Seeliger, Alexander ; Nolle, Timo ; Mühlhäuser, Max (2018):
ProcessExplorer: An Interactive Visual Recommendation System for Process Mining.
In: KDD 2018 Workshop on Interactive Data Exploration and Analytics,
London, UK, [Conference or Workshop Item]
Abstract
More and more business process data is collected by organizations to analyze and optimize their process performance. As a consequence it is particularly challenging to locate possible process issues or potential optimizations using process mining. Process mining aims at analyzing the actual usage of information systems by reconstructing a process model from recorded event log. However, such large amount of data often leads to spaghetti-like visualizations which are in-comprehensive and inaccurate. This paper addresses this issue by introducing an unsupervised visual recommender system for process analysis. The system provides suggestions during the interactive visual inspection of the discovered process model by recommending points of interests (e.g., long duration times or exceptional process behavior) ranked by severity. For calculated interest points we characterize the deviation from the average behavior as well as compute the effect the observed conspicuousness has. Our approach has been implemented as a ProM plugin. We evaluate our approach by presenting a case study using a real life event log.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2018 |
| Creators: | Seeliger, Alexander ; Nolle, Timo ; Mühlhäuser, Max |
| Title: | ProcessExplorer: An Interactive Visual Recommendation System for Process Mining |
| Language: | English |
| Abstract: | More and more business process data is collected by organizations to analyze and optimize their process performance. As a consequence it is particularly challenging to locate possible process issues or potential optimizations using process mining. Process mining aims at analyzing the actual usage of information systems by reconstructing a process model from recorded event log. However, such large amount of data often leads to spaghetti-like visualizations which are in-comprehensive and inaccurate. This paper addresses this issue by introducing an unsupervised visual recommender system for process analysis. The system provides suggestions during the interactive visual inspection of the discovered process model by recommending points of interests (e.g., long duration times or exceptional process behavior) ranked by severity. For calculated interest points we characterize the deviation from the average behavior as well as compute the effect the observed conspicuousness has. Our approach has been implemented as a ProM plugin. We evaluate our approach by presenting a case study using a real life event log. |
| Journal or Publication Title: | KDD 2018 Workshop on Interactive Data Exploration and Analytics |
| Book Title: | KDD 2018 Workshop on Interactive Data Exploration and Analytics |
| Place of Publication: | London, UK |
| Uncontrolled Keywords: | Process mining, Exploratory analysis, Process analysis, Visualization recommendation, Business process intelligence |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Telecooperation |
| Date Deposited: | 19 Jun 2018 09:46 |
| URL / URN: | http://poloclub.gatech.edu/idea2018/ |
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