Advertisement

Focusing on Precision- and Trust-Propagation in Knowledge Processing Systems

  • Markus JägerEmail author
  • Jussi Nikander
  • Stefan Nadschläger
  • Van Quoc Phuong Huynh
  • Josef Küng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10646)

Abstract

In knowledge processing systems, when gathered data and knowledge from several (external sources) is used, the trustworthiness and quality of the information and data has to be evaluated before continuing processing with these values. We try to address the problem of the evaluation and calculation of possible trusting values by considering established methods from known literature and recent research.

After the calculation, the obtained values have to be processed, depending on the complexity of the system, where the values are used and needed. Here the way of trust propagation, precision propagation and their aggregation or fusion is crucial, when multiple input values come together in one processing step. We discuss elaborated trust definitions already available and according options for trust and precision aggregation and propagation in units of knowledge processing.

Keywords

Trust Precision Trust measurement Precision measurement Trust aggregation Precision aggregation Trust fusion Precision fusion Trust propagation Precision propagation Trust management Precision management Sensors Sensor precision Knowledge processing systems 

Notes

Acknowledgements

The research leading to these results was partly funded by the federal county of Upper Austria.

References

  1. 1.
    Ackoff, R.L.: From data to wisdom. J. Appl. Syst. Anal. 16, 3–9 (1989)Google Scholar
  2. 2.
    Alavi, M., Leidner, D.E.: Review: knowledge management and knowledge management systems: conceptual foundations and research issues. MIS Q. 25, 107–136 (2001)CrossRefGoogle Scholar
  3. 3.
    Bellinger, G., Castro, D., Mills, A.: Data, information, knowledge, and wisdom (2004)Google Scholar
  4. 4.
    Buneman, P., Khanna, S., Tan, W.-C.: Data provenance: some basic issues. In: Kapoor, S., Prasad, S. (eds.) FSTTCS 2000. LNCS, vol. 1974, pp. 87–93. Springer, Heidelberg (2000). doi: 10.1007/3-540-44450-5_6 CrossRefGoogle Scholar
  5. 5.
    Buneman, P., Davidson, S.B.: Data provenance-the foundation of data quality (2010). http://www.sei.cmu.edu/measurement/research/upload/Davidson.pdf
  6. 6.
    Cugola, G., Margara, A., Matteucci, M., Tamburrelli, G.: Introducing uncertainty in complex event processing: model, implementation, and validation. J. Comput. 97, 103–144 (2015)CrossRefGoogle Scholar
  7. 7.
    Dai, C., Lin, D., Bertino, E., Kantarcioglu, M.: An approach to evaluate data trustworthiness based on data provenance. In: Jonker, W., Petković, M. (eds.) SDM 2008. LNCS, vol. 5159, pp. 82–98. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-85259-9_6 CrossRefGoogle Scholar
  8. 8.
    Dividino, R., Sizov, S., Staab, S., Schueler, B.: Querying for provenance, trust, uncertainty and other meta knowledge in RDF. Web Semant. Sci. Serv. Agents World Wide Web 7(3), 204–219 (2009)CrossRefGoogle Scholar
  9. 9.
    Hajidimitriou, Y.A., Sklavounos, N.S., Rotsios, K.P., et al.: The impact of trust on knowledge transfer in international business systems. Sci. Bull. Econ. Sci. 11(2), 39–49 (2012)Google Scholar
  10. 10.
    Jäger, M., Phan, T.N., Huber, C., Küng, J.: Incorporating trust, certainty and importance of information into knowledge processing systems – an approach. In: Dang, T.K., Wagner, R., Küng, J., Thoai, N., Takizawa, M., Neuhold, E. (eds.) FDSE 2016. LNCS, vol. 10018, pp. 3–19. Springer, Cham (2016). doi: 10.1007/978-3-319-48057-2_1 CrossRefGoogle Scholar
  11. 11.
    Jäger, M., Küng, J.: Introducing the factor importance to trust of sources and certainty of data in knowledge processing systems - a new approach for incorporation and processing. In: Proceedings of the 50th Hawaii International Conference on System Sciences, pp. 4298–4307. IEEE (2017)Google Scholar
