Transcript Decomposed Process Mining: The ILP Case

Decomposed Process
Mining: The ILP Case
Eric Verbeek and Wil van der Aalst
A Problem
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A Solution
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Regular Discovery
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Decomposed Discovery: Divide
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Decomposed Discovery: Conquer
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Regular Replay
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Decomposed Replay: Divide
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Decomposed Replay: Conquer
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Example Model (Accepting Petri Net)
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Example Event (Activity) Log
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Divide and Conquer Framework
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Divide and Conquer Framework
See http://www.promtools.org/
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Decomposed ILP Discovery Algorithm
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Filter (First Cluster)
Filter In/Out
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Replace
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Decomposed ILP Discovery Algorithm
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Discovered Model
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Decomposed ILP Replay Algorithm
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Strategy (Third Cluster)
Filter
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Replace
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Decomposed ILP Replay Algorithm
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Replay Cost Factor
• Implementation issue:
• 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.
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Decomposed ILP Replay Algorithm
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Case Study Setting
• Mode
Event log based
on BPI Challenge
2012 log
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Model discovered
in earlier work
Event log aligned
on discovered
model
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Case Study Model
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Case Study Results
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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
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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)
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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
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