April 2017, 2(2): 177-189. doi: 10.3934/bdia.2017016

An ontological account of flow-control components in BPMN process models

1. 

Information Retrieval and Knowledge Management Research Lab, School of Information Technology, York University, Toronto, ON, Canada

2. 

TD Bank Financial Group, 66 Wellington Street, ON, M5K 1A2, Canada

* Corresponding author: Xing Tan

The first and third authors are supported by NSERC CREATE ADERSIM

Published  April 2017

The Business Process Model and Notation (BPMN) has been widely adopted in the recent years as one of the standard languages for visual description of business processes. BPMN however does not include a formal semantics, which is required for formal verification and validation of behaviors of BPMN models.

Towards bridging this gap using first-order logic, we in this paper present an ontological/formal account of flow-control components in BPMN, using Situation Calculus and Petri nets. More precisely, we use SCOPE (Situation Calculus Ontology of PEtri nets), developed from our previous work, to formally describe flow-control related basic components (i.e., events, tasks, and gateways) in BPMN as SCOPE-based procedures. These components are first mapped from BPMN onto Petri nets.

Our approach differs from other major approaches for assigning semantics to BPMN (e.g., the ones applying communicating sequential processes, or abstract state machines) in the following aspects. Firstly, the approach supports direct application of automated theorem proving for checking theory consistency or verifying dynamical properties of systems. Secondly, it defines concepts through aggregation of more basic concepts in a hierarchical way thus the adoptability and extensibility of the models are presumably high. Thirdly, Petri-net-based implementation is completely encapsulated such that interfaces between the system and its users are defined completely within a BPMN context. Finally, the approach can easily further adopt the concept of time.

Citation: Xing Tan, Yilan Gu, Jimmy Xiangji Huang. An ontological account of flow-control components in BPMN process models. Big Data & Information Analytics, 2017, 2 (2) : 177-189. doi: 10.3934/bdia.2017016
References:
[1]

C. Alvarenga and R. Schoenthaler, A New Take on Supply Chain Event Management, Supply Chain Management Review, 7 (2003), 29-35.

[2]

I. Bratko, PROLOG Programming for Artificial Intelligence, Fourth Edition, AddisonWesley, 2011.

[3]

R.M. DijkmanM. Dumas and C. Ouyang, Formal Semantics and Automated Analysis of BPMN Process Models, Preprint, (2007). doi: 10.1016/j.infsof.2008.02.006.

[4]

R.M. DijkmanM. Dumas and C. Ouyang, Semantics and Analysis of Business Process Models in BPMN, Information and Software Technology, 50 (2008), 1281-1294. doi: 10.1016/j.infsof.2008.02.006.

[5]

Y. Gu, Advanced Reasoning about Dynamical Systems, PhD thesis, University of Toronto, 2010.

[6]

Y. Gu and M. Soutchanski, Decidable Reasoning in a Modified Situation Calculus, in IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6-12,2007, 2007,1891–1897, URL http://ijcai.org/Proceedings/07/Papers/305.pdf.

[7]

Y. Gu and M. Soutchanski, Reasoning About Large Taxonomies of Actions, in Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, AAAI 2008, Chicago, Illinois, USA, July 13-17,2008, 2008,931–937, URL http://www.aaai.org/Library/AAAI/2008/aaai08-148.php.

[8]

I. Horrocks and A. Voronkov, Reasoning Support for Expressive Ontology Languages Using a Theorem Prover, in Foundations of Information and Knowledge Systems, 4th International Symposium, FoIKS 2006, Budapest, Hungary, February 14-17,2006, Proceedings, 2006,201– 218. doi: 10.1007/11663881_12.

[9]

R. LiuA. Kumar and W.M.P. van der Aalst, A Formal Modeling Approach for Supply Chain Event Management, Decision Support Systems, 43 (2007), 761-778. doi: 10.1016/j.dss.2006.12.009.

[10]

J. McCarthy and P. Hayes, Some Philosophical Problems from the Standpoint of Artificial Intelligence, Readings in Artificial Intelligence, (1981), 431-450. doi: 10.1016/B978-0-934613-03-3.50033-7.

[11]

M. Michalowski, S. Wilk, D. Lin, W. Michalowski, X. Tan and S. Mohapatra, Procedural Approach to Mitigating Concurrently Applied Clinical Practice Guidelines, in Proceedings of the First Workshop on Expanding the Boundaries of Health Informatics Using Artificial Intelligence (HIAI13), 2013.

[12]

M. Michalowski, S. Wilk, X. Tan and W. Michalowski, First-Order Logic Theory for Manipulating Clinical Practice Guidelines Applied to Comorbid Patients: A Case Study, in AMIA 2014, American Medical Informatics Association Annual Symposium, Washington DC, USA, November 15-19,2014.

