Efficient Time and Space Representation of Uncertain Event Data
Process mining is a discipline which concerns the analysis of execution data of operational processes, the extraction of models from event data, the measurement of the conformance between event data and normative models, and the enhancement of all aspects of processes. Most approaches assume that ev...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/13/11/285 |
| _version_ | 1797548473355075584 |
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| author | Marco Pegoraro Merih Seran Uysal Wil M. P. van der Aalst |
| author_facet | Marco Pegoraro Merih Seran Uysal Wil M. P. van der Aalst |
| author_sort | Marco Pegoraro |
| collection | DOAJ |
| description | Process mining is a discipline which concerns the analysis of execution data of operational processes, the extraction of models from event data, the measurement of the conformance between event data and normative models, and the enhancement of all aspects of processes. Most approaches assume that event data is accurately captured behavior. However, this is not realistic in many applications: data can contain uncertainty, generated from errors in recording, imprecise measurements, and other factors. Recently, new methods have been developed to analyze event data containing uncertainty; these techniques prominently rely on representing uncertain event data by means of graph-based models explicitly capturing uncertainty. In this paper, we introduce a new approach to efficiently calculate a graph representation of the behavior contained in an uncertain process trace. We present our novel algorithm, prove its asymptotic time complexity, and show experimental results that highlight order-of-magnitude performance improvements for the behavior graph construction. |
| first_indexed | 2024-03-10T14:59:50Z |
| format | Article |
| id | doaj.art-6eff3b7f96fd432d8cdfd8275f6b1f76 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T14:59:50Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-6eff3b7f96fd432d8cdfd8275f6b1f762023-11-20T20:16:37ZengMDPI AGAlgorithms1999-48932020-11-01131128510.3390/a13110285Efficient Time and Space Representation of Uncertain Event DataMarco Pegoraro0Merih Seran Uysal1Wil M. P. van der Aalst2Process and Data Science Group (PADS), Department of Computer Science, RWTH Aachen University, 52062 Aachen, GermanyProcess and Data Science Group (PADS), Department of Computer Science, RWTH Aachen University, 52062 Aachen, GermanyProcess and Data Science Group (PADS), Department of Computer Science, RWTH Aachen University, 52062 Aachen, GermanyProcess mining is a discipline which concerns the analysis of execution data of operational processes, the extraction of models from event data, the measurement of the conformance between event data and normative models, and the enhancement of all aspects of processes. Most approaches assume that event data is accurately captured behavior. However, this is not realistic in many applications: data can contain uncertainty, generated from errors in recording, imprecise measurements, and other factors. Recently, new methods have been developed to analyze event data containing uncertainty; these techniques prominently rely on representing uncertain event data by means of graph-based models explicitly capturing uncertainty. In this paper, we introduce a new approach to efficiently calculate a graph representation of the behavior contained in an uncertain process trace. We present our novel algorithm, prove its asymptotic time complexity, and show experimental results that highlight order-of-magnitude performance improvements for the behavior graph construction.https://www.mdpi.com/1999-4893/13/11/285process mininguncertain datapartial order |
| spellingShingle | Marco Pegoraro Merih Seran Uysal Wil M. P. van der Aalst Efficient Time and Space Representation of Uncertain Event Data Algorithms process mining uncertain data partial order |
| title | Efficient Time and Space Representation of Uncertain Event Data |
| title_full | Efficient Time and Space Representation of Uncertain Event Data |
| title_fullStr | Efficient Time and Space Representation of Uncertain Event Data |
| title_full_unstemmed | Efficient Time and Space Representation of Uncertain Event Data |
| title_short | Efficient Time and Space Representation of Uncertain Event Data |
| title_sort | efficient time and space representation of uncertain event data |
| topic | process mining uncertain data partial order |
| url | https://www.mdpi.com/1999-4893/13/11/285 |
| work_keys_str_mv | AT marcopegoraro efficienttimeandspacerepresentationofuncertaineventdata AT merihseranuysal efficienttimeandspacerepresentationofuncertaineventdata AT wilmpvanderaalst efficienttimeandspacerepresentationofuncertaineventdata |