Knowledge and Information Systems

, Volume 57, Issue 3, pp 655–684 | Cite as

Time prediction on multi-perspective declarative business processes

  • Andres Jimenez-Ramirez
  • Irene Barba
  • Juan Fernandez-Olivares
  • Carmelo Del Valle
  • Barbara Weber
Regular Paper


Process-aware information systems (PAISs) are increasingly used to provide flexible support for business processes. The support given through a PAIS is greatly enhanced when it is able to provide accurate time predictions which is typically a very challenging task. Predictions should be (1) multi-dimensional and (2) not based on a single process instance. Furthermore, the prediction system should be able to (3) adapt to changing circumstances and (4) deal with multi-perspective declarative languages (e.g., models which consider time, resource, data and control flow perspectives). In this work, a novel approach for generating time predictions considering the aforementioned characteristics is proposed. For this, first, a multi-perspective constraint-based language is used to model the scenario. Thereafter, an optimized enactment plan (representing a potential execution alternative) is generated from such a model considering the current execution state of the process instances. Finally, predictions are performed by evaluating a desired function over this enactment plan. To evaluate the applicability of our approach in practical settings we apply it to a real process scenario. Despite the high complexity of the considered problems, results indicate that our approach produces a satisfactory number of good predictions in a reasonable time.


Flexible process-aware information systems Time prediction Constraint programming Planning and scheduling Constraint-based process models Decision support systems 

