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

• Published in: Application and Theory of Petri Nets and Concurrency pp. 476 - 486
• Main Authors: Pegoraro, Marco, Uysal, Merih Seran, van der Aalst, Wil M. P.
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Partial order; Petri net tool; Process mining; Uncertain data
• ISBN: 3030769828; 9783030769826
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-76983-3_24

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.