Definitions

The data-driven method for making real processes visible from IT systems.

Process data plus context and AI – from mere visibility to understanding and action.

Data as nodes and edges – the natural basis for connected process data.

Events, objects, and relationships in a graph – multiple perspectives at once.

Cause instead of mere sequence – the real relationships between process steps.

Why "A is followed by B" is not enough for realistic process models.

The classic input format for Process Mining – and its limits.

The discipline of systematically designing, steering, and improving processes.

Input, activities, output – what makes and delineates a process.

Processes centered on multiple objects instead of a single case ID.

Automatically generate a process model from event data – with no prior assumptions.

Check the actual flow against a target model – reveal deviations and violations.

Capture manual desktop activities – the complement to Process Mining.

The open standard for object-centric event data.

A distinct path through the process – the basis of variant analysis.

The concrete single execution of a process – the unit of analysis in Process Mining.

Play processes through computationally – what-if scenarios before implementation.

The living digital replica of a process – for analysis, simulation, and control.

AS-IS versus TO-BE – the foundation of every process improvement.