Flexible Batch Configuration in Business Processes Based on Events

  • Luise Pufahl
  • Nico Herzberg
  • Andreas Meyer
  • Mathias Weske
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8831)


Organizations use business process management techniques to manage their core business processes more efficiently. A recent technique is the synchronization of multiple process instances by processing a set of activities as a batch – referred to as batch regions, e.g., the shipment of goods of several order processes at once. During process execution, events occur providing information about state changes of (a) the business process environment and (b) the business process itself. Thus, these events may influence batch processing. In this paper, we investigate how these events influence batch processing to enable flexible and improved batch region execution. Therefore, we introduce the concept of batch adjustments that are defined by rules following the Event-Condition-Action principle. Based on batch adjustment rules, relevant events are correlated at run-time to batch executions that fulfill the defined condition and are adjusted accordingly. We evaluate the concept by a real-world use case.


BPM Batch Processing Event Processing Flexible Configuration 


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  1. 1.
    van der Aalst, W., Barthelmess, P., Ellis, C., Wainer, J.: Proclets: A Framework for Lightweight Interacting Workflow Processes. IJCIS 10(4), 443–481 (2001)Google Scholar
  2. 2.
    van der Aalst, W.M.P., ter Hofstede, A.H.M., Kiepuszewski, B., Barros, A.P.: Workflow Patterns. Distributed and Parallel Databases 14(1), 5–51 (2003)CrossRefGoogle Scholar
  3. 3.
    Activiti: Activiti BPM Platform,
  4. 4.
    Bonitasoft: Bonita Process Engine,
  5. 5.
    Daum, M., Götz, M., Domaschka, J.: Integrating CEP and BPM: How CEP Realizes Functional Requirements of BPM Applications (Industry Article). In: DEBS, pp. 157–166. ACM (2012)Google Scholar
  6. 6.
    Dayal, U.: Active Database Management Systems. In: JCDKB, pp. 150–169 (1988)Google Scholar
  7. 7.
    Etzion, O., Niblett, P.: Event Processing in Action. Manning Publications Co. (2010)Google Scholar
  8. 8.
    University of Hamburg, D.o.C.S.: DesmoJ - A Framework for Discrete-Event Modeling and Simulation,
  9. 9.
    Hermosillo, G., Seinturier, L., Duchien, L.: Using Complex Event Processing for Dynamic Business Process Adaptation. In: SCC, pp. 466–473. IEEE (2010)Google Scholar
  10. 10.
    Herzberg, N., Meyer, A., Weske, M.: An Event Processing Platform for Business Process Management. In: EDOC, pp. 107–116. IEEE (2013)Google Scholar
  11. 11.
    Herzberg, N., Weske, M.: Enriching Raw Events to Enable Process Intelligence - Research Challenges. Tech. Rep. 73, HPI at the University of Potsdam (2013)Google Scholar
  12. 12.
    Knöpfel, A., Gröne, B., Tabeling, P.: Fundamental Modeling Concepts: Effective Communication of IT Systems. Wiley (2005)Google Scholar
  13. 13.
    Lanz, A., Reichert, M., Dadam, P.: Robust and flexible error handling in the aristaFlow BPM suite. In: Soffer, P., Proper, E. (eds.) CAiSE Forum 2010. LNBIP, vol. 72, pp. 174–189. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Liu, J., Hu, J.: Dynamic Batch Processing in Workflows: Model and Implementation. Future Generation Computer Systems 23(3), 338–347 (2007)CrossRefGoogle Scholar
  15. 15.
    Luckham, D.: The Power of Events. Addison-Wesley (2002)Google Scholar
  16. 16.
    Luckham, D., Schulte, R.: Event Processing Glossary - Version 2.0 (July 2011),
  17. 17.
    Méndez, C.A., Cerdá, J., Grossmann, I.E., Harjunkoski, I., Fahl, M.: State-of-the-art review of optimization methods for short-term scheduling of batch processes. Computers & Chemical Engineering 30(6), 913–946 (2006)CrossRefGoogle Scholar
  18. 18.
    Meyer, A., Pufahl, L., Fahland, D., Weske, M.: Modeling and Enacting Complex Data Dependencies in Business Processes. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 171–186. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  19. 19.
    Motahari-Nezhad, H.R., Saint-Paul, R., Casati, F., Benatallah, B.: Event Correlation for Process Discovery from Web Service Interaction Logs. VLDB Journal 20(3), 417–444 (2011)CrossRefGoogle Scholar
  20. 20.
    Pufahl, L., Meyer, A., Weske, M.: Batch Regions: Process Instance Synchronization based on Data. In: EDOC. IEEE (2014) (accepted for publication)Google Scholar
  21. 21.
    Pufahl, L., Weske, M.: Batch Activities in Process Modeling and Execution. In: Basu, S., Pautasso, C., Zhang, L., Fu, X. (eds.) ICSOC 2013. LNCS, vol. 8274, pp. 283–297. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  22. 22.
    Reichert, M., Dadam, P.: Enabling Adaptive Process-aware Information Systems with ADEPT2. In: Handbook of Research on Business Process Modeling, pp. 173–203. Information Science Reference (2009)Google Scholar
  23. 23.
    Sadiq, S., Orlowska, M., Sadiq, W., Schulz, K.: When Workflows Will Not Deliver: The Case of Contradicting Work Practice. BIS 1, 69–84 (2005)Google Scholar
  24. 24.
    Weidlich, M., Ziekow, H., Mendling, J., Günther, O., Weske, M., Desai, N.: Event-based monitoring of process execution violations. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 182–198. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  25. 25.
    Weske, M.: Business Process Management: Concepts, Languages, Architectures. Second Edition, 2nd edn. Springer (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Luise Pufahl
    • 1
  • Nico Herzberg
    • 1
  • Andreas Meyer
    • 1
  • Mathias Weske
    • 1
  1. 1.Hasso Plattner Institute at the University of PotsdamGermany

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