scholarly journals Analisis Kesiapan Penerapan Process Mining pada Sistem Manajemen Pembelajaran Universitas Telkom

2021 ◽  
Vol 8 (6) ◽  
pp. 1227
Author(s):  
Angelina Prima Kurniati ◽  
Gede Agung Ary Wisudiawan
Keyword(s):  
Feasibility Study ◽  
Learning Process ◽  
Data Analytics ◽  
Process Mining ◽  
Process Models ◽  
Computer Based Learning ◽  
Event Log ◽  
User Access ◽  
Learning Management ◽  
Access Logs

<p>Sistem manajemen pembelajaran (<em>Learning Management System/ LMS</em>) berbasis komputer telah banyak digunakan untuk mengelola pembelajaran dalam institusi pendidikan, termasuk universitas. LMS merekam dan mengelola akses pengguna secara otomatis dalam bentuk <em>event log</em>. Data dalam <em>event log</em> tersebut dapat dianalisis untuk mengenali pola penggunaan LMS sebagai pertimbangan pengembangan LMS. Salah satu metode yang dapat diadopsi adalah <em>process mining</em>, yaitu menganalisis data <em>event log</em> berbasis proses. Analisis data berbasis proses ini bertujuan untuk memodelkan proses yang terjadi dan terekam dalam LMS, mengecek kesesuaian pelaksanaan proses dengan prosedur, dan mengusulkan pengembangan proses di masa mendatang. Makalah ini mengeksplorasi kesiapan data penggunaan LMS di Universitas Telkom sebagai subjek penelitian untuk dianalisis dengan pendekatan <em>process mining</em>. Sepanjang pengetahuan kami, belum ada penelitian sebelumnya yang melakukan analisis data berbasis proses pada LMS ini. Kontribusi penelitian ini adalah eksplorasi peluang untuk menganalisis proses pembelajaran dan pengembangan metode pembelajaran berbasis LMS. Analisis kesiapan LMS dilakukan berdasarkan daftar pengecekan komponen yang dibutuhkan dalam <em>process mining</em>. Makalah ini mengikuti tahap-tahap utama dalam <em>Process Mining Process Methodology</em> (PM<sup>2</sup>). Studi kasus yang dieksplorasi adalah proses pembelajaran pada satu mata kuliah dalam satu semester berdasarkan <em>event log </em>yang diekstrak dari LMS. Hasil penelitian ini menunjukkan bahwa analisis data dalam LMS ini dapat digunakan untuk menganalisis performansi pembelajaran di Universitas Telkom dari kelompok pengguna yang berbeda-beda dan dapat dikembangkan untuk menganalisis data pada studi kasus yang lebih besar. Studi kelayakan ini diakhiri dengan diskusi tentang kelayakan LMS untuk dianalisis dengan <em>process mining</em>, evaluasi oleh tim ahli LMS, dan usulan pengembangan LMS di masa mendatang. <em></em></p><p> </p><p><em><strong>Abstract</strong></em></p><p><em><em>Computer-based Learning Management Systems (LMS) are commonly used in educational institutions, including universities. An LMS records and manages user access logs in an event log. Data in an event log can be analysed to understand patterns in the LMS usage to support recommendations for improvements. One promising method is process mining, which is a process-based data analytics working on event logs. Process mining aims to discover process models as recorded in the LMS, conformance checking of process execution to the defined procedure, and suggest improvements. This paper explores the feasibility of Telkom University LMS usage data to be analysed using process mining. To the best of our knowledge, there was no previous research doing process-based data analytics on this LMS. This paper contributes to explore opportunities to analyse learning processes and enhance LMS-based learning methods. The feasibility study is based on a data component checklist for process mining. This paper is written following the main stages on the Process Mining Project Methodology (PM2). We explore a case study of the learning process of a course in a semester, based on an event log extracted from the LMS. The results show that data analytics on this LMS can be used to analyse learning process performance in Telkom University, based on different user roles. This feasibility study is concluded with a discussion on the feasibility of the LMS to be analysed using process mining, an evaluation by the representative of the LMS expert team, and a recommendation for improvements.</em></em></p>

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 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-technieken voor internecontroletesten - mogelijkheden nu en in de toekomst

