• Journal of KIISE:Computing Practices and Letters
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• v.8 no.5
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• pp.489-500
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• 2002

As the interest in business interest is increased, data mining is increasingly used in BI as the core technique. To support Business Intelligence in e-business environment, the integrated data mining system which included in various mining operations should be able to flexibly integrate with database system and also it must provide the easy and efficient interface to implement the marketing process in various business applications. In this paper, we have implemented the EC-DaMiner system to support business intelligence in e-business area. The implemented system can be integrated with the conventional database system with the standard interface. Business applications can use MQL mining query language to discover the rules and mining result is modeled in marketing database, and the EC-DaMiner system make the implementation of business marketing process more easy.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.231-234
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• 2004

본 논문에서 리눅스 프로세스들의 패턴들(정상행위 와 비정상행위)을 학습하고 그 밖에 예비 시험들의 확장을 제시하는데 의가 있다고 할 수 있다. 패턴들은 리눅스 시스템들 안에 오용과 침입들을 확인 할 수 있도록 사용하였다. 리눅스 sendmail 프로세스의 처리의 정상행위 그리고 비정상 행위들을 위해 운영체제 호출 순차들에서 기계 학습 작업을 고안하였다. 이 방법은 테스트 기록 데이터의 정상행위로부터 sendmail의 비정상행위의 실행을 모두 정확하게 구별할 수 있는 것을 보여준다. 예비 시험들은 기계학습이 침입탐지 서비스를 제공하기 위하여 저장 된 순차 정보를 추출화 함으로써 중요한 역할을 다 할 수 있다는 것으로 나타냈다.

• The Journal of the Korea Contents Association
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• v.17 no.7
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• pp.44-55
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• 2017

Many enterprises have recently been pursuing process innovation or improvement to attain their performance goal. To comprehensively support business process execution, the concept of business process management (BPM) has been widely adopted. A life cycle of BPM is composed of process diagnosis, (re)design, and enactment. For aligning with enterprise strategies, all BPM activities must be closely related to performance metrics because the metrics are the drivers and evaluators of business process operations. The objective of this paper is to propose a life cycle-based BPM framework integrated with the process-based performance measurement model, in which business processes are systematically interrelated with key performance indicators (KPIs) during an entire BPM life cycle. By using the proposed BPM framework, company practitioners involved in process innovation projects can easily and efficiently find the most influencing processes upon enterprise performance in the process diagnosis phase, evaluate the performance of newly designed process in the process (re)design phase, monitor the KPIs of new business process, and adjust business process activities in the process execution phase through the BPM life cycle.

• Journal of the Society of Naval Architects of Korea
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• v.56 no.2
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• pp.143-151
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• 2019

This study describes a method for analyzing log data generated in a process using process mining techniques. A system for collecting and analyzing a large amount of log data generated in the process of manufacturing an offshore plant piping material was constructed. The analyzed data was visualized through various methods. Through the analysis of the process model, it was evaluated whether the process performance was correctly input. Through the pattern analysis of the log data, it is possible to check beforehand whether the problem process occurred. In addition, we analyzed the process performance data of partner companies and identified the load of their processes. These data can be used as reference data for pipe production allocation. Real-time decision-making is required to cope with the various variances that arise in offshore plant production. To do this, we have built a system that can analyze the log data of real - time system and make decisions.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.723-732
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• 2022

The success of enterprise information system(EIS) is crucial to align with corporate strategies and eventually attain corporate goals. Since one of the factors to information system success is system use, managerial efforts to measure the level of EIS utilization is vital. In this paper, the EIS utilization level is analyzed using system access log data. In particular, process sequence patterns and clustering of similar functions are identified in more detail based on a process mining method, in addition to basic access log statistics. The result of this research can be used to improve existing information system design by finding real IS usage sequences and function clusters.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.427-428
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• 2021

정보통신기술의 발달로 전자상거래의 증가와 소비자들의 제품에 대한 경험과 지식의 공유가 활발하게 진행됨에 따라 소비자는 제품을 구매하기 위한 자료수집, 활용을 진행하고 있다. 따라서 기업은 다양한 기능들을 반영한 제품이 치열하게 경쟁하고 있는 현 시장에서 우위를 점하고자 소비자 리뷰를 분석하여 소비자의 정확한 소비자의 요구사항을 분석하여 제품기획 프로세스에 반영하고자 텍스트마이닝(Text Mining) 기술과 딥러닝(Deep Learning) 기술을 통한 연구가 이루어지고 있다. 본 논문의 기초자료가 되는 데이터셋은 포털사이트의 구매사이트와 오픈마켓 사이트의 소비자 리뷰를 웹크롤링하고 자연어처리하여 진행한다. 감성분석은 딥러닝기술 중 CNN(Convolutional Neural Network), LSTM(Long Short Term Memory) 조합의 모델을 구현한다. 이는 딥러닝을 이용한 제품기획 프로세스로 소비자 요구사항 반영, 경제적인 측면, 제품기획 시간단축 등 긍정적인 영향을 미칠 것으로 기대한다.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.83-92
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• 2020

