Parallel algorithms for the automated discovery of declarative process models

Information Systems

Volumn 74, Issue , 2018, Pages 136-152

  Maggi, Fabrizio Maria ^a   Di Ciccio, Claudio ^b   Di Francescomarino, Chiara ^c   Kala, Taavi ^a  

^a UNIVERSITY OF TARTU   (Estonia)

^b VIENNA UNIVERSITY OF ECONOMICS AND BUSINESS   (Austria)

^c ITC IRST   (Italy)

Author keywords

Apriori algorithm; Declarative process models; Process discovery; Process mining; Sequence analysis

Indexed keywords

DATA MINING; LEARNING ALGORITHMS; MINERS;

APRIORI ALGORITHMS; DECLARATIVE PROCESS MODELS; PROCESS DISCOVERY; PROCESS MINING; SEQUENCE ANALYSIS;

MODELING LANGUAGES;

EID: 85039726087     PISSN: 03064379     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.is.2017.12.002     Document Type: Article

References (44)

1. van der Aalst, W.M.P., Process Mining: Discovery, Conformance and Enhancement of Business Processes. 2011, Springer.
2. Zugal, S., Pinggera, J., Weber, B., The impact of testcases on the maintainability of declarative process models. BMMDS/EMMSAD, 2011, 163–177.
3. Pichler, P., Weber, B., Zugal, S., Pinggera, J., Mendling, J., Reijers, H.A., Imperative versus declarative process modeling languages: an empirical investigation. BPM Workshops, 2011, 383–394.
4. Reijers, H.A., Slaats, T., Stahl, C., Declarative modeling—an academic dream or the future for BPM?. BPM 2013, 8094, 2013, 307–322.
5. Pesic, M., Constraint-Based Workflow Management Systems: Shifting Control to Users, Ph.D. thesis, 2008, TU/e.
6. Maggi, F.M., Bose, R.P.J.C., van der Aalst, W.M.P., Efficient discovery of understandable declarative process models from event logs. CAiSE, 2012, 270–285.
7. Agrawal, R., Srikant, R., Fast algorithms for mining association rules in large databases. VLDB, 1994, Morgan Kaufmann, 487–499.
8. Kala, T., Maggi, F.M., Di Ciccio, C., Di Francescomarino, C., Apriori and sequence analysis for discovering declarative process models. 20th IEEE International Enterprise Distributed Object Computing Conference, (EDOC 2016), 2016, Vienna, Austria, September 5–9, 2016, 1–9.
9. Adamo, J., Data Mining for Association Rules and Sequential Patterns - Sequential and Parallel Algorithms. 2001, Springer New York, 10.1007/978-1-4613-0085-4.
10. van der Aalst, W.M.P., et al. Process mining manifesto. BPM 2011 Workshops, Part I, 99, 2012, Springer-Verlag, 169–194.
11. Montali, M., Pesic, M., van der Aalst, W.M.P., Chesani, F., Mello, P., Storari, S., Declarative specification and verification of service choreographies. ACM Trans. Web, 4(1), 2010.
12. van der Aalst, W.M.P., Pesic, M., Schonenberg, H., Declarative workflows: balancing between flexibility and support. Comput. Sci. - R&D 23:2 (2009), 99–113.
13. Kupferman, O., Vardi, M.Y., Vacuity detection in temporal model checking. CHARME 1999, 1703, 1999, 82–96.
14. Burattin, A., Maggi, F.M., van der Aalst, W.M.P., Sperduti, A., Techniques for a posteriori analysis of declarative processes. EDOC 2012, 2012, 41–50.
15. Maggi, F.M., Montali, M., Di Ciccio, C., Mendling, J., Semantical vacuity detection in declarative process mining. BPM, 2016, 158–175.
16. Schunselaar, D.M.M., Maggi, F.M., Sidorova, N., Patterns for a log-based strengthening of declarative compliance models. Integrated Formal Methods - 9th International Conference, (IFM 2012), 2012, Pisa, Italy, June 18–21, 2012. Proceedings, 327–342, 10.1007/978-3-642-30729-4_23.
17. W. Steeman, Real-life event logs – an incident management process: closed problems, 2013, (Third International Business Process Intelligence Challenge (BPIC’13)). 10.4121/uuid:500573e6-accc-4b0c-9576-aa5468b10cee.
18. Di Ciccio, C., Bernardi, M.L., Cimitile, M., Maggi, F.M., Generating event logs through the simulation of Declare models. EOMAS, 2015.
19. Di Ciccio, C., Mecella, M., Mendling, J., The effect of noise on mined declarative constraints. Data-Driven Process Discovery and Analysis, 203, 2015, 1–24, 10.1007/978-3-662-46436-6_1.
20. F. Mannhardt, Real-life event logs – sepsis cases, 2016, 10.4121/uuid:915d2bfb-7e84-49ad-a286-dc35f063a460.
21. B.F. van Dongen, Real-life event logs – a loan application process, 2012, (Second International Business Process Intelligence Challenge (BPIC’12)). 10.4121/uuid:3926db30-f712-4394-aebc-75976070e91f.
22. B.F. van Dongen, Bpi challenge 2017, 2017, 10.4121/uuid:5f3067df-f10b-45da-b98b-86ae4c7a310b.
23. B.F. van Dongen, Real-life event logs – logs from ITIL processes of rabobank group ICT, 2014, (Fourth International Business Process Intelligence Challenge (BPIC’14)). 10.4121/uuid:c3e5d162-0cfd-4bb0-bd82-af5268819c35.
