Seeliger, Alexander ; Nolle, Timo ; Mühlhäuser, Max (2018)
ProcessExplorer: An Interactive Visual Recommendation System for Process Mining.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2018 |
| Autor(en): | Seeliger, Alexander ; Nolle, Timo ; Mühlhäuser, Max |
| Art des Eintrags: | Bibliographie |
| Titel: | ProcessExplorer: An Interactive Visual Recommendation System for Process Mining |
| Sprache: | Englisch |
| Publikationsjahr: | 20 August 2018 |
| Ort: | London, UK |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | KDD 2018 Workshop on Interactive Data Exploration and Analytics |
| Buchtitel: | KDD 2018 Workshop on Interactive Data Exploration and Analytics |
| URL / URN: | http://poloclub.gatech.edu/idea2018/ |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Process mining, Exploratory analysis, Process analysis, Visualization recommendation, Business process intelligence |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Telekooperation |
| Hinterlegungsdatum: | 19 Jun 2018 09:46 |
| Letzte Änderung: | 14 Jun 2021 06:14 |
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| Suche nach Titel in: | TUfind oder in Google |
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