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

Andreas Wombacher

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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.
Original languageUndefined
Title of host publicationProceedings of the 9th International Conference on Business Process Management (BPM 2011)
EditorsStefanie Rinderle-Ma, Farouk Toumani, Karsten Wolf
Place of PublicationBerlin
PublisherSpringer
Pages329-344
Number of pages16
ISBN (Print)978-3-642-23058-5
DOIs
Publication statusPublished - Sep 2011
Event9th International Conference on Business Process Management, BPM 2011 - Clermont-Ferrand, France
Duration: 30 Aug 20112 Sep 2011
Conference number: 9

Publication series

NameLNCS
PublisherSpringer Verlag
Volume6896
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Business Process Management, BPM 2011
Abbreviated titleBPM
CountryFrance
CityClermont-Ferrand
Period30/08/112/09/11

Keywords

  • METIS-278729
  • EWI-20313
  • IR-78044

Cite this

Wombacher, A. (2011). A-Posteriori Detection of Sensor Infrastructure Errors in Correlated Sensor Data and Business Workflows. In S. Rinderle-Ma, F. Toumani, & K. Wolf (Eds.), Proceedings of the 9th International Conference on Business Process Management (BPM 2011) (pp. 329-344). (LNCS; Vol. 6896). Berlin: Springer. https://doi.org/10.1007/978-3-642-23059-2_25