Supplementary material


  1. 1.
    Barba I, Del Valle C, Weber B, Jimenez-Ramirez A (2013) Automatic generation of optimized business process models from constraint-based specifications. Int J Cooper Inf Syst 22(2):1350009CrossRefGoogle Scholar
  2. 2.
    Barba I, Weber B, Del Valle C, Jimenez-Ramirez A (2013) User recommendations for the optimized execution of business processes. Data Knowl Eng 86:61–84CrossRefGoogle Scholar
  3. 3.
    Barba I, Lanz A, Weber B, Reichert M, del Valle C (2012) Optimized time management for declarative workflows. In: Enterprise, business-process and information systems modeling, volume 113 of LNBIP. Springer, Berlin, pp 195–210Google Scholar
  4. 4.
    Bolt A, Sepúlveda M (2013) Process remaining time prediction using query catalogs. In: Proceedings of BPI, pp 54–65Google Scholar
  5. 5.
    Brereton P, Kitchenham B, Budgen D (2008) Using a protocol template for case study planning. In: Proceedings of EASE 2008. BCS-eWiCGoogle Scholar
  6. 6.
    Brucker P, Knust S (2006) Complex scheduling (GOR-publications). Springer, Secaucus, NJzbMATHGoogle Scholar
  7. 7.
    Business Process Model and Notation (BPMN), Version 2.0. (2011). Accessed 01 May 2014
  8. 8.
    Dumas M, van der Aalst WMP, ter Hofstede AH (eds) (2005) Process-aware information systems: bridging people and software through process technology. Wiley-Interscience, Hoboken, NJGoogle Scholar
  9. 9.
    Eder J, Panagos E, Rabinovich M (1999) Time constraints in workflow systems. In: Advanced information systems engineering, volume 1626 of LNCS, pp 286–300Google Scholar
  10. 10.
    Eder J, Pichler H (2002) Duration histograms for workflow systems. In: Proceedings of IFIP TC8/WG8.1 working conference on engineering information systems in the internet context, pp 239–253Google Scholar
  11. 11.
    Eder J, Pichler H (2005) Probabilistic calculation of execution intervals for workflows. In: Proceedings of TIME 2005, pp 183–185Google Scholar
  12. 12.
    Eder J, Pichler H, Gruber W, Ninaus M (2003) Personal schedules for workflow systems. In: Proceedings of BPM, pp 216–231Google Scholar
  13. 13.
    Gantt HL (1913) Work, wages, and profits. Engineering Magazine Co., New YorkGoogle Scholar
  14. 14.
    Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. W. H. Freeman & Co., New York, NYzbMATHGoogle Scholar
  15. 15.
    Ghallab M, Nau D, Traverso P (2004) Automated planning: theory and practice. Morgan Kaufmann, AmsterdamzbMATHGoogle Scholar
  16. 16.
    Global Constraint Catalog. Accessed 20 June 2017
  17. 17.
    IBM. CPLEX CP Optimizer. (2016). Accessed 11 July 2016
  18. 18.
    IBM. IBM ILOG CPLEX Optimization Studio. (2016). Accessed 11 July 2016
  19. 19.
    Jimenez-Ramirez A, Barba I, Weber B, Del Valle C (2015) Generating optimized configurable business process models in scenarios subject to uncertainty. Inf Softw Technol 57:571–594CrossRefGoogle Scholar
  20. 20.
    Jimenez-Ramirez A, Barba I, del Valle C, Weber B (2013) Generating multi-objective optimized business process enactment plans. In: Advanced information systems engineering. Volume 7908 of LNCS. Springer, Berlin, pp 99–115Google Scholar
  21. 21.
    Jimenez-Ramirez A, Barba I, Del Valle C, Weber B (2013) OptBPPlanner: automatic generation of optimized business process enactment plans. In: Proceedings of ISD. Springer US, pp 429–442Google Scholar
  22. 22.
    Lanz A, Weber B, Reichert M (2012) Time patterns for process-aware information systems. Requir Eng 19:113–141CrossRefGoogle Scholar
  23. 23.
    Leitner P, Wetzstein B, Rosenberg F, Michlmayr A, Dustdar S, Leymann F (2010) Runtime prediction of service level agreement violations for composite services. In: Service-oriented computing. ICSOC/ServiceWave 2009 workshops, volume 6275 of LNCS. Springer, Berlin, pp 176–186Google Scholar
  24. 24.
    Marzolla M, Mirandola R (2007) Performance prediction of web service workflows. In: Software architectures. components, and applications, volume 4880 of LNCS. Springer, Berlin, pp 127–144Google Scholar
  25. 25.
    Montali M (2009)Specification and verification of declarative open interaction models: a logic-based approach. Ph.D. thesis, Department of Electronics, Computer Science and Telecommunications Engineering, University of BolognaGoogle Scholar
  26. 26.
    Montali M, Chesani F, Mello P, Maggi FM (2013) Towards data-aware constraints in declare. In: Proceedings of the 28th annual ACM symposium on applied computing. SAC ’13, pp 1391–1396Google Scholar
  27. 27.
    Pesic M (2008) Constraint-based workflow management systems: shifting control to users. Ph.D thesis, Eindhoven University of Technology, EindhovenGoogle Scholar
  28. 28.
    Pesic M, Schonenberg MH, Sidorova N, van der Aalst WMP (2007) Constraint-based workflow models: change made easy. OTM Conf 1:77–94Google Scholar
  29. 29.
    Polato Mi, Sperduti A, Burattin A, de Leoni M (2014) Data-aware remaining time prediction of business process instances. In: Proceedings of IJCNN, pp 816–823Google Scholar
  30. 30.
    Process Specification Language project. (1977). Accessed 1 May 2014
  31. 31.
    Rossi F, van Beek P, Walsh T (eds) (2006) Handbook of constraint programming. Elsevier, LondonzbMATHGoogle Scholar
  32. 32.
    Reichert M, Weber B (2012) Enabling flexibility in process-aware information systems. Springer, BerlinCrossRefzbMATHGoogle Scholar
  33. 33.
    Reijers H (2006) Case prediction in bpm systems: a research challenge. J Korean Inst Ind Eng 33:1–10Google Scholar
  34. 34.
    Rozinat A, Wynn MT, van der Aalst WMP, ter Hofstede AHM, Fidge C (2008) Workflow simulation for operational decision support using design, historic and state information. In: Proceedings of BPM, vol. 5240, pp 196–211Google Scholar
  35. 35.
    Russell N, van der Aalst WMP, ter Hofstede AHM, Edmond D (2005) Workflow resource patterns: identification, representation and tool support. In: Proceedings of CAiSE, pp 216–232Google Scholar
  36. 36.
    Salido MA (2010) Introduction to planning, scheduling and constraint satisfaction. J Intell Manuf 21(1):1–4CrossRefGoogle Scholar
  37. 37.
    Schellekens B (2009) Cycle time prediction in staffware. Master’s thesis, University of Technology, EindhovenGoogle Scholar
  38. 38.
    Semmelrodt Franziska, Knuplesch D, Reichert M (2014) Modeling the resource perspective of business process compliance rules with the extended compliance rule graph. In: Enterprise, business-process and information systems modeling. Springer, pp 48–63Google Scholar
  39. 39.
    Souki M (2011) Operating theatre scheduling with fuzzy durations. J Appl Oper Res 3:177–191Google Scholar
  40. 40.
    Stroppi LJR, Chiotti O, Villarreal PD (2011) A bpmn 2.0 extension to define the resource perspective of business process models. In: XIV Iberoamerican conference on software engineering, pp 25–38Google Scholar
  41. 41.
    van der Aalst WMP, Schonenberg MH, Song M (2011) Time prediction based on process mining. Inf Syst 36(2):450–475CrossRefGoogle Scholar
  42. 42.
    van Dongen BF, Crooy RA, van der Aalst WMP (2008) Cycle time prediction: when will this case finally be finished? In: Proceedings of CoopIS 2008 5331(I), pp 319–336Google Scholar
  43. 43.
    Weske M (2007) Business process management: concepts, languages, architectures. Springer, BerlinGoogle Scholar
  44. 44.
    Westergaard M, Maggi FM (2012) Looking into the future: using timed automata to provide a priori advice about timed declarative process models. In: International conference on cooperative information systems (CoopIS 2012)Google Scholar
  45. 45.
    Zur Muehlen M (2004) Organizational management in workflow applications-issues and perspectives. Inf Technol Manag 5(3–4):271–291CrossRefGoogle Scholar
  46. 46.
    Zhang Y, Su R, Li Q, Cassandras CG, Xie L (2017) Distributed flight routing and scheduling for air traffic flow management. IEEE Trans Intell Transp Syst 18(99):1–12Google Scholar
  47. 47.
    Zhang X, Margellos K, Goulart P, Lygeros J (2013) Stochastic model predictive control using a combination of randomized and robust optimization. In: 52nd IEEE conference on decision and control, pp 7740–7745Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Depto. Lenguajes y Sistemas InformáticosUniversity of SevilleSevilleSpain
  2. 2.Depto. Ciencias de la Computación e Inteligencia ArtificialUniversity of GranadaGranadaSpain
  3. 3.Technical University of DenmarkLyngbyDenmark

Personalised recommendations