scholarly journals Analisis Kualitas Model Proses dalam Implementasi Process Mining : Literature Review

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Afina Lina Nurlaili ◽  
Agung Mustika Rizki
Keyword(s):  
Literature Review ◽  
Process Mining ◽  
Event Log

Proses bisnis memiliki peran dalam mengatur pola kerja suatu organisasi. Kerumitan dalam membuat proses bisnis menjadi tantangan tersendiri, terutama jika pekerjaan yang dicakup relatif banyak. Process mining menjadi salah satu solusi yang paling handal dengan secara otomatis dapat menemukan model proses, menganalisis kualitas model proses hingga dapat meningkatkan kualitas model proses dari data event log yang tersimpan pada sistem informasi. Penelitian ini mengevaluasi tiga algoritma process mining, yaitu Alpha, Alpha++, dan Heuristic Miner dalam hal pembuatan model proses dan kualitasnya. Berdasarkan evaluasi yang dilakukan, algoritma Alpha, Alpha++, dan Heuristic Miner memiliki kecocokan tersendiri untuk kasusnya masing-masing.

Process Mining Perspectives in Software Engineering: A Systematic Literature Review

2021 ◽  
Author(s):  
Silvia Jaqueline Urrea-Contreras ◽  
Brenda L. Flores-Rios ◽  
Maria Angelica Astorga-Vargas ◽  
Jorge E. Ibarra-Esquer
Keyword(s):  
Software Engineering ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Process Mining

scholarly journals Role of Stochastic Petri Net (SPN) in Process Discovery for Modelling and Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Shabnam Shahzadi ◽  
Xianwen Fang ◽  
David Anekeya Alilah
Keyword(s):  
Petri Net ◽  
Business Processes ◽  
Time Perspective ◽  
Process Mining ◽  
Vital Role ◽  
Process Discovery ◽  
Stochastic Petri Net ◽  
Event Log ◽  
The Stability ◽  
Generalized Stochastic Petri Net

For exploitation and extraction of an event’s data that has vital information which is related to the process from the event log, process mining is used. There are three main basic types of process mining as explained in relation to input and output. These are process discovery, conformance checking, and enhancement. Process discovery is one of the most challenging process mining activities based on the event log. Business processes or system performance plays a vital role in modelling, analysis, and prediction. Recently, a memoryless model such as exponential distribution of the stochastic Petri net SPN has gained much attention in research and industry. This paper uses time perspective for modelling and analysis and uses stochastic Petri net to check the performance, evolution, stability, and reliability of the model. To assess the effect of time delay in firing the transition, stochastic reward net SRN model is used. The model can also be used in checking the reliability of the model, whereas the generalized stochastic Petri net GSPN is used for evaluation and checking the performance of the model. SPN is used to analyze the probability of state transition and the stability from one state to another. However, in process mining, logs are used by linking log sequence with the state and, by this, modelling can be done, and its relation with stability of the model can be established.

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.

How is process mining technology used by organizations? A systematic literature review of empirical studies

2018 ◽  
Vol 24 (4) ◽  
pp. 900-922 ◽  
Author(s):  
Malte Thiede ◽  
Daniel Fuerstenau ◽  
Ana Paula Bezerra Barquet
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Enterprise Resource Planning ◽  
Process Mining ◽  
Empirical Studies ◽  
Resource Planning ◽  
Classification Model ◽  
Organizational Process ◽  
Content Type ◽  
Cross System

