Anti-Alignments - Measuring The Precision of Process Models and Event Logs

Processes are a crucial artefact in organizations, since they coordinate the execution of activities so that products and services are provided. The use of models to analyse the underlying processes is a well-known practice. However, due to the complexity and continuous evolution of their processes, organizations need an effective way of analysing the relation between processes and models. Conformance checking techniques asses the suitability of a process model in representing an underlying process, observed through a collection of real executions. One important metric in conformance checking is to asses the precision of the model with respect to the observed executions, i.e., characterize the ability of the model to produce behavior unrelated to the one observed. In this paper we present the notion of anti-alignment as a concept to help unveiling runs in the model that may deviate significantly from the observed behavior. Using anti-alignments, a new metric for precision is proposed. In contrast to existing metrics, anti-alignment based precision metrics satisfy most of the required axioms highlighted in a recent publication. Moreover, a complexity analysis of the problem of computing anti-alignments is provided, which sheds light into the practicability of using anti-alignment to estimate precision. Experiments are provided that witness the validity of the concepts introduced in this paper.

[1]  Jens Palsberg,et al.  Complexity Results for 1-Safe Nets , 1993, Theor. Comput. Sci..

[2]  Josep Carmona,et al.  Conformance Checking , 2018, Springer International Publishing.

[3]  Sander J. J. Leemans,et al.  Discovering Block-Structured Process Models from Event Logs - A Constructive Approach , 2013, Petri Nets.

[4]  Bart Baesens,et al.  Determining Process Model Precision and Generalization with Weighted Artificial Negative Events , 2014, IEEE Transactions on Knowledge and Data Engineering.

[5]  Joao Marques-Silva,et al.  PySAT: A Python Toolkit for Prototyping with SAT Oracles , 2018, SAT.

[6]  Massimo Mecella,et al.  Automated Discovery of Process Models from Event Logs: Review and Benchmark , 2017, IEEE Transactions on Knowledge and Data Engineering.

[7]  A Arya Adriansyah,et al.  Aligning observed and modeled behavior , 2014 .

[8]  Sander J. J. Leemans,et al.  Scalable process discovery and conformance checking , 2016, Software & Systems Modeling.

[9]  Marlon Dumas,et al.  Abstract-and-Compare: A Family of Scalable Precision Measures for Automated Process Discovery , 2018, BPM.

[10]  Mathias Weske,et al.  Efficient Consistency Measurement Based on Behavioral Profiles of Process Models , 2011, IEEE Transactions on Software Engineering.

[11]  Matthias Weidlich,et al.  The 4C Spectrum of Fundamental Behavioral Relations for Concurrent Systems , 2014, Petri Nets.

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

[13]  Boudewijn F. van Dongen,et al.  Measuring precision of modeled behavior , 2015, Inf. Syst. E Bus. Manag..

[14]  Marlon Dumas,et al.  Split Miner: Discovering Accurate and Simple Business Process Models from Event Logs , 2017, 2017 IEEE International Conference on Data Mining (ICDM).

[15]  Jorge Munoz-Gama,et al.  Conformance Checking and Diagnosis in Process Mining , 2016, Lecture Notes in Business Information Processing.

[16]  Boudewijn F. van Dongen,et al.  Quality Dimensions in Process Discovery: The Importance of Fitness, Precision, Generalization and Simplicity , 2014, Int. J. Cooperative Inf. Syst..

[17]  G. S. Tseitin.  On the Complexity of Derivation in Propositional Calculus , 1983 .

[18]  Michaël Thomazo,et al.  Complexity of universality and related problems for partially ordered NFAs , 2016, Inf. Comput..

[19]  C. Humby,et al.  Process Mining: Data science in Action , 2014 .

[20]  Niklas Sörensson,et al.  An Extensible SAT-solver , 2003, SAT.

[21]  Josep Carmona,et al.  Conformance Checking - Relating Processes and Models , 2018 .

[22]  Marlon Dumas,et al.  Behavioral Comparison of Process Models Based on Canonically Reduced Event Structures , 2014, BPM.

[23]  Boudewijn F. van Dongen,et al.  ProM: The Process Mining Toolkit , 2009, BPM.

[24]  Josep Carmona,et al.  A Unified Approach for Measuring Precision and Generalization Based on Anti-alignments , 2016, BPM.

[25]  Boudewijn F. van Dongen,et al.  ProM 6: The Process Mining Toolkit , 2010, BPM.

[26]  A Anne Rozinat,et al.  Process mining : conformance and extension , 2010 .

[27]  Remco M. Dijkman.  Diagnosing Differences between Business Process Models , 2008, BPM.

[28]  Alessandro Berti,et al.  Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Science , 2019, ArXiv.

[29]  Dirk Fahland,et al.  The Imprecisions of Precision Measures in Process Mining , 2017, Inf. Process. Lett..

[30]  Slawomir Lasota,et al.  The Reachability Problem for Petri Nets Is Not Elementary , 2018, J. ACM.

[31]  Josep Carmona,et al.  Anti-alignments in Conformance Checking - The Dark Side of Process Models , 2016, Petri Nets.

[32]  Tadao Murata,et al.  Petri nets: Properties, analysis and applications , 1989, Proc. IEEE.

[33]  Josep Carmona,et al.  Event-Based Real-Time Decomposed Conformance Analysis , 2014, OTM Conferences.

[34]  Moe Thandar Wynn,et al.  Estimating completeness of event logs , 2012 .

[35]  Iain A. Stewart.  Reachability in Some Classes of Acyclic Petri Nets , 1995, Fundam. Informaticae.

[36]  Mathias Weske,et al.  Causal Behavioural Profiles - Efficient Computation, Applications, and Evaluation , 2011, Fundam. Informaticae.