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Evaluation of Heuristics for Product Data Models

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Business Process Management Workshops (BPM 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 397))

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Abstract

Product Data Model (PDM) is an example of a data-centric approach to modelling information-intensive business processes, which offers flexibility and facilitates process optimization. It is declarative, and as such, there may be multiple workflow designs that can produce the end product. To this end, several heuristics have been proposed. The contributions of this work are twofold: (i) we propose new heuristics that capitalize on established techniques for optimizing data-intensive workflows; and (ii) we extensively evaluate the existing solutions. Our results shed light on the merits of each heuristic and show that our proposal can yield significant benefits in certain cases. We provide our implementation as an open-source product.

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Notes

  1. 1.

    https://github.com/kmvarvou/pdm_heuristics.

References

  1. van der Aalst, W.M.P.: Re-engineering knock-out processes. Decis. Support Syst. 30(4), 451–468 (2001). https://doi.org/10.1016/S0167-9236(00)00136-6

    Article  Google Scholar 

  2. Agrawal, K., Benoit, A., Dufossé, F., Robert, Y.: Mapping filtering streaming applications. Algorithmica 62(1–2), 258–308 (2012). https://doi.org/10.1007/s00453-010-9453-6

    Article  MathSciNet  MATH  Google Scholar 

  3. Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: Discovering and navigating a collection of process models using multiple quality dimensions. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013. LNBIP, vol. 171, pp. 3–14. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06257-0_1

    Chapter  Google Scholar 

  4. Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: Mining configurable process models from collections of event logs. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 33–48. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40176-3_5

    Chapter  Google Scholar 

  5. Chawla, N., King, I., Sperduti, A.: User-guided discovery of declarative process models (2011)

    Google Scholar 

  6. Deshpande, A., Hellerstein, L.: Parallel pipelined filter ordering with precedence constraints. ACM Trans. Algorithms 8(4), 1–38 (2012)

    Article  MathSciNet  Google Scholar 

  7. Henriques, R., Rito Silva, A.: Object-centered process modeling: principles to model data-intensive systems. In: zur Muehlen, M., Su, J. (eds.) BPM 2010. LNBIP, vol. 66, pp. 683–694. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20511-8_62

    Chapter  Google Scholar 

  8. Kougka, G., Gounaris, A., Simitsis, A.: The many faces of data-centric workflow optimization: a survey. Int. J. Data Sci. Anal. 6(2), 81–107 (2018). https://doi.org/10.1007/s41060-018-0107-0

    Article  Google Scholar 

  9. Kougka, G., Varvoutas, K., Gounaris, A., Tsakalidis, G., Vergidis, K.: On knowledge transfer from cost-based optimization of data-centric workflows to business process redesign. In: Hameurlain, A., Tjoa, A.M. (eds.) Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIII. LNCS, vol. 12130, pp. 62–85. Springer, Heidelberg (2020). https://doi.org/10.1007/978-3-662-62199-8_3

    Chapter  Google Scholar 

  10. Künzle, V., Reichert, M.: Philharmonicflows: towards a framework for object-aware process management. J. Softw. Maintain. 23(4), 205–244 (2011)

    Article  Google Scholar 

  11. Orlicky, J.A., Plossl, G.W., Wight, O.W.: Structuring the bill of material for MRP. In: Lewis, M., Slack, N. (eds.) Operations Management: Critical Perspectives on Business and Management, vol. 58. Taylor & Francis, New York (2003)

    Google Scholar 

  12. Pesic, M., Schonenberg, H., van der Aalst, W.M.P.: Declare: full support for loosely-structured processes. In: 11th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2007), pp. 287–300 (2007)

    Google Scholar 

  13. Reijers, H.A., Limam, S., van der Aalst, W.M.P.: Product-based workflow design. J. Manag. Inf. Syst. 20(1), 229–262 (2003)

    Article  Google Scholar 

  14. Reijers, H.A., et al.: Evaluating data-centric process approaches: does the human factor factor in? Softw. Syst. Model. 16(3), 649–662 (2016). https://doi.org/10.1007/s10270-015-0491-z

    Article  Google Scholar 

  15. Schunselaar, D.: Configurable process trees : elicitation, analysis, and enactment. Ph.D. thesis, Department of Mathematics and Computer Science, October 2016. Proefschrift

    Google Scholar 

  16. Simitsis, A., Wilkinson, K., Dayal, U., Castellanos, M.: Optimizing ETL workflows for fault-tolerance. In: 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010), pp. 385–396 (2010)

    Google Scholar 

  17. Simitsis, A., Wilkinson, K., Castellanos, M., Dayal, U.: Optimizing analytic data flows for multiple execution engines. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, pp. 829–840 (2012)

    Google Scholar 

  18. Vanderfeesten, I.T.P., Reijers, H.A., van der Aalst, W.M.P.: Product-based workflow support. Inf. Syst. 36(2), 517–535 (2011)

    Article  Google Scholar 

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Acknowledgment

The research work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant” (Project Number:1052, Project Name: DataflowOpt). We would like also to thank Dr. Georgia Kougka for her comments and help.

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Authors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Konstantinos Varvoutas & Anastasios Gounaris

Authors
  1. Konstantinos Varvoutas
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  2. Anastasios Gounaris
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Corresponding author

Correspondence to Anastasios Gounaris .

Editor information

Editors and Affiliations

  1. Universidad de Sevilla, Seville, Spain

    Adela Del Río Ortega

  2. Kühne Logistics University, Hamburg, Germany

    Henrik Leopold

  3. Universidade do Estado do Rio de Janeiro – UERJ, Rio de Janeiro, Brazil

    Flávia Maria Santoro

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Varvoutas, K., Gounaris, A. (2020). Evaluation of Heuristics for Product Data Models. In: Del Río Ortega, A., Leopold, H., Santoro, F.M. (eds) Business Process Management Workshops. BPM 2020. Lecture Notes in Business Information Processing, vol 397. Springer, Cham. https://doi.org/10.1007/978-3-030-66498-5_26

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  • DOI: https://doi.org/10.1007/978-3-030-66498-5_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66497-8

  • Online ISBN: 978-3-030-66498-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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