Process Discoverer

When analysing business processes obtaining insights about their execution and assessing their performance are some of the most common activities performed by business analysts.

To provide the academic community with a free and open-source solution, I developed “Process Discoverer”. This tool provides business analysts with an easy way to inspect process event logs and acquire insights such as:

  • Process Control-flow (in which order the activities performed)
  • Frequency Insights (how often activities are performed)
  • Performance Insights (how long takes to perform activities)

The tool is available as a plugin for the Apromore platform. Additionally, the source-code of the tool is available at:

https://github.com/raffaeleconforti/ProcessDiscoverer


Infrequent Behaviour Filter

The analysis of business process event logs (i.e. process mining) can be negatively influenced by the presence of outliers, which reflect infrequent behavior or “noise”. In process discovery, where the objective is to automatically extract a process model from the data, this may result in rarely travelled pathways that clutter the process model.

To address this problem we proposed an automated technique to remove infrequent behavior from event logs (R. Conforti, M. La Rosa, and A.H.M. ter Hofstede. Filtering out Infrequent Behavior from Business Process Event Logs. IEEE Transactions on Knowledge and Data Engineering, 2016. PDF).

As an example, the figure below shows a process models in the Business Process Model and Notation (BPMN) language, discovered from the log of a Dutch Financial Institution (BPI Challenge 2012) using the Inductive Miner.

When applying our filtering technique the resulting discovered model improves significantly. The figure below shows the process model​ of the process discovered from the log of a Dutch Financial Institution (BPI Challenge 2012) by first pre-processing the log with our filtering technique, and then using the Inductive Miner.

The approach is available as a plugin for the ProM Framework as well as a standalone tool. Here you can download the standalone version of the tool. To use the tool, give the following command in the terminal:

java -jar InfrequentBehaviourFilter.jar


BPMNMiner

Existing techniques for automated discovery of process models from event logs generally produce flat process models. Thus, they fail to exploit the notion of subprocess as well as error handling and repetition constructs provided by contemporary process modeling notations, such as the Business Process Model and Notation (BPMN).

To address this problem we proposed a technique for automated discovery of hierarchical BPMN models containing interrupting and non-interrupting boundary events and activity markers (R. Conforti, M. Dumas, L. García-Bañuelos, and M. La Rosa. BPMN Miner: Automated discovery of BPMN process models with hierarchical structure. Information Systems, 56, pp 284-303, 2016).

As an example, the figure below shows a process models in the Business Process Model and Notation (BPMN) language, discovered using the Inductive Miner over an artificial log.

When applying our BPMNMiner the quality of the discovered model improves significantly. The figure below shows the process model of the process discovered from the same artificial log using our BPMNMiner.

The approach is available as a plugin for the ProM Framework as well as a standalone tool. Here you can download the standalone version of the tool. To use the tool, give the following command in the terminal:

java -jar BPMNMiner.jar