24. M. de Leoni, F. Mannhardt, Road traffic fine management process, 2015, 10.4121/uuid:270fd440-1057-4fb9-89a9-b699b47990f5.
25. Mannhardt, F., Blinde, D., Analyzing the trajectories of patients with sepsis using process mining. Gulden, J., Nurcan, S., Reinhartz-Berger, I., Guédria, W., Bera, P., Guerreiro, S., Fellmann, M., Weidlich, M., (eds.) RADAR+EMISA CEUR Workshop Proceedings, 1859, 2017, CEUR-ws.org, 72–80.
26. Di Ciccio, C., Marrella, A., Russo, A., Knowledge-intensive processes: characteristics, requirements and analysis of contemporary approaches. J. Data Semant. 4:1 (2015), 29–57, 10.1007/s13740-014-0038-4.
27. Fahland, D., Lübke, D., Mendling, J., Reijers, H.A., Weber, B., Weidlich, M., Zugal, S., Declarative versus imperative process modeling languages: the issue of understandability. Halpin, T.A., Krogstie, J., Nurcan, S., Proper, E., Schmidt, R., Soffer, P., Ukor, R., (eds.) BMMDS/EMMSAD Lecture Notes in Business Information Processing, 29, 2009, Springer, 353–366, 10.1007/978-3-642-01862-6_29.
28. Bellodi, E., Riguzzi, F., Lamma, E., Probabilistic declarative process mining. KSEM 2010, 6291, 2010, 292–303.
29. Bellodi, E., Riguzzi, F., Lamma, E., Probabilistic logic-based process mining. CILC2010, 598, 2010.
30. Chesani, F., Lamma, E., Mello, P., Montali, M., Riguzzi, F., Storari, S., Exploiting inductive logic programming techniques for declarative process mining. ToPNoC II, 5460, 2009, 278–295.
31. Di Ciccio, C., Mecella, M., Mining artful processes from knowledge workers’ emails. IEEE Internet Comput. 17:5 (2013), 10–20, 10.1109/MIC.2013.60.
32. Di Ciccio, C., Mecella, M., On the discovery of declarative control flows for artful processes. ACM Trans. Manag. Inf. Syst. 5:4 (2015), 24:1–24:37, 10.1145/2629447.
33. Di Ciccio, C., Maggi, F.M., Mendling, J., Discovering target-branched declare constraints. Proceedings of the 12th International Conference on Business Process Management - (BPM 2014), 2014, Haifa, Israel, September 7–11, 2014., 34–50.
34. Di Ciccio, C., Maggi, F.M., Mendling, J., Efficient discovery of target-branched declare constraints. Inf. Syst. 56 (2016), 258–283.
35. Bernardi, M.L., Cimitile, M., Di Francescomarino, C., Maggi, F.M., Using discriminative rule mining to discover declarative process models with non-atomic activities. Rules on the Web. From Theory to Applications - 8th International Symposium, RuleML 2014, Co-located with the 21st European Conference on Artificial Intelligence, (ECAI 2014 Proceedings, 2014, Prague, Czech Republic, August 18–20, 2014., 281–295.
36. Bernardi, M.L., Cimitile, M., Di Francescomarino, C., Maggi, F.M., Do activity lifecycles affect the validity of a business rule in a business process?. Inf. Syst. 62 (2016), 42–59.
37. Westergaard, M., Maggi, F.M., Looking into the future. using timed automata to provide a priori advice about timed declarative process models. On the Move to Meaningful Internet Systems: OTM 2012, Confederated International Conferences: CoopIS, DOA-SVI, and (ODBASE 2012 Proceedings, Part I), 2012, Rome, Italy, September 10–14, 2012., 250–267.
38. Maggi, F.M., Westergaard, M., Using timed automata for a priori warnings and planning for timed declarative process models. Int. J. Cooperative Inf. Syst., 23(1), 2014.
39. Maggi, F.M., Discovering metric temporal business constraints from event logs. Proceedings of the 13th International Conference on Perspectives in Business Informatics Research - (BIR 2014), 2014, Lund, Sweden, September 22–24, 2014., 261–275.
40. Maggi, F.M., Dumas, M., García-Bañuelos, L., Montali, M., Discovering data-aware declarative process models from event logs. BPM, 2013, 81–96.
41. Schönig, S., Rogge-Solti, A., Cabanillas, C., Jablonski, S., Mendling, J., Efficient and customisable declarative process mining with SQL. Proceedings of the 28th International Conference on Advanced Information Systems Engineering - (CAiSE 2016), 2016, Ljubljana, Slovenia, June 13–17, 2016., 290–305.
42. Schönig, S., Di Ciccio, C., Maggi, F.M., Mendling, J., Discovery of multi-perspective declarative process models. Proceedings of the 14th International Conference on Service-Oriented Computing - (ICSOC 2016), 2016, Banff, AB, Canada, October 10–13, 2016, 87–103.
43. Maggi, F.M., Burattin, A., Cimitile, M., Sperduti, A., Online process discovery to detect concept drifts in Ltl-based declarative process models. OTM 2013 Conferences, 8185, 2013, 94–111.
44. Burattin, A., Cimitile, M., Maggi, F.M., Sperduti, A., Online discovery of declarative process models from event streams. IEEE Trans. Serv. Comput. 8:6 (2015), 833–846.