Mining E-Mail Messages

Methodological Advancements in Intelligent Information Technologies - Advances in Intelligent Information Technologies ◽

10.4018/978-1-60566-970-0.ch011 ◽

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.

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

International Journal of Science and Engineering Applications ◽

10.7753/ijsea1009.1006 ◽

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.

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Database-Less Extraction of Event Logs from Redo Logs

Business Information Systems ◽

10.52825/bis.v1i.66 ◽

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.

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Auditor Choices during Event Log Building for Process Mining

Journal of Emerging Technologies in Accounting ◽

10.2308/jeta-52496 ◽

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.

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

Business Process Management Journal ◽

10.1108/bpmj-10-2017-0279 ◽

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.

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Metamodel of the Artifact-Centric Approach to Event Log Extraction from ERP Systems

International Journal of Decision Support System Technology ◽

10.4018/ijdsst.2016040102 ◽

2016 ◽

Vol 8 (2) ◽

pp. 18-28 ◽

Cited By ~ 5

Author(s):

Ana Pajić ◽

Dragana Bečejski-Vujaklija

Keyword(s):

Enterprise Resource Planning ◽

Business Processes ◽

Process Mining ◽

Resource Planning ◽

Erp Systems ◽

Huge Amount ◽

Erp System ◽

Event Logs ◽

Event Log ◽

Data Source

Enterprise Resource Planning (ERP) systems handle a huge amount of data related to the actual execution of business processes and the goal is to discover from transaction log a model of how the business processes are actually carried out. The authors' work captures the knowledge of existing approaches and tools in converting the data from transaction logs to event logs for process mining techniques. They conduct a detailed analysis of the artifact-centric approach concepts and describe its constructs by the ontological metamodel. The underlying logical and semantically rich structure of the approach is presented through the model definition. The paper specifies how concepts of the data source are mapped onto the concept of the event log. Dynamics NAV ERP system is used as an example to illustrate the data-oriented structure of ERP system.

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A Cluster Analysis Method of Software Development Activities Based on Event Log

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666191204144931 ◽

2019 ◽

Vol 13 ◽

Author(s):

MingJing Tang ◽

Tong Li ◽

Rui Zhu ◽

ZiFei Ma

Keyword(s):

Software Development ◽

Development Process ◽

Clustering Algorithm ◽

Process Mining ◽

Development Project ◽

Software Development Process ◽

Log Data ◽

Event Logs ◽

Event Log ◽

Process Event

Background: Event log data generated in the software development process contains historical information and future trends of software development activities. The mining and analysis of event log data contribute to identify and discover software development activities and provide effective support for software development process mining and modeling. Method: Firstly, deep learning model (Word2vec) has used for feature extraction and vectorization of software development process event logs. Then, K-means clustering algorithm and silhouette coefficient measure has used for clustering and clustering effect evaluation of vectorized software development process event logs. Results: This paper obtained the mapping relationship between software development activities and events, and realized the identification and discovery of software development activities. Conclusion: A practical software development project (jEdit) is given to prove the feasibility, rationality and effectiveness of our proposed method. This work provides effective support for software development process mining and software development behavior guidance.

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

Accounting Horizons ◽

10.2308/acch-52458 ◽

2019 ◽

Vol 33 (3) ◽

pp. 141-156 ◽

Cited By ~ 3

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.

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Spreadsheets for business process management

Business Process Management Journal ◽

10.1108/bpmj-10-2016-0190 ◽

2018 ◽

Vol 24 (1) ◽

pp. 105-127 ◽

Cited By ~ 8

Author(s):

Wil van der Aalst

Keyword(s):

Process Management ◽

Process Mining ◽

Event Data ◽

Data Set ◽

Content Type ◽

Event Logs ◽

Process Support ◽

Commercial Process ◽

Operational Processes ◽

Mining Tools

Purpose Process mining provides a generic collection of techniques to turn event data into valuable insights, improvement ideas, predictions, and recommendations. This paper uses spreadsheets as a metaphor to introduce process mining as an essential tool for data scientists and business analysts. The purpose of this paper is to illustrate that process mining can do with events what spreadsheets can do with numbers. Design/methodology/approach The paper discusses the main concepts in both spreadsheets and process mining. Using a concrete data set as a running example, the different types of process mining are explained. Where spreadsheets work with numbers, process mining starts from event data with the aim to analyze processes. Findings Differences and commonalities between spreadsheets and process mining are described. Unlike process mining tools like ProM, spreadsheets programs cannot be used to discover processes, check compliance, analyze bottlenecks, animate event data, and provide operational process support. Pointers to existing process mining tools and their functionality are given. Practical implications Event logs and operational processes can be found everywhere and process mining techniques are not limited to specific application domains. Comparable to spreadsheet software widely used in finance, production, sales, education, and sports, process mining software can be used in a broad range of organizations. Originality/value The paper provides an original view on process mining by relating it to the spreadsheets. The value of spreadsheet-like technology tailored toward the analysis of behavior rather than numbers is illustrated by the over 20 commercial process mining tools available today and the growing adoption in a variety of application domains.

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On the Selection of Process Mining Tools

Electronics ◽

10.3390/electronics10040451 ◽

2021 ◽

Vol 10 (4) ◽

pp. 451

Author(s):

Panagiotis Drakoulogkonas ◽

Dimitris Apostolou

Keyword(s):

Computational Intelligence ◽

Process Mining ◽

Software Tool ◽

Software Tools ◽

Analytic Hierarchy ◽

Event Logs ◽

New Process ◽

Hierarchy Process ◽

Mining Tools ◽

Selection Of

Process mining is a research discipline that applies data analysis and computational intelligence techniques to extract knowledge from event logs of information systems. It aims to provide new means to discover, monitor, and improve processes. Process mining has gained particular attention over recent years and new process mining software tools, both academic and commercial, have been developed. This paper provides a survey of process mining software tools. It identifies and describes criteria that can be useful for comparing the tools. Furthermore, it introduces a multi-criteria methodology that can be used for the comparative analysis of process mining software tools. The methodology is based on three methods, namely ontology, decision tree, and Analytic Hierarchy Process (AHP), that can be used to help users decide which software tool best suits their needs.

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