Do activity lifecycles affect the validity of a business rule in a business process?

Traditional process mining techniques offer limited possibilities to analyze business processes working in low-predictable and dynamic environments. Recently, to close this gap, declarative process models have been introduced to represent process mining results since they allow for describing complex behaviors as a compact set of business rules. However, in this context, activities of a business process are still considered as atomic/instantaneous events. This is a strong limitation for these approaches because often, in realistic environments, process activities are not instantaneous but executed across a time interval and pass through a sequence of states of a lifecycle. This paper investigates how the existing techniques for the discovery of declarative process models can be adapted when the business process under analysis contains non-atomic activities. In particular, we base our proposed approach on the use of discriminative rule mining to determine how the characteristics of the activity lifecycles in a business process influence the validity of a business rule in that process. The approach has been implemented as a plug-in of the process mining tool ProM and validated on synthetic logs and on a real-life log recorded by an incident and problem management system called VINST in use at Volvo IT Belgium. HighlightsWe present an approach to discover declarative specifications from logs.The approach has a strong focus on activity lifecycles.The lifecycle identification can be FIFO-based or event correlation based.The approach is implemented as a ProM plug-in.The approach has been validated on synthetic logs and on a real-life log.

[1]  Alessandro Sperduti,et al.  Online Discovery of Declarative Process Models from Event Streams , 2015, IEEE Transactions on Services Computing.

[2]  Massimo Mecella,et al.  On the Discovery of Declarative Control Flows for Artful Processes , 2015, ACM Trans. Manag. Inf. Syst..

[3]  Wil M. P. van der Aalst,et al.  Declarative workflows: Balancing between flexibility and support , 2009, Computer Science - Research and Development.

[4]  Matthew Richardson,et al.  Markov logic networks , 2006, Machine Learning.

[5]  Fabrizio Maria Maggi Declarative Process Mining with the Declare Component of ProM , 2013, BPM.

[6]  Jan Mendling,et al.  Discovering Target-Branched Declare Constraints , 2014, BPM.

[7]  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).

[8]  Evelina Lamma,et al.  Applying Inductive Logic Programming to Process Mining , 2007, ILP.

[9]  Marco Montali,et al.  Monitoring data-aware business constraints with finite state automata , 2014, ICSSP 2014.

[10]  David G. Stork,et al.  Pattern Classification , 1973 .

[11]  Massimo Mecella,et al.  A two-step fast algorithm for the automated discovery of declarative workflows , 2013, 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[12]  Fabrizio Maria Maggi,et al.  Looking into the Future. Using Timed Automata to Provide a Priori Advice about Timed Declarative Process Models , 2012, OTM Conferences.

[13]  Evelina Lamma,et al.  Probabilistic Declarative Process Mining , 2010, KSEM.

[14]  Marco Montali,et al.  Discovering Data-Aware Declarative Process Models from Event Logs , 2013, BPM.

[15]  Jianmin Wang,et al.  Mining process models with non-free-choice constructs , 2007, Data Mining and Knowledge Discovery.

[16]  Robert I. Jennrich,et al.  Newton-Raphson and Related Algorithms for Maximum Likelihood Variance Component Estimation , 1976 .

[17]  Hong Cheng,et al.  Mining closed discriminative dyadic sequential patterns , 2011, EDBT/ICDT '11.

[18]  Wynne Hsu,et al.  Integrating Classification and Association Rule Mining , 1998, KDD.

[19]  Evelina Lamma,et al.  Inducing Declarative Logic-Based Models from Labeled Traces , 2007, BPM.

[20]  Fabrizio Maria Maggi,et al.  Discovering cross-organizational business rules from the cloud , 2014, 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[21]  Wil M. P. van der Aalst,et al.  Process Mining - Discovery, Conformance and Enhancement of Business Processes , 2011 .

[22]  Wil M. P. van der Aalst,et al.  Efficient Discovery of Understandable Declarative Process Models from Event Logs , 2012, CAiSE.

[23]  Ning Chen,et al.  Mining explicit rules for software process evaluation , 2013, ICSSP.

[24]  Jian Pei,et al.  CMAR: accurate and efficient classification based on multiple class-association rules , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[25]  Alexander Artikis,et al.  Run-time composite event recognition , 2012, DEBS.

[26]  Wil M. P. van der Aalst Process mining , 2012, CACM.

[27]  Nicklas Holmberg,et al.  Perspectives in Business Informatics Research: 13th International Conference, BIR 2014, Lund, Sweden, September 22-24, 2014, Proceedings , 2014 .

[28]  Jan Mendling,et al.  Log-Based Understanding of Business Processes through Temporal Logic Query Checking , 2014, OTM Conferences.

[29]  Alessandro Sperduti,et al.  Techniques for a Posteriori Analysis of Declarative Processes , 2012, 2012 IEEE 16th International Enterprise Distributed Object Computing Conference.

[30]  Boudewijn F. van Dongen,et al.  Process mining: a two-step approach to balance between underfitting and overfitting , 2008, Software & Systems Modeling.

[31]  Dimitrios Gunopulos,et al.  Mining Process Models from Workflow Logs , 1998, EDBT.

[32]  Wil M. P. van der Aalst,et al.  Workflow mining: discovering process models from event logs , 2004, IEEE Transactions on Knowledge and Data Engineering.

[33]  Wil M. P. van der Aalst,et al.  A Knowledge-Based Integrated Approach for Discovering and Repairing Declare Maps , 2013, CAiSE.

[34]  Wil M. P. van der Aalst,et al.  Enhancing Declare Maps Based on Event Correlations , 2013, BPM.

[35]  Orna Kupferman,et al.  Vacuity detection in temporal model checking , 2003, International Journal on Software Tools for Technology Transfer.

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

[37]  Paola Mello,et al.  Declarative specification and verification of service choreographiess , 2010, TWEB.

[38]  R. P. Jagadeesh Chandra Bose,et al.  Process mining in the large : preprocessing, discovery, and diagnostics , 2012 .

[39]  Wil M. P. van der Aalst,et al.  Genetic process mining: an experimental evaluation , 2007, Data Mining and Knowledge Discovery.

[40]  LammaEvelina,et al.  Verifiable agent interaction in abductive logic programming , 2008 .

[41]  Marlon Dumas,et al.  Correlation Patterns in Service-Oriented Architectures , 2007, FASE.

[42]  Paola Mello,et al.  Monitoring business constraints with the event calculus , 2013, ACM Trans. Intell. Syst. Technol..

[43]  Federico Chesani,et al.  Verification of Choreographies During Execution Using the Reactive Event Calculus , 2008, WS-FM.

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

[45]  Dimitrios Gunopulos,et al.  Self-adaptive event recognition for intelligent transport management , 2013, 2013 IEEE International Conference on Big Data.

[46]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[47]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[48]  Paola Mello,et al.  Towards data-aware constraints in declare , 2013, SAC '13.

[49]  Alessandro Sperduti,et al.  Heuristics Miner for Time Intervals , 2010, ESANN.

[50]  Evelina Lamma,et al.  Probabilistic Logic-Based Process Mining , 2010, CILC.

[51]  Claudio Di Ciccio,et al.  Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches , 2015, Journal on Data Semantics.

[52]  Jiawei Han,et al.  Classification of software behaviors for failure detection: a discriminative pattern mining approach , 2009, KDD.

[53]  Wil M. P. van der Aalst,et al.  User-guided discovery of declarative process models , 2011, 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[54]  Wil M. P. van der Aalst,et al.  Rediscovering workflow models from event-based data using little thumb , 2003, Integr. Comput. Aided Eng..