Heuristics Miner

This node implements the Heuristics Miner to discover a Petri net from an event table. The Heuristics Miner discovers a heuristics net, which is a directed graph with activities as nodes and edges connecting nodes to model dependencies between activities. The discovered heuristics net is converted into a Petri net. A Petri net is a directed bipartite graph used to model processes. It consists of places, transitions, and directed arcs connecting them. A place is enabled if it it contains at least one token. A transition can only fire if all incoming places are enabled. After firing a transition, a token is consumed from all of its incoming places, and a token is produced in all of its outgoing places. The initial marking indicates the initial state of the Petri net. Places that belong to the initial marking are marked by green tokens inside them. The final marking denotes the final state of the Petri net. Places within the final marking are highlighted with a heavier border.

Options

Case ID

The column that contains the case/trace identifiers.

Activity

The column that contains the activity/event identifiers.

Timestamp

The column that contains the timestamps.

All Tasks Connected

If enabled, every task must have at least one input and one output arc (except the initial and the final activity).

Long Distance Dependency

If enabled, long distance dependencies are shown in the model.

Threshold: Relative-to-Best

The percentage for the admissible distance between directly follows relations for an activity and the activity's best one. At 0, only the best directly follows relation will be shown for every activity. At 100, all will be shown.

Threshold: Dependency

A threshold for the strength of the directly follows relation.

Threshold: Length-One-loops

A threshold for the L1L metric.

Threshold: Length-Two-loops

A threshold for the L1L metric.

Threshold: Long Distance

A threshold for the strength of the eventually follows relation.

Views

Interactive View: Petri Net

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