Process Mining of Event Logs: A Case Study Evaluating Internal Control Effectiveness

2019 ◽  
Vol 33 (3) ◽  
pp. 141-156 ◽  
Author(s):  
Tiffany Chiu ◽  
Mieke Jans
Keyword(s):  
Internal Control ◽  
Process Mining ◽  
Real Life ◽  
Potential Control ◽  
Control Effectiveness ◽  
Event Logs ◽  
Event Log ◽  
New Type ◽  
Potential Risks

SYNOPSISThis paper aims at adopting process mining to evaluate the effectiveness of internal control using a real-life event log. Specifically, the evaluation is based on the full population of an event log and it contains four analyses: (1) variant analysis that identifies standard and non-standard variants, (2) segregation of duties analysis that examines whether employees violate segregation of duties controls, (3) personnel analysis that investigates whether employees are involved in multiple potential control violations, and (4) timestamp analysis that detects time-related issues including weekend activities and lengthy process duration. Results from the case study indicate that process mining could assist auditors in identifying audit-relevant issues such as non-standard variants, weekend activities, and personnel who are involved in multiple violations. Process mining enables auditors to detect potential risks, ineffective internal controls, and inefficient processes. Therefore, process mining generates a new type of audit evidence and could revolutionize the current audit procedure.

Auditor Choices during Event Log Building for Process Mining

2019 ◽  
Vol 16 (2) ◽  
pp. 59-67
Author(s):  
Mieke Jans
Keyword(s):  
Analytical Procedure ◽  
Process Mining ◽  
Current Event ◽  
Dual Purpose ◽  
Event Logs ◽  
Event Log ◽  
Audit Evidence ◽  
Mining Projects ◽  
Made In

ABSTRACT Applying process mining as an analytical procedure is a relatively young stream of thought in auditing. This paper examines the first step of such process-mining projects, which involves extracting and structuring the data in the required format for analysis. The article has a dual purpose: (1) to provide an overview of the choices to be made in this phase, and (2) to provide insights into the current event log preferences of auditors. These insights are valuable for a better understanding of how event logs are currently structured, along with the consequences of this structure for the analytical procedure. This matter is important because different preparation steps could lead to varying analytical procedures and consequently, to other audit evidence. This study also aims to reveal what data are perceived as most valuable to the auditor for further analysis. To address this goal, a case study has been conducted.

scholarly journals https://ijsea.com/archive/volume10/issue9/IJSEA10091005.pdf

2021 ◽  
Vol 10 (9) ◽  
pp. 144-147
Author(s):  
Huiling LI ◽  
Xuan SU ◽  
Shuaipeng ZHANG
Keyword(s):  
Process Model ◽  
Large Scale ◽  
Process Mining ◽  
Data Set ◽  
Systems Model ◽  
Event Logs ◽  
Event Log ◽  
Low Efficiency ◽  
Sampling Approach

Massive amounts of business process event logs are collected and stored by modern information systems. Model discovery aims to discover a process model from such event logs, however, most of the existing approaches still suffer from low efficiency when facing large-scale event logs. Event log sampling techniques provide an effective scheme to improve the efficiency of process discovery, but the existing techniques still cannot guarantee the quality of model mining. Therefore, a sampling approach based on set coverage algorithm named set coverage sampling approach is proposed. The proposed sampling approach has been implemented in the open-source process mining toolkit ProM. Furthermore, experiments using a real event log data set from conformance checking and time performance analysis show that the proposed event log sampling approach can greatly improve the efficiency of log sampling on the premise of ensuring the quality of model mining.

