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A-Posteriori Detection of Sensor Infrastructure Errors in Correlated Sensor Data and Business Workflows

  • Andreas Wombacher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6896)

Abstract

Some physical objects are influenced by business workflows and are observed by sensors. Since both sensor infrastructures and business workflows must deal with imprecise information, the correlation of sensor data and business workflow data related to physical objects might be used a-posteriori to determine the source of the imprecision. In this paper, an information theory based approach is presented to distinguish sensor infrastructure errors from inhomogeneous business workflows. This approach can be applied on detecting imprecisions in the sensor infrastructure, like e.g. sensor errors or changes of the sensor infrastructure deployment.

Keywords

Mutual Information Sensor Data Physical Object Sensor Reading Digital Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Jedermann, R., Behrens, C., Westphal, D., Lang, W.: Applying autonomous sensor systems in logistics–combining sensor networks, rfids and software agents. Sensors and Actuators A: Physical, 370–375 (2006)Google Scholar
  2. 2.
    Wieland, M., Kopp, O., Nicklas, D., Leymann, F.: Towards context-aware workflows. In: CAiSE 2007 WS Proc., vol. 2 (2007)Google Scholar
  3. 3.
    Wieland, M., Käppeler, U.P., Levi, P., Leymann, F., Nicklas, D.: Towards integration of uncertain sensor data into context-aware workflows. In: GI Jahrestagung, pp. 2029–2040 (2009)Google Scholar
  4. 4.
    Soffer, P.: Mirror, mirror on the wall, can i count on you at all? exploring data inaccuracy in business processes. In: Bider, I., Halpin, T., Krogstie, J., Nurcan, S., Proper, E., Schmidt, R., Ukor, R. (eds.) BPMDS 2010 and EMMSAD 2010. Lecture Notes in Business Information Processing, vol. 50, pp. 14–25. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Lenz, R., Reichert, M.: It support for healthcare processes - premises, challenges, perspectives. Data Knowl. Eng. 61, 39–58 (2007)CrossRefGoogle Scholar
  6. 6.
    Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDB J 10, 334–350 (2001)CrossRefzbMATHGoogle Scholar
  7. 7.
    Wombacher, A.: A-posteriori detection of sensor infrastructure errors in correlated sensor data and business workflows. Technical report (2011)Google Scholar
  8. 8.
    Hopcroft, J.E., Motwani, R., Ullman, J.D.: Introduction to Automata Theory, Languages, and Computation. Addison Wesley, Reading (2001)zbMATHGoogle Scholar
  9. 9.
    Oppenheim, A.V., Schafer, R.W.: Discrete-Time Signal Processing. Prentice Hall Signal Processing Series. Prentice Hall, Englewood Cliffs (1989)zbMATHGoogle Scholar
  10. 10.
    Tzschach, H., Haßlinger, G.: Codes für den störungssicheren Datentransfer. Oldenburg (1993)Google Scholar
  11. 11.
    Zlatev, Z., Wombacher, A.: Consistency between e3-value models and activity diagrams in a multi-perspective development method. In: Chung, S. (ed.) OTM 2005. LNCS, vol. 3760, pp. 520–538. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Bodenstaff, L., Wombacher, A., Reichert, M.U., Wieringa, R.: Monitoring Collaboration from a Value Perspective. In: Intl. Conf. on Digital Ecosystems and Technologies (2007)Google Scholar
  13. 13.
    Bodenstaff, L., Wombacher, A., Reichert, M., Jaeger, M.C.: Monitoring dependencies for SLAs: The mode4SLA approach. In: IEEE SCC, pp. 21–29 (2008)Google Scholar
  14. 14.
    van Keulen, M., de Keijzer, A., Alink, W.: A probabilistic XML approach to data integration. In: ICDE, pp. 459–470. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  15. 15.
    Tan, W., Missier, P., Madduri, R.K., Foster, I.T.: Building scientific workflow with taverna and BPEL: A comparative study in cagrid. In: Feuerlicht, G., Lamersdorf, W. (eds.) ICSOC 2008. LNCS, vol. 5472, pp. 118–129. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  16. 16.
    Ludäscher, B., Weske, M., McPhillips, T.M., Bowers, S.: Scientific workflows: Business as usual? In: Dayal, U., Eder, J., Koehler, J., Reijers, H.A. (eds.) BPM 2009. LNCS, vol. 5701, pp. 31–47. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    Wieringa, R., Pijpers, V., Bodenstaff, L., Gordijn, J.: Value-driven coordination process design using physical delivery models. In: Li, Q., Spaccapietra, S., Yu, E.S.K., Olivé, A. (eds.) ER 2008. LNCS, vol. 5231, pp. 216–231. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  18. 18.
    Zeng, Y., Doshi, P.: Model identification in interactive influence diagrams using mutual information. Web Intelli. and Agent Sys. 8, 313–327 (2010)Google Scholar
  19. 19.
    Vatcheva, I., de Jong, H., Bernard, O., Mars, N.J.I.: Experiment selection for the discrimination of semi-quantitative models of dynamical systems. Artificial Intelligence 170, 472–506 (2006)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Andreas Wombacher
    • 1
  1. 1.Database GroupUniversity of TwenteThe Netherlands

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