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Learning of Process Representations Using Recurrent Neural Networks

Seeliger, Alexander ; Luettgen, Stefan ; Nolle, Timo ; Mühlhäuser, Max
eds.: La Rosa, Marcello ; Sadiq, Shazia ; Teniente, Ernest (2021)
Learning of Process Representations Using Recurrent Neural Networks.
33rd International Conference on Advanced Information Systems Engineering (CAiSE 2021). virtual Conference (28.06-02.07.2021)
doi: 10.1007/978-3-030-79382-1_7
Conference or Workshop Item, Bibliographie

Abstract

In process mining, many tasks use a simplified representation of a single case to perform tasks like trace clustering, anomaly detection, or subset identification. These representations may capture the control flow of the process as well as the context a case is executed in. However, most of these representations are hand-crafted, which is very time-consuming for practical use, and the incorporation of event and case attributes as contextual factors is challenging. In this paper, we propose a neural network architecture for representation learning to automate the generation. Our network is trained in a supervised fashion to learn the most meaningful features to obtain highly dense and accurate vector representations of cases of an event log. We implemented our approach and conducted experiments in the context of trace clustering with publicly available event logs to show its applicability. The results show improvements regarding the separation of cases, and that process models discovered from identified subsets are of high quality.

Item Type: Conference or Workshop Item
Erschienen: 2021
Editors: La Rosa, Marcello ; Sadiq, Shazia ; Teniente, Ernest
Creators: Seeliger, Alexander ; Luettgen, Stefan ; Nolle, Timo ; Mühlhäuser, Max
Type of entry: Bibliographie
Title: Learning of Process Representations Using Recurrent Neural Networks
Language: English
Date: 2021
Publisher: Springer International Publishing
Book Title: Advanced Information Systems Engineering
Series: Lecture Notes in Computer Science
Series Volume: 12751
Event Title: 33rd International Conference on Advanced Information Systems Engineering (CAiSE 2021)
Event Location: virtual Conference
Event Dates: 28.06-02.07.2021
DOI: 10.1007/978-3-030-79382-1_7
Abstract:

In process mining, many tasks use a simplified representation of a single case to perform tasks like trace clustering, anomaly detection, or subset identification. These representations may capture the control flow of the process as well as the context a case is executed in. However, most of these representations are hand-crafted, which is very time-consuming for practical use, and the incorporation of event and case attributes as contextual factors is challenging. In this paper, we propose a neural network architecture for representation learning to automate the generation. Our network is trained in a supervised fashion to learn the most meaningful features to obtain highly dense and accurate vector representations of cases of an event log. We implemented our approach and conducted experiments in the context of trace clustering with publicly available event logs to show its applicability. The results show improvements regarding the separation of cases, and that process models discovered from identified subsets are of high quality.

Divisions: 20 Department of Computer Science
20 Department of Computer Science > Telecooperation
Date Deposited: 23 Jun 2021 12:29
Last Modified: 23 Jun 2021 12:29
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