Mining E-Mail Messages

2010 ◽  
pp. 212-234
Author(s):  
Wil M.P. van der Aalst ◽  
Andriy Nikolov
Keyword(s):  
Process Mining ◽  
Workflow Management ◽  
Social Structures ◽  
Interaction Patterns ◽  
Work Processes ◽  
Event Logs ◽  
Event Log ◽  
Patterns And Processes ◽  
E Mail ◽  
Mining Tools

Increasingly information systems log historic information in a systematic way. Workflow management systems, but also ERP, CRM, SCM, and B2B systems often provide a so-called “event log’’, i.e., a log recording the execution of activities. Thus far, process mining has been mainly focusing on structured event logs resulting in powerful analysis techniques and tools for discovering process, control, data, organizational, and social structures from event logs. Unfortunately, many work processes are not supported by systems providing structured logs. Instead very basic tools such as text editors, spreadsheets, and e-mail are used. This paper explores the application of process mining to e-mail, i.e., unstructured or semi-structured e-mail messages are converted into event logs suitable for application of process mining tools. This paper presents the tool EMailAnalyzer, embedded in the ProM process mining framework, which analyzes and transforms e-mail messages to a format that allows for analysis using our process mining techniques. The main innovative aspect of this work is that, unlike most other work in this area, our analysis is not restricted to social network analysis. Based on e-mail logs we can also discover interaction patterns and processes.

Fuzzy Hybrid FMEA for Risk Assessment in Service Industry

2021 ◽  
pp. 43-75
Author(s):  
Nihan Kabadayi
Keyword(s):  
Risk Assessment ◽  
Hospitality Industry ◽  
Failure Modes ◽  
Service Industry ◽  
Service Providers ◽  
Real Life ◽  
Potential Risks ◽  
Fmea Method ◽  
Organization Failure

Service products are mostly produced and consumed simultaneously through interaction between customer and service providers. To prevent external failures in service operations, it is important to identify potential risks and take relevant actions to eliminate or reduce the occurrence. Therefore, risk assessment is vital to customer satisfaction in any service organization. Failure mode and effects analysis (FMEA) is an effective and useful tool for risk assessment. Although FMEA has been extensively studied in the manufacturing literature, there are a limited number of studies considering the application of FMEA in the hospitality industry. In traditional FMEA, the risk priority of failure modes is determined by generating a crisp risk priority number (RPN). However, it has been claimed in the literature that crisp RPN doesn't have a good performance in reflecting real-life situations. To overcome this shortcoming, a fuzzy hybrid FMEA method is developed. The proposed method has been tested on a case study in a five-star hotel to assess its applicability and benefits.

scholarly journals Database-Less Extraction of Event Logs from Redo Logs

2021 ◽  
pp. 73-82
Author(s):  
Dorina Bano ◽  
Tom Lichtenstein ◽  
Finn Klessascheck ◽  
Mathias Weske
Keyword(s):  
Performance Analysis ◽  
Real World ◽  
Business Processes ◽  
Process Mining ◽  
Detailed Knowledge ◽  
System Failure ◽  
Conformance Checking ◽  
Event Logs ◽  
Event Log ◽  
And Performance

Process mining is widely adopted in organizations to gain deep insights about running business processes. This can be achieved by applying different process mining techniques like discovery, conformance checking, and performance analysis. These techniques are applied on event logs, which need to be extracted from the organization’s databases beforehand. This not only implies access to databases, but also detailed knowledge about the database schema, which is often not available. In many real-world scenarios, however, process execution data is available as redo logs. Such logs are used to bring a database into a consistent state in case of a system failure. This paper proposes a semi-automatic approach to extract an event log from redo logs alone. It does not require access to the database or knowledge of the databaseschema. The feasibility of the proposed approach is evaluated on two synthetic redo logs.

scholarly journals Process Modelling Based on Event Logs

2018 ◽  
Vol 1 (1) ◽  
pp. 385-392
Author(s):  
Edyta Brzychczy
Keyword(s):  
Process Management ◽  
Process Mining ◽  
Process Analysis ◽  
Process Modelling ◽  
Process Models ◽  
Sources Of Information ◽  
Event Logs ◽  
It Systems ◽  
Many Sources

Abstract Process modelling is a very important stage in a Business Process Management cycle enabling process analysis and its redesign. Many sources of information for process modelling purposes exist. It may be an analysis of documentation related directly or indirectly to the process being analysed, observations or participation in the process. Nowadays, for this purpose, it is increasingly proposed to use the event logs from organization’s IT systems. Event logs could be analysed with process mining techniques to create process models expressed by various notations (i.e. Petri Nets, BPMN, EPC). Process mining enables also conformance checking and enhancement analysis of the processes. In the paper issues related to process modelling and process mining are briefly discussed. A case study, an example of delivery process modelling with process mining technique is presented.

