The Automated Discovery of Hybrid Processes

Fabrizio M. MaggiUniversity of Tartu

Tijs Slaats*

IT University of Copenhagen

Exformatics

Hajo A. ReijersVU University of

Amsterdam

Overview

• Hybrid Process Models• Discovering Hybrid Process Models• Evaluation• Future Work + Conclusion

Imperative Process Models

Imperative Process Models

• Flow-oriented• Well-suited to rigid processes• In a model with no flow nothing can happen• Adding flow allows for additional possible

behaviors• Common in academia and industry

Declarative Process Models

Declarative Process Models

• Constraint-oriented• Well-suited to flexible processes• In an unconstrained model anything can

happen• Adding constraints limits behavior• Still a novelty in industry

Hybrid Process Models

Hybrid Process Models

• Different parts of the same process may be more or less flexible.• Modeling a flexible process imperatively, or a strict process declaratively, often leads to incomprehensible models.• Mixing of paradigms on the sub-process level:– Pockets of flexibility in workflow services [Sadiq et al.] – Flexibility as a Service (FAAS) [Aalst et al.]

Process Discovery

Event Log Process Model

Process Discovery

• Current discovery techniques:– Mining Petri-nets / Flowcharts

• Alpha miner, Heuristic Miner, ILP miner, …

– Mining Declarative constraints• Declare miner

• But what if the log contains both flexible and rigid parts?– Imperative miners tend to blow-up on flexible logs– Declarative miners will need to find many constraints to model

the strict parts of the process and will often have trouble finding all of them (resulting in processes with low precision)

• Solution: Hybrid Process Discovery!

Hybrid Process Discovery

Context analysis

Clustering (based on

context analysis)

Clustering (association rule

mining)

Standard Process

Discovery

Declare Discovery

String Edit Distance

Evaluation – BPI Challenge 2012Results of Imperative Miners

Evaluation – BPI Challenge 2012Result of Hybrid Miner

Evaluation – BPI Challenge 2012Comparison of Results

Fitness

Size

Future Work

• Proper plugin for Prom.• Visualization of resulting hybrid model.• Further evaluation on real cases.• Further refinement of the heuristics used in

the approach, for example the thresholds used for determining if an event is structured or unstructured.

Conclusion

• We offer the first automated approach for discovering hybrid process models.

• Using the approach on existing logs gives encouraging results: in particular for semi-structured logs the discovered models become more readable.

• Plenty of room for future work in an exciting new angle on process mining.