Kökten Ulaş BİRANT, İsmail YÜREK, Derya BİRANT

Interactive process miner: a new approach for process mining

Process mining is a technique for extracting knowledge from event logs recorded by an information system. In the process discovery phase of process mining, a process model is constructed to represent the business processes systematically and to give a general opinion about the progressive of processes in the event log. The constructed process model can be very complex as a result of structured and unstructured processes recorded in real life. Previous studies proposed different approaches to filter or eliminate some processes from the model to simplify it by implementing some statistical or mathematical formulas rather than user interactions. The main objective of this study is to develop an algorithm that is capable of working on large volume of event logs and handling the execution records of running process instances to analyze the execution of processes. The other significant principle is to provide an interactive method to ensure the decisions that will be taken to improve the execution of processes by verifying in a simulation environment before being put into practice. This study proposes a novel algorithm, named interactive process miner, to create a process model based on event logs and a new approach that contains three different features, including activity deletion, aggregation, and addition operations on the existing process model. The experimental results show a fundamental improvement in performance compared to the existing algorithms. As a result of this study, users will have an opportunity to analyze a large volume of event logs in a short time with low memory usage and to modify the created process model to observe the impact of improvement changes in a simulation environment before applying any changes to a system in real life.

PDF

___

Mannhardt F, de Leoni M, Reijers HA. The multi-perspective process explorer. In: Demo Session of the 13th International Conference on Business Process Management; 31 August–3 September 2015; Innsbruck, Austria. CEUR Workshop Proceedings. pp. 130-134.
Bose RPJC, van der Aalst WMP. Trace alignment in process mining: opportunities for process diagnostics. In: 8th International Conference on Business Process Management; 13–16 September 2010; Hoboken, NJ, USA. Heidelberg, Germany: Springer. pp. 227-242.
van Dongen BF. Real-life event logs - Hospital log. Eindhoven University of Technology Dataset 2011. doi: 10.4121/uuid:270fd440-1057-4fb9-89a9-b699b47990f5
de Leoni M, Mannhardt F. Road traffic fine management process. Eindhoven University of Technology Dataset 2015. doi: 10.4121/uuid:270fd440-1057-4fb9-89a9-b699b47990f5
Alizadeh M, Lu X, Fahland D, Zannone N, van der Aalst WMP. Linking data and process perspectives for conformance analysis. Comput Secur 2018; 73: 172-193.
Bolt A, de Leoni M, ter Hofstedea AHM, van der Aalst WMP. Process variant comparison: using event logs to detect differences in behavior and business rules. Inform Syst 2017; 0: 1-14.
Mitsyuk AA, Shugurov IS, Kalenkova AA, van der Aalst WMP. Generating event logs for high-level process models. Simul Model Pract Th 2017; 74: 1-16.
Suriadi S, Andrewsa R, ter Hofstedea AHM, Wynna MT. Event log imperfection patterns for process mining: Towards a systematic approach to cleaning event logs. Inform Syst 2017; 64: 132-1564.
Bolt A, de Leoni M, van der Aalst WMP. Scientific workflows for process mining: building blocks, scenarios, and implementation. Int J Softw Tools Te 2016; 18: 607-628.
Taxa N, Sidorovaa, N, Haakmab R, van der Aalst WMP. Mining local process models. Journal of Innovation in Digital Ecosystems 2016; 3: 183-196.
Pika A, van der Aalst WMP, Wynn MT, Fidge CJ, ter Hofstede AHM. Evaluating and predicting overall process risk using event logs. Inform Sciences 2016; 352-353: 98-120.
Mannhardt F, de Leoni M, Reijers HA, van der Aalst WMP. Balanced multi-perspective checking of process conformance. Computing 2016; 98: 407-437.
de Leoni M, van der Aalst WMP, Dees M. A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Inform Syst 2016; 56: 235-257.
Rovani M, Maggi FM, de Leoni M, van der Aalst WMP. Declarative process mining in healthcare. Expert Syst Appl 2015; 42: 9236-9251.
Fahland D, van der Aalst WMP. Model repair — aligning process models to reality. Inform Syst 2015; 47: 220-243.
Cheng HJ, Kumar A. Process mining on noisy logs — Can log sanitization help to improve performance? Decis Support Syst 2015; 79: 138-149.
