scholarly journals Implementasi Process Mining pada Proses Praktik Kerja Lapangan (PKL)

Techno Com ◽  
2021 ◽  
Vol 20 (4) ◽  
pp. 579-587
Author(s):  
Afina Lina Nurlaili ◽  
- Muhsin ◽  
Eristya Maya Safitri
Keyword(s):  
Process Mining ◽  
Event Log

Proses yang tidak terdokumentasi dapat menjadi masalah apabila tidak ada suatu prosedur yang mendasarinya, khususnya pada proses yang melibatkan banyak aktivitas. Prosedur dibutuhkan untuk mengatur kegiatan dalam mencapai tujuan tertentu. Dalam hal memenuhi tujuan tersebut, dibuatlah panduan berupa Standar Operasional Prosedur (SOP) yang dapat memberikan manfaat bagi organisasi mana pun yang menerapkannya. SOP juga menjadi dasar dalam proses evaluasi antara kenyataan di lapangan dengan prosedur yang telah dibuat ke dalam SOP sehingga diperlukan suatu teknik untuk mengevaluasi antara SOP dengan kenyataan. Process mining merupakan teknik evaluasi antara suatu model proses dengan data peristiwa atau event log yang terdapat dalam sistem informasi. Berbagai metode process mining telah dikenalkan yaitu algoritma Alpha dan Heuristic Miner. Algoritma Alpha dan Heurisctic pada penelitian ini akan dihitung nilai fitness dan presisi. Fitness dilakukan dengan mengukur kesesuaian antara event log dan model proses. Presisi mengukur apakah suatu algoritma tepat untuk menyelesaikan kasus tertentu. Berdasarkan evaluasi yang dilakukan, algoritma Alpha tidak mampu menggambarkan proses sesuai dengan event log PKL. Hal ini disebabkan karena varian kasus mengandung proses loop/perulangan. Hal ini juga menunjukkan event log yang ada pada proses PKL belum menerapkan SOP PKL. Sedangkan Heuristic Miner mengabaikan proses minor menyebabkan proses-proses yang tidak banyak terjadi, tidak digambarkan ke dalam model proses. Secara keseluruhan proses model yang terbentuk menggunakan algoritma Alpha yang paling mendekati dengan kenyataan karena memiliki fitness 0,96.

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.

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

2020 ◽  
Author(s):  
Kwanghoon Kim
Keyword(s):  
Process Model ◽  
Large Scale ◽  
Process Mining ◽  
Functional Integration ◽  
Structural Complexity ◽  
Integrated Approach ◽  
Parallel Process ◽  
Process Models ◽  
Massively Parallel ◽  
Event Log

Process (or business process) management systems fulfill defining, executing, monitoring and managing process models deployed on process-aware enterprises. Accordingly, the functional formation of the systems is made up of three subsystems such as modeling subsystem, enacting subsystem and mining subsystem. In recent times, the mining subsystem has been becoming an essential subsystem. Many enterprises have successfully completed the introduction and application of the process automation technology through the modeling subsystem and the enacting subsystem. According as the time has come to the phase of redesigning and reengineering the deployed process models, from now on it is important for the mining subsystem to cooperate with the analyzing subsystem; the essential cooperation capability is to provide seamless integrations between the designing works with the modeling subsystem and the redesigning work with the mining subsystem. In other words, we need to seamlessly integrate the discovery functionality of the mining subsystem and the analyzing functionality of the modeling subsystem. This integrated approach might be suitable very well when those deployed process models discovered by the mining subsystem are complex and very large-scaled, in particular. In this paper, we propose an integrated approach for seamlessly as well as effectively providing the mining and the analyzing functionalities to the redesigning work on 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. And more precisely, we excogitate a series of functional algorithms for extracting the structural constructs as well as for visualizing the behavioral properness on 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 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 Conformance Checking Techniques of Process Mining: A Survey

