mlsanityData to 7 Oct 2026

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

arXiv 2103.05564 ↗First posted 9 Mar 2021cs.AIcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 5 of its 15 references that are in the map. First posted on arXiv 9 Mar 2021.

Builds on

5 of 15 references.

References · 5

  1. 2019Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Sciencecited by 32
  2. 2020Conformance Checking over Uncertain Event Datacited by 13
  3. 2019Mining Uncertain Event Data in Process Miningcited by 9
  4. 2019Discovering Process Models from Uncertain Event Datacited by 8
  5. 2020Efficient Time and Space Representation of Uncertain Event Datacited by 6

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Citing papers per year: 2022: 4; 2023: 1.

5 papers.