[13]

M. Michalowski, S. Wilk, D. Rosu, M. Kezadri, W. Michalowski and M. Carrier, Expanding a First-Order Logic Mitigation Framework to Handle Multimorbid Patient Preferences, in AMIA 2015, American Medical Informatics Association Annual Symposium, San Francisco, CA, USA, November 14-18,2015.

[14]

T. Murata, Petri Nets: Properties, Analysis and Applications, Proceedings of the IEEE, 77 (1989), 541-580. doi: 10.1109/5.24143.

[15]

OMG, Documents Associated With Business Process Model And Notation (BPMN), Version 2. 0, 2011, URL http://www.omg.org/spec/BPMN/2.0/.

[16] R. Reiter, Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems, MIT Press, Cambridge, MA, USA, 2001.
[17]

X. Tan, SCOPE: A Situation Calculus Ontology of Petri Nets, in 6th International Conference of Formal Ontology in Information Systems, Toronto, Canada, 2010,227–240.

[18]

X. Tan, The Application of Ontologies to Reasoning with Process Modeling Formalisms, PhD thesis, University of Toronto, 2012.

[19]

X. Tan, Go beyond the SCOPE: A Temporal Situation Calculus-based Software Tool for Time Petri Nets, in 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, Dalian, China, 7345 (2012), 134–143. doi: 10.1007/978-3-642-31087-4_15.

[20]

X. Tan, Towards a Formal Representation of Clinical Practice Guidelines for the Treatment of Comorbid Patients, in Seventh IEEE International Conference on Bioinformatics and Biomedicine, Shanghai, China, December 18-21,2013, 2013,578–583. doi: 10.1109/BIBM.2013.6732561.

[21]

X. Tan and G. K. Tayi, An Ontological and Hierarchical Approach for Supply Chain Event Aggregation, in Ninth IEEE International Conference on Semantic Computing, Anaheim, California, USA, 2015, 69–72. doi: 10.1109/ICOSC.2015.7050780.

[22]

X. Tan, X. An, N. Pairaudeau and J. Huang, Towards a Formal Account of the Dynamics of Knowledge and Context in Surgical Rooms for the Practice of Surgical Safety CheckLists, in AMIA 2016, American Medical Informatics Association Annual Symposium, Chicago, IL, USA, November 12-16,2016

[23]

X. Tan, J. Huang and A. An, Ranking Documents Through Stochastic Sampling on Bayesian Network-based Models: A Pilot Study, in SIGIR '16: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, 961–964,2016.

[24]

X. Tan, F. Jiang and J. Huang, On the Effectiveness of Bayesian Network-based Models for Document Ranking, in ICTIR '17: Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval, 309–312,2016.

[25]

W.M.P. van der Aalst, The Application of Petri Nets to Workflow Management, Journal of Circuits, Systems and Computers, 8 (1998), 21-66.

[26]

W.M.P. van der AalstA.H. ter HofstedeB. Kiepuszewski and A.P. Barros, Workflow Patterns, Distributed and Parallel Databases, 14 (2003), 5-51.

[27]

S. Wilk, M. Michalowski, X. Tan, W. Michalowski, Using First-Order Logic to Represent Clinical Practice Guidelines and to Mitigate Adverse Interactions, in 6th International Workshop Knowledge Representation for Health Care at the the Vienna Summer of Logic, 45-61, Vienna, Austria, 2014.

show all references

References:
[1]

C. Alvarenga and R. Schoenthaler, A New Take on Supply Chain Event Management, Supply Chain Management Review, 7 (2003), 29-35.

[2]

I. Bratko, PROLOG Programming for Artificial Intelligence, Fourth Edition, AddisonWesley, 2011.

[3]

R.M. DijkmanM. Dumas and C. Ouyang, Formal Semantics and Automated Analysis of BPMN Process Models, Preprint, (2007). doi: 10.1016/j.infsof.2008.02.006.

[4]

R.M. DijkmanM. Dumas and C. Ouyang, Semantics and Analysis of Business Process Models in BPMN, Information and Software Technology, 50 (2008), 1281-1294. doi: 10.1016/j.infsof.2008.02.006.

[5]

Y. Gu, Advanced Reasoning about Dynamical Systems, PhD thesis, University of Toronto, 2010.

[6]

Y. Gu and M. Soutchanski, Decidable Reasoning in a Modified Situation Calculus, in IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6-12,2007, 2007,1891–1897, URL http://ijcai.org/Proceedings/07/Papers/305.pdf.

[7]

Y. Gu and M. Soutchanski, Reasoning About Large Taxonomies of Actions, in Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, AAAI 2008, Chicago, Illinois, USA, July 13-17,2008, 2008,931–937, URL http://www.aaai.org/Library/AAAI/2008/aaai08-148.php.

[8]

I. Horrocks and A. Voronkov, Reasoning Support for Expressive Ontology Languages Using a Theorem Prover, in Foundations of Information and Knowledge Systems, 4th International Symposium, FoIKS 2006, Budapest, Hungary, February 14-17,2006, Proceedings, 2006,201– 218. doi: 10.1007/11663881_12.

