PROVED: A Tool for Graph Representation and Analysis of Uncertain Event Data

TL;DR

PROVED is a tool designed to analyze uncertain event data in process mining by using behavior graphs and Petri net semantics to facilitate discovery and conformance checking.

Contribution

It introduces a novel approach to represent and analyze uncertain event data using behavior graphs and Petri nets, enabling new analysis capabilities.

Findings

Supports exploration and navigation of uncertain event data

Enables discovery of process models from uncertain logs

Facilitates conformance checking with uncertain information

Abstract

The discipline of process mining aims to study processes in a data-driven manner by analyzing historical process executions, often employing Petri nets. Event data, extracted from information systems (e.g. SAP), serve as the starting point for process mining. Recently, novel types of event data have gathered interest among the process mining community, including uncertain event data. Uncertain events, process traces and logs contain attributes that are characterized by quantified imprecisions, e.g., a set of possible attribute values. The PROVED tool helps to explore, navigate and analyze such uncertain event data by abstracting the uncertain information using behavior graphs and nets, which have Petri nets semantics. Based on these constructs, the tool enables discovery and conformance checking.