  12. 12.
    Jøsang, A., Knapskog, S.J.: A metric for trusted systems (full paper). In: Proceedings of the 21st National Information Systems Security Conference, NSA (1998)Google Scholar
  13. 13.
    Jøsang, A., Marsh, S., Pope, S.: Exploring different types of trust propagation. In: Stølen, K., Winsborough, W.H., Martinelli, F., Massacci, F. (eds.) iTrust 2006. LNCS, vol. 3986, pp. 179–192. Springer, Heidelberg (2006). doi: 10.1007/11755593_14 CrossRefGoogle Scholar
  14. 14.
    Karlsson, A., Hammarfelt, B., Joe Steinhauer, H., Falkman, G., Olson, N., Nelhans, G., Nolin, J.: Modeling uncertainty in bibliometrics and information retrieval: an information fusion approach. J. Sci. 102, 2255–2274 (2015)Google Scholar
  15. 15.
    McKnight, D.H.: Trust in information technology. In: The Blackwell Encyclopedia of Management: Operations Management. Blackwell Publishers, New York (2005)Google Scholar
  16. 16.
    Maia, G., Alcântara, J.: Reasoning about trust and belief in possibilistic answer set programming. In: 2016 5th Brazilian Conference on Intelligent Systems (BRACIS), pp. 217–222. IEEE (2016)Google Scholar
  17. 17.
    Schenk, S., Dividino, R., Staab, S.: Reasoning with provenance, trust and all that other meta knowledge in owl. In: Proceedings of the First International Conference on Semantic Web in Provenance Management, vol. 526, pp. 11–16. CEUR-WS. org (2009)Google Scholar
  18. 18.
    Streiner, D.L., Norman, G.R.: “Precision” and “accuracy”: two terms that are neither. J. Clin. Epidemiol. 59, 327–330 (2006). ElsevierCrossRefGoogle Scholar
  19. 19.
    Usman, M.: Design and implementation of an iPad web application for indoor-outdoor navigation and tracking locations. Master’s thesis, Aalto University (2012)Google Scholar
  20. 20.
    Tan, W.-C.: Research problems in data provenance. IEEE Data Eng. Bull. 27, 45–52 (2004)Google Scholar
  21. 21.
    Elmenreich, W.: An Introduction to Sensor Fusion. Research Report 47/2001. Vienna University of Technology, Austria, Institut für Technische Informatik (2001)Google Scholar
  22. 22.
    Chen, Z., Tian, L., Lin, C.: Trust model of wireless sensor networks and its application in data fusion. Sensors 17(4), 703 (2017)CrossRefGoogle Scholar
  23. 23.
    Cho J.H., Chan K., Mikulski, D.: Trust-based information and decision fusion for military convoy operations. In: Military Communications Conference (MILCOM), pp. 1387–1392 (2014)Google Scholar
  24. 24.
    Ganeriwal, S., Balzano, L.K., Srivastava, M.B.: Reputation-based framework for high integrity sensor networks. ACM Trans. Sensor Netw. (TOSN) 4(3), 1–37 (2008)CrossRefGoogle Scholar
  25. 25.
    Dhulipala, V.R.S., Karthik, N., Chandrasekaran, R.: A novel heuristic approach based trust worthy architecture for wireless sensor networks. Wirel. Pers. Commun. 70, 1–17 (2013)CrossRefGoogle Scholar
  26. 26.
    Fan, C.Q., Wang, S.G., Sun, Q.B., Wang, H.M., Zhang, G.W., Yang, F.C.A.: Trust valuation method of sensors based on energy monitoring. Acta Electronica Sinica 41(4), 646–651 (2013)Google Scholar
  27. 27.
    Sahoo, S.S., Sardar, A.R., Singh, M., Ray, S., Sarkar, S.K.: A Bio-inspired and trust based approach for clustering in WSN. Nat. Comput. Int. J. 15(3), 423–434 (2016)CrossRefMathSciNetGoogle Scholar
  28. 28.
    Draheim, D.: Semantics of the Probabilistic Typed Lambda Calculus: Markov Chain Semantics, Termination Behavior, and Denotational Semantics. Springer, Heidelberg (2017). doi: 10.1007/978-3-642-55198-7 CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Markus Jäger
    • 1
    Email author
  • Jussi Nikander
    • 2
  • Stefan Nadschläger
    • 1
  • Van Quoc Phuong Huynh
    • 1
  • Josef Küng
    • 1
  1. 1.Institute for Application Oriented Knowledge Processing (FAW), Faculty of Engineering and Natural Sciences (TNF), Johannes Kepler University Linz (JKU)LinzAustria
  2. 2.Natural Resources Institute Finland (LUKE)HelsinkiFinland

Personalised recommendations