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2021 | OriginalPaper | Chapter

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

Authors : Marco Pegoraro, Merih Seran Uysal, Wil M. P. van der Aalst

Published in: Application and Theory of Petri Nets and Concurrency

Publisher: Springer International Publishing

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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.

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previous chapter Cortado—An Interactive Tool for Data-Driven Process Discovery and Modeling

Available at https://github.com/proved-py/proved-core/.

Available at https://github.com/proved-py/proved-app/.

Pip - PyPi. https://pypi.org/project/pip/. Accessed 03 Feb 2020

The PROVED project on GitHub. https://github.com/proved-py/. Accessed 03 Feb 2021

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Title: PROVED: A Tool for Graph Representation and Analysis of Uncertain Event Data
Authors: Marco Pegoraro
Merih Seran Uysal
Wil M. P. van der Aalst
Publisher: Springer International Publishing
Book: Application and Theory of Petri Nets and Concurrency
Print ISBN: 978-3-030-76982-6

Electronic ISBN: 978-3-030-76983-3

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-76983-3_24

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