PROVED: A Tool for Graph Representation and Analysis of Uncertain Event Data
This work addresses the need for tools to handle uncertain event data in process mining, which is an incremental advancement for researchers and practitioners in this domain.
The paper tackles the challenge of analyzing uncertain event data in process mining by introducing the PROVED tool, which uses behavior graphs and nets with Petri net semantics to enable discovery and conformance checking for such data.
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.