2017 ◽  
Vol 91 (3/4) ◽  
pp. 90-95 ◽  
Author(s):  
Mieke Jans ◽  
Marzie Hosseinpour
Keyword(s):  
Data Analytics ◽  
Process Mining ◽  
Wordt Door ◽  
Event Log

‘Data analytics' en ‘accounting' zijn termen die steeds vaker in combinatie worden gebruikt. Zowel van de financiële rapportering als van de processen die leiden tot deze rapportering worden steeds meer gegevens opgeslagen. Dat data-analyse een toegevoegde waarde kan bieden aan accounting, wordt door steeds meer partijen aangenomen. Hoe deze toegevoegde waarde concreet bereikt kan worden, is echter minder duidelijk. In dit artikel wordt concreet ingegaan op het perspectief van internecontroletesten en process mining, een subset van data-analysetechnieken. Enerzijds worden concrete activiteiten geïdentificeerd in het proces van interne beheersing, die ondersteund zouden kunnen worden door process mining- algoritmes. Dit is vooral voor het vergelijken van werkelijke uitvoeringen met een verwacht procesmodel. Anderzijds worden de wetenschappelijke uitdagingen die hiermee gepaard gaan toegelicht: 1) de impact van de event log-structuur op controletesten en 2) de classificatie van procesafwijkingen, zodat een volledige analyse haalbaar wordt.

scholarly journals How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study

2019 ◽  
Vol 14 (2) ◽  
pp. 189-213
Author(s):  
Cindy Paans ◽  
Erdem Onan ◽  
Inge Molenaar ◽  
Ludo Verhoeven ◽  
Eliane Segers
Keyword(s):  
Cognitive Processing ◽  
Learning Process ◽  
Process Mining ◽  
Process Models ◽  
Vicious Cycle ◽  
Social Challenges ◽  
Social Challenge ◽  
Low Levels ◽  
Sequential Analyses

Abstract The present study investigated the extent to which 18 dyads in 5th and 6th grade, who experienced low levels of social challenge, differed from 12 dyads who experience high levels of social challenge in terms of the quality of their written assignment, as well as the frequency and sequential pattern of their cognitive, metacognitive, relational, and off-task activities during a collaborative hypermedia assignment. Sequential analyses were performed by means of process mining with a fuzzy miner algorithm. Results showed that assignment quality was higher for low social challenge dyads. In addition, these more successful dyads showed more cognitive processing activities, more high-cognition, and fewer off-task activities. In terms of their process models, low and high challenge dyads showed marked differences. More specifically, high social challenge dyads showed a vicious cycle of social challenges and off-task behaviors, whereas low social challenge dyads engaged in high-cognition. In addition, for low challenge dyads, but not high challenge dyads, the various metacognitive activities were closely connected to each other. These findings indicate that social challenges not only affect assignment quality, but also fundamentally affect the overall learning process.

scholarly journals Process Discovery of Business Processes Using Temporal Causal Relation

2019 ◽  
Vol 5 (2) ◽  
pp. 183
Author(s):  
Yutika Amelia Effendi ◽  
Nania Nuzulita
Keyword(s):  
Business Process ◽  
Business Processes ◽  
Process Mining ◽  
Causal Relation ◽  
Real Life ◽  
Process Models ◽  
Time Interval ◽  
Start Time ◽  
Process Discovery ◽  
Event Log

Background: Nowadays, enterprise computing manages business processes which has grown up rapidly. This situation triggers the production of a massive event log. One type of event log is double timestamp event log. The double timestamp has a start time and complete time of each activity executed in the business process. It also has a close relationship with temporal causal relation. The temporal causal relation is a pattern of event log that occurs from each activity performed in the process.Objective: In this paper, seven types of temporal causal relation between activities were presented as an extended version of relations used in the double timestamp event log. Since the event log was not always executed sequentially, therefore using temporal causal relation, the event log was divided into several small groups to determine the relations of activities and to mine the business process.Methods: In these experiments, the temporal causal relation based on time interval which were presented in Gantt chart also determined whether each case could be classified as sequential or parallel relations. Then to obtain the business process, each temporal causal relation was combined into one business process based on the timestamp of activity in the event log.Results: The experimental results, which were implemented in two real-life event logs, showed that using temporal causal relation and double timestamp event log could discover business process models.Conclusion: Considering the findings, this study concludes that business process models and their sequential and parallel AND, OR, XOR relations can be discovered by using temporal causal relation and double timestamp event log.Keywords:Business Process, Process Discovery, Process Mining, Temporal Causal Relation, Double Timestamp Event Log

