Deviance mining with treatment learning and declare-based encoding of event logs

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.

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An Efficient Analytical Approach to Visualize Text-Based Event Logs for Semiconductor Equipment

Applied Sciences ◽

10.3390/app11135944 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5944

Author(s):

Gunwoo Lee ◽

Jongpil Jeong

Keyword(s):

Complex System ◽

Analytical Approach ◽

System Behavior ◽

File Size ◽

Log Files ◽

Event Logs ◽

Hard Disk Space ◽

Long Time ◽

Log File ◽

Runtime Information

Semiconductor equipment consists of a complex system in which numerous components are organically connected and controlled by many controllers. EventLog records all the information available during system processes. Because the EventLog records system runtime information so developers and engineers can understand system behavior and identify possible problems, it is essential for engineers to troubleshoot and maintain it. However, because the EventLog is text-based, complex to view, and stores a large quantity of information, the file size is very large. For long processes, the log file comprises several files, and engineers must look through many files, which makes it difficult to find the cause of the problem and therefore, a long time is required for the analysis. In addition, if the file size of the EventLog becomes large, the EventLog cannot be saved for a prolonged period because it uses a large amount of hard disk space on the CTC computer. In this paper, we propose a method to reduce the size of existing text-based log files. Our proposed method saves and visualizes text-based EventLogs in DB, making it easier to approach problems than the existing text-based analysis. We will confirm the possibility and propose a method that makes it easier for engineers to analyze log files.

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Enhancing change mining from a collection of event logs: Merging and Filtering approaches

Journal of Physics Conference Series ◽

10.1088/1742-6596/1743/1/012020 ◽

2021 ◽

Vol 1743 ◽

pp. 012020

Author(s):

A Hmami ◽

H Sbai ◽

M Fredj

Keyword(s):

Event Logs

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Cross-Organization Emergency Response Process Mining: An Approach Based on Petri Nets

Mathematical Problems in Engineering ◽

10.1155/2020/8836007 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Cong Liu ◽

Huiling Li ◽

Qingtian Zeng ◽

Ting Lu ◽

Caihong Li

Keyword(s):

Petri Nets ◽

Emergency Response ◽

Resource Sharing ◽

Process Mining ◽

Process Models ◽

Response Process ◽

Extensive Experience ◽

Event Logs ◽

Collaboration Patterns ◽

Sharing Patterns

To support effective emergency disposal, organizations need to collaborate with each other to complete the emergency mission that cannot be handled by a single organization. In general, emergency disposal that involves multiple organizations is typically organized as a group of interactive processes, known as cross-organization emergency response processes (CERPs). The construction of CERPs is a time-consuming and error-prone task that requires practitioners to have extensive experience and business background. Process mining aims to construct process models by analyzing event logs. However, existing process mining techniques cannot be applied directly to discover CERPs since we have to consider the complexity of various collaborations among different organizations, e.g., message exchange and resource sharing patterns. To tackle this challenge, a CERP model mining method is proposed in this paper. More specifically, we first extend classical Petri nets with resource and message attributes, known as resource and message aware Petri nets (RMPNs). Then, intra-organization emergency response process (IERP) models that are represented as RMPNs are discovered from emergency drilling event logs. Next, collaboration patterns among emergency organizations are formally defined and discovered. Finally, CERP models are obtained by merging IERP models and collaboration patterns. Through comparative experimental evaluation using the fire emergency drilling event log, we illustrate that the proposed approach facilitates the discovery of high-quality CERP models than existing state-of-the-art approaches.

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