scholarly journals Variational Autoencoder for Anomaly Detection in Event Data in Online Process Mining

2021 ◽  
Author(s):  
Philippe Krajsic ◽  
Bogdan Franczyk
Keyword(s):  
Anomaly Detection ◽  
Process Mining ◽  
Event Data ◽  
Variational Autoencoder ◽  
Online Process

scholarly journals Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems

2021 ◽  
Vol 11 (12) ◽  
pp. 5476
Author(s):  
Ana Pajić Simović ◽  
Slađan Babarogić ◽  
Ognjen Pantelić ◽  
Stefan Krstović
Keyword(s):  
Process Mining ◽  
Modeling Language ◽  
Sufficient Information ◽  
Event Data ◽  
Erp Systems ◽  
Domain Experts ◽  
Domain Specific ◽  
Event Logs ◽  
Domain Specific Modeling ◽  
Specific Modeling

Enterprise resource planning (ERP) systems are often seen as viable sources of data for process mining analysis. To perform most of the existing process mining techniques, it is necessary to obtain a valid event log that is fully compliant with the eXtensible Event Stream (XES) standard. In ERP systems, such event logs are not available as the concept of business activity is missing. Extracting event data from an ERP database is not a trivial task and requires in-depth knowledge of the business processes and underlying data structure. Therefore, domain experts require proper techniques and tools for extracting event data from ERP databases. In this paper, we present the full specification of a domain-specific modeling language for facilitating the extraction of appropriate event data from transactional databases by domain experts. The modeling language has been developed to support complex ambiguous cases when using ERP systems. We demonstrate its applicability using a case study with real data and show that the language includes constructs that enable a domain expert to easily model data of interest in the log extraction step. The language provides sufficient information to extract and transform data from transactional ERP databases to the XES format.

Toward Unsupervised 3d Point Cloud Anomaly Detection Using Variational Autoencoder

2021 ◽  
Author(s):  
Mana Masuda ◽  
Ryo Hachiuma ◽  
Ryo Fujii ◽  
Hideo Saito ◽  
Yusuke Sekikawa
Keyword(s):  
Anomaly Detection ◽  
Point Cloud ◽  
3D Point Cloud ◽  
Variational Autoencoder

Variational Autoencoder Anomaly-Detection of Avalanche Deposits in Satellite SAR Imagery

2020 ◽  
Author(s):  
Saumya Sinha ◽  
Sophie Giffard-Roisin ◽  
Fatima Karbou ◽  
Michael Deschatres ◽  
Anna Karas ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Variational Autoencoder ◽  
Sar Imagery
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