scholarly journals The Log Skeleton Visualizer in ProM 6.9

2021 ◽  
Author(s):  
H. M. W. Verbeek
Keyword(s):  
Process Mining ◽  
Process Models ◽  
Process Discovery ◽  
Event Log ◽  
Sequential Information ◽  
Set Up ◽  
The Moment ◽  
Workflow Net

AbstractProcess discovery is an important area in the field of process mining. To help advance this area, a process discovery contest (PDC) has been set up, which allows us to compare different approaches. At the moment of writing, there have been three instances of the PDC: in 2016, in 2017, and in 2019. This paper introduces the winning contribution to the PDC 2019, called the Log Skeleton Visualizer. This visualizer uses a novel type of process models called log skeletons. In contrast with many workflow net-based discovery techniques, these log skeletons do not rely on the directly follows relation. As a result, log skeletons offer circumstantial information on the event log at hand rather than only sequential information. Using this visualizer, we were able to classify 898 out of 900 traces correctly for the PDC 2019 and to win this contest.

Experimental Setup and Case Study Example

2020 ◽  
pp. 168-180
Keyword(s):  
Real Time ◽  
Learning Process ◽  
Process Mining ◽  
Process Models ◽  
Experimental Setup ◽  
Main Components ◽  
The Individual

This chapter represents as a practical follow-up or implementation of the main components of the SPMaAF described in Chapter 5. In the experimental setup, the chapter demonstrates by using the case study of the learning process: the development and application of the semantic-based process mining. Essentially, the chapter looks at how the proposed semantic-based process mining and analysis framework (SPMaAF) is applied to answer real-time questions about any given process domain, as well as the classification of the individual process instances or elements that constitutes process models. This includes the semantic representations and modelling of the learning process in order to allow for an abstraction analysis of the resultant models. The chapter finalizes with a conceptual description of the resultant semantic fuzzy mining approach which is discussed in detail in the next chapter.

scholarly journals Event Log Preprocessing for Process Mining: A Review

2021 ◽  
Vol 11 (22) ◽  
pp. 10556
Author(s):  
Heidy M. Marin-Castro ◽  
Edgar Tello-Leal
Keyword(s):  
Business Process ◽  
Process Model ◽  
Process Mining ◽  
Data Cleaning ◽  
Data Preprocessing ◽  
Process Models ◽  
Event Data ◽  
Event Logs ◽  
Event Log ◽  
Preprocessing Technique

Process Mining allows organizations to obtain actual business process models from event logs (discovery), to compare the event log or the resulting process model in the discovery task with the existing reference model of the same process (conformance), and to detect issues in the executed process to improve (enhancement). An essential element in the three tasks of process mining (discovery, conformance, and enhancement) is data cleaning, used to reduce the complexity inherent to real-world event data, to be easily interpreted, manipulated, and processed in process mining tasks. Thus, new techniques and algorithms for event data preprocessing have been of interest in the research community in business process. In this paper, we conduct a systematic literature review and provide, for the first time, a survey of relevant approaches of event data preprocessing for business process mining tasks. The aim of this work is to construct a categorization of techniques or methods related to event data preprocessing and to identify relevant challenges around these techniques. We present a quantitative and qualitative analysis of the most popular techniques for event log preprocessing. We also study and present findings about how a preprocessing technique can improve a process mining task. We also discuss the emerging future challenges in the domain of data preprocessing, in the context of process mining. The results of this study reveal that the preprocessing techniques in process mining have demonstrated a high impact on the performance of the process mining tasks. The data cleaning requirements are dependent on the characteristics of the event logs (voluminous, a high variability in the set of traces size, changes in the duration of the activities. In this scenario, most of the surveyed works use more than a single preprocessing technique to improve the quality of the event log. Trace-clustering and trace/event level filtering resulted in being the most commonly used preprocessing techniques due to easy of implementation, and they adequately manage noise and incompleteness in the event logs.

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