Recently, many companies and organizations are interested in predictive process monitoring for the efficient operation of business process models. Traditional process monitoring focused on the elapsed execution state of a particular process instance. On the other hand, predictive process monitoring focuses on predicting the future execution status of a particular process instance. In this paper, we implement the function of the business process remaining time prediction, which is one of the predictive process monitoring functions. In order to effectively model the remaining time, normalization by activity is proposed and applied to the predictive model by taking into account the difference in the distribution of time feature values according to the properties of each activity. In order to demonstrate the superiority of the predictive performance of the proposed model in this paper, it is compared with previous studies through event log data of actual companies provided by 4TU.Centre for Research Data.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.35-52
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• 2019

As public services are provided in various forms, including e-government, the level of public demand for public service quality is increasing. Although continuous measurement and improvement of the quality of public services is needed to improve the quality of public services, traditional surveys are costly and time-consuming and have limitations. Therefore, there is a need for an analytical technique that can measure the quality of public services quickly and accurately at any time based on the data generated from public services. In this study, we analyzed the quality of public services based on data using process mining techniques for civil licensing services in N city. It is because the N city's building license complaint service can secure data necessary for analysis and can be spread to other institutions through public service quality management. This study conducted process mining on a total of 3678 building license complaint services in N city for two years from January 2014, and identified process maps and departments with high frequency and long processing time. According to the analysis results, there was a case where a department was crowded or relatively few at a certain point in time. In addition, there was a reasonable doubt that the increase in the number of complaints would increase the time required to complete the complaints. According to the analysis results, the time required to complete the complaint was varied from the same day to a year and 146 days. The cumulative frequency of the top four departments of the Sewage Treatment Division, the Waterworks Division, the Urban Design Division, and the Green Growth Division exceeded 50% and the cumulative frequency of the top nine departments exceeded 70%. Higher departments were limited and there was a great deal of unbalanced load among departments. Most complaint services have a variety of different patterns of processes. Research shows that the number of 'complementary' decisions has the greatest impact on the length of a complaint. This is interpreted as a lengthy period until the completion of the entire complaint is required because the 'complement' decision requires a physical period in which the complainant supplements and submits the documents again. In order to solve these problems, it is possible to drastically reduce the overall processing time of the complaints by preparing thoroughly before the filing of the complaints or in the preparation of the complaints, or the 'complementary' decision of other complaints. By clarifying and disclosing the cause and solution of one of the important data in the system, it helps the complainant to prepare in advance and convinces that the documents prepared by the public information will be passed. The transparency of complaints can be sufficiently predictable. Documents prepared by pre-disclosed information are likely to be processed without problems, which not only shortens the processing period but also improves work efficiency by eliminating the need for renegotiation or multiple tasks from the point of view of the processor. The results of this study can be used to find departments with high burdens of civil complaints at certain points of time and to flexibly manage the workforce allocation between departments. In addition, as a result of analyzing the pattern of the departments participating in the consultation by the characteristics of the complaints, it is possible to use it for automation or recommendation when requesting the consultation department. In addition, by using various data generated during the complaint process and using machine learning techniques, the pattern of the complaint process can be found. It can be used for automation / intelligence of civil complaint processing by making this algorithm and applying it to the system. This study is expected to be used to suggest future public service quality improvement through process mining analysis on civil service.

• The Journal of Bigdata
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• v.1 no.2
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• pp.9-19
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• 2016

Since ICT convergence became a major issue, German government has carried forward a policy 'Industry 4.0' that triggered ICT convergence with manufacturing. Now this trend gets into our stride. From this facts, we can expect great leap up to quality perfection in low cost. Recently Korean government also enforces policy with 'Manufacturing 3.0' for upgrading Korean manufacturing industry with being accelerated by many related technologies. We, in the paper, developed a custom-made operational big data analysis platform for the implementation of operational intelligence to improve industry capability. Our platform is designed based on spring framework and web. In addition, HDFS and spark architectures helps our system analyze massive data on the field with streamed data processed by process mining algorithm. Extracted knowledge from data will support enhancement of manufacturing performance.

• The Journal of the Korea Contents Association
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• v.14 no.10
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• pp.10-19
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• 2014

In recent, hospital information systems are widely used to electronically record, manage and share the data collected in hospitals. Such systems have contributed greatly to improving the work efficiency in modern hospitals, however, the collected data concerning the patients should be appropriately processed and reused to provide the healthcare service providers with decision supports. Especially, this paper proposes the patient flow monitoring system for the operations management of the outpatient department for patients with chronic diseases, and discusses the related issues. The proposed system visualizes the standard process model extracted from the patient history data and various performance measures, and this enables the managers to evaluate and enhance the operations of the outpatient clinic. In this paper, the patient flow monitoring system is applied to the rheumatology clinic, and the prototype system optimized for I-pad is illustrated.