[9]

R. LiuA. Kumar and W.M.P. van der Aalst, A Formal Modeling Approach for Supply Chain Event Management, Decision Support Systems, 43 (2007), 761-778. doi: 10.1016/j.dss.2006.12.009.

[10]

J. McCarthy and P. Hayes, Some Philosophical Problems from the Standpoint of Artificial Intelligence, Readings in Artificial Intelligence, (1981), 431-450. doi: 10.1016/B978-0-934613-03-3.50033-7.

[11]

M. Michalowski, S. Wilk, D. Lin, W. Michalowski, X. Tan and S. Mohapatra, Procedural Approach to Mitigating Concurrently Applied Clinical Practice Guidelines, in Proceedings of the First Workshop on Expanding the Boundaries of Health Informatics Using Artificial Intelligence (HIAI13), 2013.

[12]

M. Michalowski, S. Wilk, X. Tan and W. Michalowski, First-Order Logic Theory for Manipulating Clinical Practice Guidelines Applied to Comorbid Patients: A Case Study, in AMIA 2014, American Medical Informatics Association Annual Symposium, Washington DC, USA, November 15-19,2014.

[13]

M. Michalowski, S. Wilk, D. Rosu, M. Kezadri, W. Michalowski and M. Carrier, Expanding a First-Order Logic Mitigation Framework to Handle Multimorbid Patient Preferences, in AMIA 2015, American Medical Informatics Association Annual Symposium, San Francisco, CA, USA, November 14-18,2015.

[14]

T. Murata, Petri Nets: Properties, Analysis and Applications, Proceedings of the IEEE, 77 (1989), 541-580. doi: 10.1109/5.24143.

[15]

OMG, Documents Associated With Business Process Model And Notation (BPMN), Version 2. 0, 2011, URL http://www.omg.org/spec/BPMN/2.0/.

[16] R. Reiter, Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems, MIT Press, Cambridge, MA, USA, 2001.
[17]

X. Tan, SCOPE: A Situation Calculus Ontology of Petri Nets, in 6th International Conference of Formal Ontology in Information Systems, Toronto, Canada, 2010,227–240.

[18]

X. Tan, The Application of Ontologies to Reasoning with Process Modeling Formalisms, PhD thesis, University of Toronto, 2012.

[19]

X. Tan, Go beyond the SCOPE: A Temporal Situation Calculus-based Software Tool for Time Petri Nets, in 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, Dalian, China, 7345 (2012), 134–143. doi: 10.1007/978-3-642-31087-4_15.

[20]

X. Tan, Towards a Formal Representation of Clinical Practice Guidelines for the Treatment of Comorbid Patients, in Seventh IEEE International Conference on Bioinformatics and Biomedicine, Shanghai, China, December 18-21,2013, 2013,578–583. doi: 10.1109/BIBM.2013.6732561.

[21]

X. Tan and G. K. Tayi, An Ontological and Hierarchical Approach for Supply Chain Event Aggregation, in Ninth IEEE International Conference on Semantic Computing, Anaheim, California, USA, 2015, 69–72. doi: 10.1109/ICOSC.2015.7050780.

[22]

X. Tan, X. An, N. Pairaudeau and J. Huang, Towards a Formal Account of the Dynamics of Knowledge and Context in Surgical Rooms for the Practice of Surgical Safety CheckLists, in AMIA 2016, American Medical Informatics Association Annual Symposium, Chicago, IL, USA, November 12-16,2016

[23]

X. Tan, J. Huang and A. An, Ranking Documents Through Stochastic Sampling on Bayesian Network-based Models: A Pilot Study, in SIGIR '16: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, 961–964,2016.

[24]

X. Tan, F. Jiang and J. Huang, On the Effectiveness of Bayesian Network-based Models for Document Ranking, in ICTIR '17: Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval, 309–312,2016.

[25]

W.M.P. van der Aalst, The Application of Petri Nets to Workflow Management, Journal of Circuits, Systems and Computers, 8 (1998), 21-66.

[26]

W.M.P. van der AalstA.H. ter HofstedeB. Kiepuszewski and A.P. Barros, Workflow Patterns, Distributed and Parallel Databases, 14 (2003), 5-51.

[27]

S. Wilk, M. Michalowski, X. Tan, W. Michalowski, Using First-Order Logic to Represent Clinical Practice Guidelines and to Mitigate Adverse Interactions, in 6th International Workshop Knowledge Representation for Health Care at the the Vienna Summer of Logic, 45-61, Vienna, Austria, 2014.

Figure 2.  An Order Process in BPMN
Figure 3.  A Petri Net for the Order Process (Transformed from BPMN)
Figure 1.  Mapping tasks, events, and gateways onto Petri-net components (Fig. 3. in [4] is copied here)
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