Purpose The purpose of this paper is to review empirical studies on process mining in order to understand its use by organizations. The paper further aims to outline future research opportunities. Design/methodology/approach The authors propose a classification model that combines core conceptual elements of process mining with prior models from technology classification from the enterprise resource planning and business intelligence field. The model incorporates an organizational usage, a system-orientation and service nature, adding a focus on physical services. The application is based on a systematic literature review of 144 research papers. Findings The results show that, thus far, the literature has been chiefly concerned with realization of single business process management systems in single organizations. The authors conclude that cross-system or cross-organizational process mining is underrepresented in the ISR, as is the analysis of physical services. Practical implications Process mining researchers have paid little attention to utilizing complex use cases and mining mixed physical-digital services. Practitioners should work closely with academics to overcome these knowledge gaps. Only then will process mining be on the cusp of becoming a technology that allows new insights into customer processes by supplying business operations with valuable and detailed information. Originality/value Despite the scientific interest in process mining, particularly scant attention has been given by researchers to investigating its use in relatively complex scenarios, e.g., cross-system and cross-organizational process mining. Furthermore, coverage on the use of process mining from a service perspective is limited, which fails to reflect the marketing and business context of most contemporary organizations, wherein the importance of such scenarios is widely acknowledged. The small number of studies encountered may be due to a lack of knowledge about the potential of such scenarios as well as successful examples, a situation the authors seek to remedy with this study.

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.

Discovering Process Horizontal Boundaries to Facilitate Process Comprehension

2018 ◽  
Vol 9 (2) ◽  
pp. 1-31 ◽  
Author(s):  
Pavlos Delias ◽  
Kleanthi Lakiotaki
Keyword(s):  
Process Model ◽  
Process Mining ◽  
A Priori ◽  
Quality Criteria ◽  
Large Set ◽  
Process Discovery ◽  
Event Log ◽  
Human Interpretation ◽  
Automated Discovery ◽  
Priori Information

Automated discovery of a process model is a major task of Process Mining that means to produce a process model from an event log, without any a-priori information. However, when an event log contains a large number of distinct activities, process discovery can be real challenging. The goal of this article is to facilitate process discovery in such cases when a process is expected to contain a large set of unique activities. To this end, this article proposes a clustering approach that recommends horizontal boundaries for the process. The proposed approach ultimately partitions the event log in a way that human interpretation efforts are decomposed. In addition, it makes automated discovery more efficient as well as effective by simultaneously considering two quality criteria: informativeness and robustness of the derived groups of activities. The authors conducted several experiments to test the behavior of the algorithm under different settings, and to compare it against other techniques. Finally, they provide a set of recommendations that may help process analysts during the process discovery endeavor.

scholarly journals Functional Integration with Process Mining and Process Analyzing for Structural and Behavioral Properness Validation of Processes Discovered from Event Log Datasets

2020 ◽  
Vol 10 (4) ◽  
pp. 1493 ◽  
Author(s):  
Kwanghoon Pio Kim
Keyword(s):  
Process Model ◽  
Large Scale ◽  
Process Mining ◽  
Functional Integration ◽  
Structural Complexity ◽  
Integrated Approach ◽  
Parallel Process ◽  
Process Models ◽  
Massively Parallel ◽  
Event Log

In this paper, we propose an integrated approach for seamlessly and effectively providing the mining and the analyzing functionalities to redesigning work for very large-scale and massively parallel process models that are discovered from their enactment event logs. The integrated approach especially aims at analyzing not only their structural complexity and correctness but also their animation-based behavioral properness, and becomes concretized to a sophisticated analyzer. The core function of the analyzer is to discover a very large-scale and massively parallel process model from a process log dataset and to validate the structural complexity and the syntactical and behavioral properness of the discovered process model. Finally, this paper writes up the detailed description of the system architecture with its functional integration of process mining and process analyzing. More precisely, we excogitate a series of functional algorithms for extracting the structural constructs and for visualizing the behavioral properness of those discovered very large-scale and massively parallel process models. As experimental validation, we apply the proposed approach and analyzer to a couple of process enactment event log datasets available on the website of the 4TU.Centre for Research Data.

scholarly journals Event abstraction in process mining: literature review and taxonomy

2020 ◽  
Author(s):  
Sebastiaan J. van Zelst ◽  
Felix Mannhardt ◽  
Massimiliano de Leoni ◽  
Agnes Koschmider
Keyword(s):  
Literature Review ◽  
Process Mining
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