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

arXiv: https://arxiv.org/abs/2103.05564 · HTML: https://mlsanity.com/paper/2103.05564/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 9 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Science](https://mlsanity.com/paper/1905.06169/) (2019)
- [Conformance Checking over Uncertain Event Data](https://mlsanity.com/paper/2009.14452/) (2020)
- [Mining Uncertain Event Data in Process Mining](https://mlsanity.com/paper/1910.00089/) (2019)
- [Discovering Process Models from Uncertain Event Data](https://mlsanity.com/paper/1909.11567/) (2019)
- [Efficient Time and Space Representation of Uncertain Event Data](https://mlsanity.com/paper/2010.00334/) (2020)

## Built on by (most cited first, 5 of 5)

- [Process Mining for Unstructured Data: Challenges and Research Directions](https://mlsanity.com/paper/2401.13677/) (2023)
- [Probabilistic and Non-Deterministic Event Data in Process Mining: Embedding Uncertainty in Process Analysis Techniques](https://mlsanity.com/paper/2205.04827/) (2022)
- [Process Mining on Uncertain Event Data](https://mlsanity.com/paper/2204.04148/) (2022)
- [Uncertain Case Identifiers in Process Mining: A User Study of the Event-Case Correlation Problem on Click Data](https://mlsanity.com/paper/2204.04164/) (2022)
- [Resolving Uncertain Case Identifiers in Interaction Logs: A User Study](https://mlsanity.com/paper/2212.00009/) (2022)

## 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.