scholarly journals Applied Sequence Clustering Techniques for Process Mining

2011 ◽  
pp. 481-502 ◽  
Author(s):  
Diogo R. Ferreira
Keyword(s):  
Business Processes ◽  
Process Mining ◽  
Behavioral Patterns ◽  
Daily Work ◽  
Sequence Clustering ◽  
Event Logs ◽  
Common Trend ◽  
First Case ◽  
Insight Into

This chapter introduces the principles of sequence clustering and presents two case studies where the technique is used to discover behavioral patterns in event logs. In the first case study, the goal is to understand the way members of a software team perform their daily work, and the application of sequence clustering reveals a set of behavioral patterns that are related to some of the main processes being carried out by that team. In the second case study, the goal is to analyze the event history recorded in a technical support database in order to determine whether the recorded behavior complies with a predefined issue handling process. In this case, the application of sequence clustering confirms that all behavioral patterns share a common trend that resembles the original process. Throughout the chapter, special attention is given to the need for data preprocessing in order to obtain results that provide insight into the typical behavior of business processes.

An application framework for mining online learning processes through event-logs

2019 ◽  
Vol 25 (5) ◽  
pp. 860-886
Author(s):  
Güzin Özdağoğlu ◽  
Gülin Zeynep Öztaş ◽  
Mehmet Çağliyangil
Keyword(s):  
Data Mining ◽  
Online Learning ◽  
Process Mining ◽  
General Pattern ◽  
Learning Processes ◽  
Application Framework ◽  
Main Process ◽  
Content Type ◽  
Event Logs ◽  
Event Log

Purpose Learning management systems (LMS) provide detailed information about the processes through event-logs. Process and related data-mining approaches can reveal valuable information from these files to help teachers and executives to monitor and manage their online learning processes. In this regard, the purpose of this paper is to present an overview of the current direction of the literature on educational data mining, and an application framework to analyze the educational data provided by the Moodle LMS. Design/methodology/approach The paper presents a framework to provide a decision support through the approaches existing in process and data-mining fields for analyzing the event-log data gathered from LMS platforms. In this framework, latent class analysis (LCA) and sequential pattern mining approaches were used to understand the general patterns; heuristic and fuzzy approaches were performed for process mining to obtain the workflows and statistics; finally, social-network analysis was conducted to discover the collaborations. Findings The analyses conducted in the study give clues for the process performance of the course during a semester by indicating exceptional situations, clarifying the activity flows, understanding the main process flow and revealing the students’ interactions. Findings also show that using the preliminary data analyses before process mining steps is also beneficial to understand the general pattern and expose the irregular ones. Originality/value The study highlights the benefits of analyzing event-log files of LMSs to improve the quality of online educational processes through a case study based on Moodle event-logs. The application framework covers preliminary analyses such as LCA before the use of process mining algorithms to reveal the exceptional situations.

scholarly journals Simplified Process Model Discovery Based on Role-Oriented Genetic Mining

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Weidong Zhao ◽  
Xi Liu ◽  
Weihui Dai
Keyword(s):  
Process Model ◽  
Process Mining ◽  
Fitness Function ◽  
Control Flow ◽  
Process Models ◽  
Programming Approach ◽  
Event Logs ◽  
Process Complexity

Process mining is automated acquisition of process models from event logs. Although many process mining techniques have been developed, most of them are based on control flow. Meanwhile, the existing role-oriented process mining methods focus on correctness and integrity of roles while ignoring role complexity of the process model, which directly impacts understandability and quality of the model. To address these problems, we propose a genetic programming approach to mine the simplified process model. Using a new metric of process complexity in terms of roles as the fitness function, we can find simpler process models. The new role complexity metric of process models is designed from role cohesion and coupling, and applied to discover roles in process models. Moreover, the higher fitness derived from role complexity metric also provides a guideline for redesigning process models. Finally, we conduct case study and experiments to show that the proposed method is more effective for streamlining the process by comparing with related studies.

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