Decomposed Process Mining: The ILP Case

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Presentation transcript:

Decomposed Process Mining: The ILP Case Eric Verbeek and Wil van der Aalst

A Problem / department of mathematics and computer science 24-11-2018

A Solution / department of mathematics and computer science 24-11-2018

Regular Discovery / department of mathematics and computer science 24-11-2018

Decomposed Discovery: Divide / department of mathematics and computer science 24-11-2018

Decomposed Discovery: Conquer / department of mathematics and computer science 24-11-2018

Regular Replay / department of mathematics and computer science 24-11-2018

Decomposed Replay: Divide / department of mathematics and computer science 24-11-2018

Decomposed Replay: Conquer / department of mathematics and computer science 24-11-2018

Example Model (Accepting Petri Net) / department of mathematics and computer science 24-11-2018

Example Event (Activity) Log / department of mathematics and computer science 24-11-2018

Divide and Conquer Framework / department of mathematics and computer science 24-11-2018

Divide and Conquer Framework See http://www.promtools.org/ / department of mathematics and computer science 24-11-2018

Decomposed ILP Discovery Algorithm / department of mathematics and computer science 24-11-2018

Filter (First Cluster) Filter In/Out Replace / name of department 24-11-2018

Decomposed ILP Discovery Algorithm / department of mathematics and computer science 24-11-2018

Discovered Model / department of mathematics and computer science 24-11-2018

Decomposed ILP Replay Algorithm / department of mathematics and computer science 24-11-2018

Strategy (Third Cluster) Filter Replace / department of mathematics and computer science 24-11-2018

Decomposed ILP Replay Algorithm / department of mathematics and computer science 24-11-2018

Replay Cost Factor Implementation issue: Solution: The ILP-based replayer takes integer costs. If an activity occurs in, say, 3 clusters, then the replay costs of this activity in a single cluster should be a third of the usual replay costs in the entire model. Solution: Take the greatest common divisor of all activity cluster counts, multiply all replay costs by that factor, and later on divide all replay costs by this factor again. / department of mathematics and computer science 24-11-2018

Decomposed ILP Replay Algorithm / department of mathematics and computer science 24-11-2018

Case Study Setting Mode Event log based on BPI Challenge 2012 log Model discovered in earlier work Event log aligned on discovered model / department of mathematics and computer science 24-11-2018

Case Study Model / department of mathematics and computer science 24-11-2018

Case Study Results / department of mathematics and computer science 24-11-2018

Conclusions General framework for decomposed process mining Objects with imports, exports, and visualizers Accepting Petri Net Causal Activity Matrix Causal Activity Graph Activity Cluster Array Event Log Array Accepting Petri Net Array Log Alignment Log Alignment Array Many algorithms / department of mathematics and computer science 24-11-2018

Conclusions ILP-based decomposed discovery and ILP-based decomposed replay Discovery can result in the same model in a fraction of the time Replay can result in less costs in less time (trade-off) / department of mathematics and computer science 24-11-2018

Future Work Non-maximal decompositions Grouping maximally-decomposed clusters may be beneficial (work of Bart Hompes) Splitting large maximally-decomposed clusters may also be beneficial (cf. original BPI Challenge 2012 log) Support for different discovery and replay algorithms Merging nets Merging alignments / department of mathematics and computer science 24-11-2018