Aleem S, Capretz LF, Ahmed F. Business process mining approaches: a relative comparison. International Journal of Science, Technology and Management 2015; 4: 1557-1564.
Fahland D, van der Aalst WMP. Simplifying discovered process models in a controlled manner. Inform Syst 2013;38: 585-605.
van Dongen BF, de Medeiros AKA, Verbeek HMW, Weijters AJMM, van der Aalst WMP. The ProM framework: a new era in process mining tool support. In: 26th International Conference on Application and Theory of Petri Nets; 20–25 June 2005; Miami, USA. Heidelberg, Germany: Springer. pp. 444-454.
van Dongen BF, van der Aalst WMP. EMiT: A process mining tool. In: 25th International Conference on Application and Theory of Petri Nets; 21–25 June 2004; Bologna, Italy. Heidelberg, Germany: Springer. pp. 454-463.
van der Aalst WMP. Process mining: overview and opportunities. ACM Transactions on Management Information Systems 2012; 3: 1-17.
van der Aalst WMP, Schonenberg MH, Song M. Time Prediction Based on Process Mining. Inform Syst 2011; 36:450-475.
Luengo D, Sepulveda M. Applying clustering in process mining to different versions of business process that changes over time. In: BPM 2011 International Workshops; 29 August 2012; Clermont-Ferrand, France. Heidelberg,Germany: Springer. pp. 153-158
Bose RPJC, van der Aalst WMP, Zliobait˙e I, Pechenizkiy M. Handling concept drift in process mining. In: 23rd ˇ International Conference on Advanced Information Systems Engineering; 20–24 June 2011; London, UK. Heidelberg, Germany: Springer. pp. 391-405.
Song M, Gunther CW, van der Aalst WMP. Trace clustering in process mining. In: BPM 2008 International Workshops; 1–4 September 2008; Milan, Italy. Heidelberg, Germany: Springer. pp. 109-120.
Song W, Liu S, Liu Q. Business process mining based on simulated annealing. In: 9th International Conference for Young Computer Scientists; 18–21 November 2008; Hunan, China. New York, NY, USA: IEEE. pp. 135-139.
Bratosin C, Sidorova N, van der Aalst WMP. Distributed genetic process mining. In: IEEE Congress on Evolutionary Computation; 18–23 July 2010; Barcelona, Spain. New York, NY, USA: IEEE. pp. 1-8.
de Medeiros AKA, Weijters AJMM, van der Aalst WPM. Genetic process mining: an experimental evaluation.Data Min Knowl Disc 2007; 14: 245-304.
de Medeiros AKA, Weijters AJM, van der Aalst WPM. Genetic process mining: a basic approach and its challenges. In: BPM 2005 International Workshops; 5 September 2005; Nancy, France. Heidelberg, Germany: Springer. pp. 203-215.
Weijters AJMM, van der Aalst WMP. Rediscovering workflow models from event-based data using little thumb. Integr Comput-Aid E 2003; 10: 151-162.
van der Aalst WMP, Weijters T, Maruster L. Workflow mining: discovering process models from event logs. IEEE T Knowl Data E 2004; 16: 1128-1128.
Günther CW, van der Aalst WMP. Fuzzy Mining: Adaptive process simplification based on multi-perspective metrics. In: 5th International Conference on Business Process Management; 24–28 September 2007; Brisbane, Australia. Heidelberg, Germany: Springer. pp. 328-343.
Weijters AJMM, van der Aalst WMP, de Medeiros AKA. Process mining with the heuristics miner-algorithm. Technische Universiteit Eindhoven Technical Report WP 2006; 166: 1-34.
Wainer J, Kim K, Ellis CA. A workflow mining method through model rewriting. In: 11th International Workshop on Groupware; 25–29 September; Porto de Galinhas, Brazil. Heidelberg, Germany: Springer, 2005. pp. 184-191.
van Dongen BF, van der Aalst WMP. A Meta model for process mining data. In: 17th Conference on Advanced Information Systems Engineering (EMOI-INTEROP Workshop); 13–17 June 2005; Porto, Portugal: FEUP edi¸cöes. pp. 309-320.
Schimm G. Mining exact models of concurrent workflows. Comput Ind 2004; 53: 265-281.
Herbst J, Karagiannis D. Workflow mining with InWoLvE. Comput Ind 2004; 53: 245-264.
Cook JE, Du Z, Liu C, Wolf AL. Discovering models of behavior for concurrent workflows. Comput Ind 2004; 53: 297-319.
van der Aalst WMP, Weijters T, Maruster L. Workflow mining: discovering process models from event logs. IEEE T Knowl Data En 2004; 16: 1128-1142.
Cook JE, Wolf AL. Discovering models of software processes from event-based data. ACM T Softw Eng Meth 1998;7: 215-249.
Agrawal R, Gunopulos D, and Leymann F. Mining process models from workflow logs. In: 6th International Conference on Extending Database Technology; 23–27 March 1998; Valencia, Spain. Heidelberg, Germany: Springer. pp. 467-483.