July 2010

    Google Scholar 

  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, 1996

    Google Scholar 

  31. P. Gardenfors, On the logic of relevance. Synthese 37(3), 351 (1978)

    Article  MathSciNet  Google Scholar 

  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. 595–600

    Google Scholar 

  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. 1158–1165

    Google Scholar 

  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. 285–324

    Google Scholar 

  35. M. Smithson, Ignorance and Uncertainty: Emerging Paradigms (Springer, New York, 1989)

    Book  Google Scholar 

  36. E. Bossé, J. Roy, S. Wark, Concepts, models, and tools for information fusion (Artech House, Norwood, 2007)

    MATH  Google Scholar 

  37. P. Krause, D. Clark, Representing Uncertain Knowledge: An Artificial Intelligence Approach (Kluwer Academic Publishers, Dordrecht, 1993)

    Book  Google Scholar 

  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. 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, 2013

    Google Scholar 

  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, 2002

    Google Scholar 

  41. G. Shafer, A Mathematical Theory of Evidence (Princeton University Press, Princeton, 1976)

    MATH  Google Scholar 

  42. D. Dubois, H. Prade, Possibility Theory: An Approach to Computerized Processing of Uncertainty (Plenum, New York, 1988)

    Book  Google Scholar 

  43. R. Yager, Conditional approach to possibility-probability fusion. IEEE Trans. Fuzzy Syst. 20(1), 46–55 (2012)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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), 2013

    Google Scholar 

  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, 2014

    Google Scholar 

  48. H. Hexmoor, S. Wilson, S. Bhattaram, A theoretical inter-organizational trust-based security model. Knowl. Eng. Rev. 21(2), 127–161 (2006)

    Article  Google Scholar 

  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), 2005

    Google Scholar 

  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. H.D. White, Relevance theory and citations. J. Pragmat. 43(14), 3345–3361 (2011)

    Article  Google Scholar 

  52. Y. Wang, R. Wang, Anchoring data quality dimensions in ontological foundations. Commun. ACM 39(11), 86–95 (1996)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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, 1999

    Google Scholar 

  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. 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 2009

    Google Scholar 

  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, Canada

    Google Scholar 

  60. F. Pichon, D. Dubois, T. Denoeux, Relevance and truthfulness in information correction and fusion. Int. J. Approx. Reason. 53(2), 159–175 (2012)

    Article  MathSciNet  Google Scholar 

  61. D.N. Walton, Appeal to Expert Opinion: Arguments from Authority (Penn State University Press, University Park, 1997)

    Google Scholar 

  62. J. Lang, M. Spear, S.F. Wu, Social manipulation of online recommender systems, in Proceedings of the 2nd International Conference on Social Informatics, 2010

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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, 2009

    Google Scholar 

  66. PROV-DM: the PROV data model, https://www.w3.org/TR/prov-dm/

  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)

    Article  Google Scholar 

  68. D. Koller, N. Friedman, Probabilistic Graphical Models: Principles and Techniques (MIT Press, Cambridge, MA, 2009)

    MATH  Google Scholar 

  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. 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. 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, 2005

    Google Scholar 

  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 2008

    Google Scholar 

  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. 481–505

    Google Scholar 

  74. J. von Neuman, O. Morgenstern, Theory of Games and Economic Behavior (Princeton University Press, Princeton, 1947)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The State University of New York at Buffalo, Buffalo, NY, USA

    Galina L. Rogova

Authors
  1. Galina L. Rogova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Galina L. Rogova .

Editor information

Editors and Affiliations

  1. IMT-Atlantique, Brest, France

    Éloi Bossé

  2. The State University of New York at Buffalo, Buffalo, NY, USA

    Galina L. Rogova

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Rogova, G.L. (2019). Information Quality in Fusion-Driven Human-Machine Environments. In: Bossé, É., Rogova, G. (eds) Information Quality in Information Fusion and Decision Making. Information Fusion and Data Science. Springer, Cham. https://doi.org/10.1007/978-3-030-03643-0_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03643-0_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03642-3

  • Online ISBN: 978-3-030-03643-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation