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History-Aware, Real-Time Risk Detection in Business Processes

  • Conference paper
On the Move to Meaningful Internet Systems: OTM 2011 (OTM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7044))

Abstract

This paper proposes a novel approach for identifying risks in executable business processes and detecting them at run-time. The approach considers risks in all phases of the business process management lifecycle, and is realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of faults to occur. Both historical and current process execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a process automation suite to prompt the results to the user who may take remedial actions. The proposed architecture has been implemented in the YAWL system and its performance has been evaluated in practice.

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Author information

Authors and Affiliations

  1. Queensland University of Technology, Australia

    Raffaele Conforti, Marcello La Rosa & Arthur H. M. ter Hofstede

  2. Università della Calabria, Italy

    Giancarlo Fortino

  3. Eindhoven University of Technology, The Netherlands

    Arthur H. M. ter Hofstede

Authors
  1. Raffaele Conforti
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  2. Giancarlo Fortino
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  3. Marcello La Rosa
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  4. Arthur H. M. ter Hofstede
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Editor information

Editors and Affiliations

  1. STAR Lab, Vrije Universiteit Brussel (VUB), Bldg G/10, Pleinlaan 2, 1050, Brussel, Belgium

    Robert Meersman

  2. DEBII, Curtin University of Technology, Technology Park, De Laeter Way, 6102, Bentley, WA, Australia

    Tharam Dillon

  3. Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, 28660, Boadilla del Monte, Madrid, Spain

    Pilar Herrero

  4. Smeal College of Business, Pennsylvania State University, University Park, PA 16802, U.S.A.

    Akhil Kumar

  5. Institute of Databases and Information Systems, Ulm University, Germany

    Manfred Reichert

  6. City University of Hong Kong, Hong Kong

    Li Qing

  7. National University of Singapore (NUS), Singapore

    Beng-Chin Ooi

  8. Dipartemento Tecnologie dell’Informazione, Universitá degli Studi di Milano, Via Bramante 65, 26013, Crema, Italy

    Ernesto Damiani

  9. VU Station B #1829, Vanderbilt University, 2015 Terrace Place, TN 37203, Nashville, USA

    Douglas C. Schmidt

  10. Virginia Tech, 24060, Blacksburg, VA, USA

    Jules White

  11. Digital Enterprise Research Institute (DERI), National University of Ireland, IDA Business Park, Lower Dangan, Galway, Ireland

    Manfred Hauswirth

  12. Kno.e.sis Center, Wright State University, Dayton, Ohio, USA

    Pascal Hitzler

  13. IBM India Research Lab, 4, Block C, Institutional Area, Vasant Kunj, 110 070, New Delhi, India

    Mukesh Mohania

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© 2011 Springer-Verlag Berlin Heidelberg

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Conforti, R., Fortino, G., La Rosa, M., ter Hofstede, A.H.M. (2011). History-Aware, Real-Time Risk Detection in Business Processes. In: Meersman, R., et al. On the Move to Meaningful Internet Systems: OTM 2011. OTM 2011. Lecture Notes in Computer Science, vol 7044. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25109-2_8

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  • DOI: https://doi.org/10.1007/978-3-642-25109-2_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25108-5

  • Online ISBN: 978-3-642-25109-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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