2021 ◽  
Author(s):  
Ashok Kumar Saini ◽  
Ruchi Kamra ◽  
Utpal Shrivastava
Keyword(s):  
Information Systems ◽  
Process Model ◽  
Process Mining ◽  
Quality Parameters ◽  
Process Models ◽  
Conformance Checking ◽  
Business Goals ◽  
Key Concepts ◽  
Event Log ◽  
Challenges And Opportunities

Conformance Checking (CC) techniques enable us to gives the deviation between modelled behavior and actual execution behavior. The majority of organizations have Process-Aware Information Systems for recording the insights of the system. They have the process model to show how the process will be executed. The key intention of Process Mining is to extracting facts from the event log and used them for analysis, ratification, improvement, and redesigning of a process. Researchers have proposed various CC techniques for specific applications and process models. This paper has a detailed study of key concepts and contributions of Process Mining. It also helps in achieving business goals. The current challenges and opportunities in Process Mining are also discussed. The survey is based on CC techniques proposed by researchers with key objectives like quality parameters, perspective, algorithm types, tools, and achievements.

scholarly journals An Optimization Approach for Mining of Process Models with Infrequent Behaviors Integrating Data Flow and Control Flow

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Li-li Wang ◽  
Xian-wen Fang ◽  
Esther Asare ◽  
Fang Huan
Keyword(s):  
Process Model ◽  
Data Flow ◽  
Process Mining ◽  
Control Flow ◽  
Process Models ◽  
Frequent Pattern ◽  
Optimization Approach ◽  
Event Log ◽  
Flow Information ◽  
And Control

Infrequent behaviors of business process refer to behaviors that occur in very exceptional cases, and their occurrence frequency is low as their required conditions are rarely fulfilled. Hence, a strong coupling relationship between infrequent behavior and data flow exists. Furthermore, some infrequent behaviors may reveal very important information about the process. Thus, not all infrequent behaviors should be disregarded as noise, and identifying infrequent but correct behaviors in the event log is vital to process mining from the perspective of data flow. Existing process mining approaches construct a process model from frequent behaviors in the event log, mostly concentrating on control flow only, without considering infrequent behavior and data flow information. In this paper, we focus on data flow to extract infrequent but correct behaviors from logs. For an infrequent trace, frequent patterns and interactive behavior profiles are combined to find out which part of the behavior in the trace occurs in low frequency. And, conditional dependency probability is used to analyze the influence strength of the data flow information on infrequent behavior. An approach for identifying effective infrequent behaviors based on the frequent pattern under data awareness is proposed correspondingly. Subsequently, an optimization approach for mining of process models with infrequent behaviors integrating data flow and control flow is also presented. The experiments on synthetic and real-life event logs show that the proposed approach can distinguish effective infrequent behaviors from noise compared with others. The proposed approaches greatly improve the fitness of the mined process model without significantly decreasing its precision.

scholarly journals Process Mining: Measuring Key Performance Indicator Container Dwell Time

2019 ◽  
Vol 16 (1) ◽  
pp. 401
Author(s):  
Bambang Jokonowo ◽  
Riyanarto Sarno ◽  
Siti Rochimah ◽  
Bagus Priambodo
Keyword(s):  
Dwell Time ◽  
Process Model ◽  
Performance Indicator ◽  
Process Mining ◽  
Key Performance Indicator ◽  
Life Cycle Model ◽  
Cycle Model ◽  
Knowledge Process ◽  
Event Log ◽  
Process Behavior

<span>The issues measures duration of stay the container logistic processes at ports in developing countries is often a major problem. Therefore, a knowledge process discovery, i.e., Heuristics Miner and Fuzzy Miner, can be used to discover the insight of process by creating a process model. The container import dwell time (DT) processes can be modeled based on the event log data sources are extracted from the terminal operating system (TOS). The <em>L</em>* life-cycle model is used to perform the process behavior analysis steps. The results of analysis and verification show that the container import DT processes have a median duration of 5.5 days and a mean duration of 6.07 days.</span>

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