Validating and Enhancing Declarative Business Process Models Based on Allowed and Non-occurring Past Behavior

Contemporary organizations have been implementing a wide variety of process-aware information systems in order to streamline their operations. The current organizational environment is often characterized by a multitude of internal and external directives which impose restrictions through business rules on the operations and as such define declarative business process models. We present a twofold methodology which can be applied towards the validation and enhancement of process models which are expressed in a declarative form in order to improve their correctness and completeness. Our approach is based on validation of real-life behavior using rule property checking, and on allowed behavior by the process model which was not encountered in real-life cases by matching rule-generated rejected activity occurrences with absent behavior in the event log. Our methodology retains the ability to correspond retrieved findings to decision-makers in a clear and comprehensible manner (i.e. in the form of a new rule), rather than a formal revision of an implemented procedural model, which is a significant advantage when considering business-IT alignment concerns.

[1]  Wil M. P. van der Aalst,et al.  Conformance checking of processes based on monitoring real behavior , 2008, Inf. Syst..

[2]  Boudewijn F. van Dongen,et al.  Business process mining: An industrial application , 2007, Inf. Syst..

[3]  Wil M. P. van der Aalst,et al.  Transactions on Petri Nets and Other Models of Concurrency II, Special Issue on Concurrency in Process-Aware Information Systems , 2009, Trans. Petri Nets and Other Models of Concurrency.

[4]  Bart Baesens,et al.  Robust Process Discovery with Artificial Negative Events , 2009, J. Mach. Learn. Res..

[5]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.

[6]  Wil M. P. van der Aalst,et al.  DECLARE: Full Support for Loosely-Structured Processes , 2007, 11th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2007).

[7]  Bart Baesens,et al.  Improved Artificial Negative Event Generation to Enhance Process Event Logs , 2012, CAiSE.

[8]  Michael Rosemann,et al.  Handbook on Business Process Management 2, Strategic Alignment, Governance, People and Culture, 2nd Ed , 2010, International Handbooks on Information Systems.

[9]  Evelina Lamma,et al.  Exploiting Inductive Logic Programming Techniques for Declarative Process Mining , 2009, Trans. Petri Nets Other Model. Concurr..

[10]  Evelina Lamma,et al.  Verifiable agent interaction in abductive logic programming: The SCIFF framework , 2008, TOCL.

[11]  Wil M. P. van der Aalst,et al.  A Declarative Approach for Flexible Business Processes Management , 2006, Business Process Management Workshops.

[12]  Edmund M. Clarke,et al.  Model Checking , 1999, Handbook of Automated Reasoning.

[13]  Boudewijn F. van Dongen,et al.  Process Mining and Verification of Properties: An Approach Based on Temporal Logic , 2005, OTM Conferences.

[14]  Wil M. P. van der Aalst,et al.  A Rule-Based Approach for Process Discovery: Dealing with Noise and Imbalance in Process Logs , 2005, Data Mining and Knowledge Discovery.

[15]  Wil M. P. van der Aalst,et al.  Conformance Testing: Measuring the Fit and Appropriateness of Event Logs and Process Models , 2005, Business Process Management Workshops.

[16]  William W. Cohen Fast Effective Rule Induction , 1995, ICML.