ILP Miner (Event Log)

This Node Is Deprecated — This node is kept for backwards-compatibility, but the usage in new workflows is no longer recommended. The documentation below might contain more information.

This node implements the ILP Miner to discover a Petri net from an event log. Multiple algorithms based on transition theory and ILP are provided.


Event classifer
The attribute to be used as an event classifier.
Filter Type
The filter type includes the following choices:
  • NONE: no filter.
  • SEQUENCE_ENCODING: the sequence encoding filter specifies at what level a branch should be cut off.
  • SLACK_VAR: the slack variable filter specifies what portion of constraints should be filtered out.
Noise Threshold
Threshold for filtering out noise.
Objective Function
The objective function for ILP. The following options are supported:
  • WEIGHTED_ABSOLUTE_PARIKH: weighted parikh values, using absolute frequencies.
  • WEIGHTED_RELATIVE_PARIKH: weighted parikh values, using relative frequencies.
  • UNWEIGHTED_PARIKH: unweighted parikh values.
  • MINIMIZE_ARCS: minimize arcs.
Variable Distribution
Setting LP variable. The following options are supported:
  • DUAL: two variables per event.
  • HYBRID: one variable per event, two for an event which is potentially in a self loop.
  • SINGLE: one variable per event.
Discovery Strategy
The Discovery Strategy. The following options are supported:
  • CAUSAL_FLEX_HEUR: mine a place per causal relation (flexible heuristics miner).
  • TRANSITION_PAIR: mine a connecting place between each pair of transitions.

Input Ports

an event log

Output Ports

a Petri net

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