A Multi-View Framework to Detect Redundant Activity Labels for More Representative Event Logs in Process Mining
Process mining aims to gain knowledge of business processes via the discovery of process models from event logs generated by information systems. The insights revealed from process mining heavily rely on the quality of the event logs. Activities extracted from different data sources or the free-text...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
|
| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/14/6/181 |
| _version_ | 1797487331000713216 |
|---|---|
| author | Qifan Chen Yang Lu Charmaine S. Tam Simon K. Poon |
| author_facet | Qifan Chen Yang Lu Charmaine S. Tam Simon K. Poon |
| author_sort | Qifan Chen |
| collection | DOAJ |
| description | Process mining aims to gain knowledge of business processes via the discovery of process models from event logs generated by information systems. The insights revealed from process mining heavily rely on the quality of the event logs. Activities extracted from different data sources or the free-text nature within the same system may lead to inconsistent labels. Such inconsistency would then lead to redundancy in activity labels, which refer to labels that have different syntax but share the same behaviours. Redundant activity labels can introduce unnecessary complexities to the event logs. The identification of these labels from data-driven process discovery are difficult and rely heavily on human intervention. Neither existing process discovery algorithms nor event data preprocessing techniques can solve such redundancy efficiently. In this paper, we propose a multi-view approach to automatically detect redundant activity labels by using not only context-aware features such as control–flow relations and attribute values but also semantic features from the event logs. Our evaluation of several publicly available datasets and a real-life case study demonstrate that our approach can efficiently detect redundant activity labels even with low-occurrence frequencies. The proposed approach can add value to the preprocessing step to generate more representative event logs. |
| first_indexed | 2024-03-09T23:46:06Z |
| format | Article |
| id | doaj.art-c023f377e5564b72aa9daaea6f23e2d8 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-09T23:46:06Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-c023f377e5564b72aa9daaea6f23e2d82023-11-23T16:43:39ZengMDPI AGFuture Internet1999-59032022-06-0114618110.3390/fi14060181A Multi-View Framework to Detect Redundant Activity Labels for More Representative Event Logs in Process MiningQifan Chen0Yang Lu1Charmaine S. Tam2Simon K. Poon3School of Computer Science, The University of Sydney, Sydney, NSW 2006, AustraliaSchool of Computer Science, The University of Sydney, Sydney, NSW 2006, AustraliaCentre for Translational Data Science and Northern Clinical School, The University of Sydney, Sydney, NSW 2006, AustraliaSchool of Computer Science, The University of Sydney, Sydney, NSW 2006, AustraliaProcess mining aims to gain knowledge of business processes via the discovery of process models from event logs generated by information systems. The insights revealed from process mining heavily rely on the quality of the event logs. Activities extracted from different data sources or the free-text nature within the same system may lead to inconsistent labels. Such inconsistency would then lead to redundancy in activity labels, which refer to labels that have different syntax but share the same behaviours. Redundant activity labels can introduce unnecessary complexities to the event logs. The identification of these labels from data-driven process discovery are difficult and rely heavily on human intervention. Neither existing process discovery algorithms nor event data preprocessing techniques can solve such redundancy efficiently. In this paper, we propose a multi-view approach to automatically detect redundant activity labels by using not only context-aware features such as control–flow relations and attribute values but also semantic features from the event logs. Our evaluation of several publicly available datasets and a real-life case study demonstrate that our approach can efficiently detect redundant activity labels even with low-occurrence frequencies. The proposed approach can add value to the preprocessing step to generate more representative event logs.https://www.mdpi.com/1999-5903/14/6/181process miningactivity labelprocess event logdata quality |
| spellingShingle | Qifan Chen Yang Lu Charmaine S. Tam Simon K. Poon A Multi-View Framework to Detect Redundant Activity Labels for More Representative Event Logs in Process Mining Future Internet process mining activity label process event log data quality |
| title | A Multi-View Framework to Detect Redundant Activity Labels for More Representative Event Logs in Process Mining |
| title_full | A Multi-View Framework to Detect Redundant Activity Labels for More Representative Event Logs in Process Mining |
| title_fullStr | A Multi-View Framework to Detect Redundant Activity Labels for More Representative Event Logs in Process Mining |
| title_full_unstemmed | A Multi-View Framework to Detect Redundant Activity Labels for More Representative Event Logs in Process Mining |
| title_short | A Multi-View Framework to Detect Redundant Activity Labels for More Representative Event Logs in Process Mining |
| title_sort | multi view framework to detect redundant activity labels for more representative event logs in process mining |
| topic | process mining activity label process event log data quality |
| url | https://www.mdpi.com/1999-5903/14/6/181 |
| work_keys_str_mv | AT qifanchen amultiviewframeworktodetectredundantactivitylabelsformorerepresentativeeventlogsinprocessmining AT yanglu amultiviewframeworktodetectredundantactivitylabelsformorerepresentativeeventlogsinprocessmining AT charmainestam amultiviewframeworktodetectredundantactivitylabelsformorerepresentativeeventlogsinprocessmining AT simonkpoon amultiviewframeworktodetectredundantactivitylabelsformorerepresentativeeventlogsinprocessmining AT qifanchen multiviewframeworktodetectredundantactivitylabelsformorerepresentativeeventlogsinprocessmining AT yanglu multiviewframeworktodetectredundantactivitylabelsformorerepresentativeeventlogsinprocessmining AT charmainestam multiviewframeworktodetectredundantactivitylabelsformorerepresentativeeventlogsinprocessmining AT simonkpoon multiviewframeworktodetectredundantactivitylabelsformorerepresentativeeventlogsinprocessmining |