DecMiner: a ProM plug-in for declarative process mining

Process mining is a recent research area which aims at learning a process models from a set of process execution traces (see for further details). In this area, mining declarative process descriptions is still an open challenge.


DecMiner, a plug-in of the ProM process mining framework, proposes a logic-based approach for tackling this process mining problem. It relies on Inductive Logic Programming techniques and, in particular, on a modified version of the Inductive Constraint Logic algorithm. The algorithm takes as input a set of process execution traces, previously labeled as compliant or not, and produces a set of SCIFF rules which correctly classify them. This algorithm has been further modified for learning ConDec models.

The plug-in envisages different phases, ranging from the classification of traces into compliant and non-compliant subsets to the choice of which ConDec constraints have to be considered and finally to the rendering of the mined model.

The effectiveness of the approach has been evaluated in several papers.


The most recent DecMiner plug-in can be downloaded here (this version is compatible with ProM 4.2 and ProM 5.0).

An older version can be downloaded here.


The datasets used in the TOPNOC paper can be downloaded from here (instructions for using DecMiner to learn process models from these datasets are described here).

The random generator of logs used in the ILP07 paper and in the BPM07 paper can be downloaded from here.

The datasets and the code used in the ILP07 paper can be downloaded from here.