Process Discovery

Process Discovery algorithms want to find a suitable process model that describes the order of events/activities that are executed during a process execution. [6]

Alpha Miner

The alpha miner is one of the most known Process Discovery algorithm and is able to find:

  • A Petri net model where all the transitions are visible and unique and correspond to classified events (for example, to activities).

  • An initial marking that describes the status of the Petri net model when a execution starts.

  • A final marking that describes the status of the Petri net model when a execution ends.

Although this algorithm has the following disadvantage:

  • Cannot handle loops of length one and length two

  • Invisible and duplicated tasks cannot be discovered

  • Discovered model might not be sound

  • Weak against noise [6]

Inductive Miner

The basic idea of Inductive Miner is about detecting a ‘cut’ in the log (e.g. sequential cut, parallel cut, concurrent cut and loop cut) and then recur on sublogs, which were found applying the cut, until a base case is found. The Directly-Follows variant avoids the recursion on the sublogs but uses the Directly Follows graph.

Inductive miner models usually make extensive use of hidden transitions, especially for skipping/looping on a portion on the model. Furthermore, each visible transition has a unique label (there are no transitions in the model that share the same label).

Two process models can be derived: Petri Net and Process Tree.

Some advantages of this algorithm are:

  • Can handle invisible tasks

  • Model is sound

  • Most used process mining algorithm [6]

Heuristic Miner

Heuristics Miner is an algorithm that acts on the Directly-Follows Graph, providing way to handle with noise and to find common constructs (dependency between two activities, AND). The output of the Heuristics Miner is a Heuristics Net, so an object that contains the activities and the relationships between them. The Heuristics Net can be then converted into a Petri net.

It is possible to obtain a Heuristic Net and a Petri Net.

To apply the Heuristics Miner to discover a Heuristics Net, it is necessary to import a log. Then, a Heuristic Net can be found. There are also numerous possible parameters that can be inspected by clicking on the following button.

In addition, this algorithm takes frequency into account, detects short loops but does not guarantee a sound model.[6]