• Information Systems Review
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• v.20 no.1
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• pp.81-98
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• 2018

ERP supports the automation and integration of business processes of enterprises and records voluminous data about the business activities of enterprises. The academe and business enterprises are focusing on process mining, which improves the performance of business processes and strengthens compliance. However, these studies focused on analysis of the business process of large companies, which adopts foreign ERP, such as SAP ERP or Oracle ERP. In comparison with foreign ERP, domestic ERP lags behind in terms of logging and managing of event data. Therefore, the application of process mining to domestic ERP is a challenging task. The present study aims to analyze domestic ERP based on process mining to overcome this challenge. This study discusses the lessons learned from a case study in a Korean cosmetics manufacturing company. Our results are expected to strengthen the competitiveness of Korean small and medium-sized enterprises that adopt domestic ERP and realize the outcomes of the large investment of the Korean government on the ERP implementation of enterprises.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.05a
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• pp.266-273
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• 2005

Data mining techniques enable us to generate useful information for decision support from the data sources which are generated and accumulated in the process of routine organizational management activities. College administration system is a typical example that produces a warehouse of student records as each and every student enters a college and undertakes the curricular and extracurricular activities. So far, these data have been utilized to a very limited student service purposes, such as issuance of transcripts, graduation evaluation, GPA calculation, etc. In this paper, we utilize Holland career search test results, TOEIC score, course work list, and GPA score as the input for data mining and generation the student advisory information. Factor analysis, AHP(Analytic Hierarchy Process), artificial neural net, and CART(Classification And Regression Tree) techniques are deployed in the data mining process. Since these data mining techniques are very powerful in processing and discovering useful knowledge and information from large scale student databases, we can expect a highly sophisticated student advisory knowledge and services which may not be obtained with the human student advice experts.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.1
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• pp.57-61
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• 2009

To increase the efficiency of mining process and derive accurate spatio-temporal patterns, continuous values of attributes should be discretized prior to mining process. In this paper, we propose a discretization method which improves the mining efficiency by reducing the data size without losing the correlations in the data. The proposed method first s original trajectories into approximations using line simplification and then groups them into similar clusters. Our experiments show that the proposed approach improves the mining efficiency as well as extracts more intuitive patterns compared to existing discretization methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.390-395
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• 2004

A semiconductor manufacturing process is the complicate and dynamic process, and consists of many sub-processes. An etching process is the most important process in the semiconductor fabrication. In this paper, the decision support system based upon data mining and knowledge discovery is an important factor to improve the productivity and yield. The proposed decision support system consists of a neural network model and an inference system based on fuzzy logic Firstly, the product results are predicted by the neural network model constructed by the product patterns that represent the quality of the etching process. And the product patters are classified by expert's knowledge. Finally, the product conditions are estimated by the fuzzy inference system using the rules extracted from the classified patterns. Prediction of product qualities can be linked to each input and process variables. We employ data mining and intelligent techniques to find the best condition of the etching process. The proposed decision support system is efficient and easy to be implemented for the process management based upon expert's knowledge.

• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.115-120
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• 2018

A Clinical Pathway(CP) is standard process to way of treat diseases or injuries which is adapted to each hospital based on National Clinical Practice Guideline(CPG). Since CP is standard guideline for doctors and nurses working in a hospital, making and modifying CP is one of the most important administrational work for hospital and also rare work because once it is fixed, it's not changed whether there are new kind of disease discovered or new treatment is developed. However, in present, patient's waiting time during hospital residence process, is discussed as service competitive for patients. In this research, we utilize process mining tool to verify patients treatment process follows CP with EMR(Electronic Medical Record) in a sample hospital, and suggest modifcation point of CP through verification.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1141-1151
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• 2008

Data mining is the process of sorting through large amounts of data and picking out useful information. One of the well-studied problems in data mining is the exploration of association rules. An association rule technique finds the relation among each items in massive volume database. Some interestingness measures have been developed in association rule mining. Interestingness measures are useful in that it shows the causes for pruning uninteresting rules statistically or logically. This paper propose a conditional pure confidence to evaluate association rules and then describe some properties for a proposed measure. The comparative studies with confidence and pure confidence are shown by numerical example. The results show that the conditional pure confidence is better than confidence or pure confidence.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.123-131
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• 2016

Process mining in big data environment utilize a number of data were generated from the business process. It generates lots of knowledge and insights regarding implementation and improvement of the process through the event log of the company's enterprise resource planning (ERP) system. In recent years, various research activities engaged with the audit work of company organizations are trying actively by using the maximum strength of the mining process. However, domestic studies on applicable sales auditing system for the process mining are insufficient under big data environment. Therefore, we propose process-mining methods that can be optimally applied to online and traditional auditing system. In advance, we propose continuous monitoring information system that can early detect and prevent the risk under the big data environment by monitoring risk factors in the organizations of enterprise. The scope of the research of this paper is to design a pre-verification system for risk factor via practical examples in sales auditing. Furthermore, realizations of preventive audit, continuous monitoring for high risk, reduction of fraud, and timely action for violation of rules are enhanced by proposed sales auditing system. According to the simulation results, avoidance of financial risks, reduction of audit period, and improvement of audit quality are represented.

• Journal of the Korean Home Economics Association
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• v.43 no.3 s.205
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• pp.1-10
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• 2005

The purpose of this study was to investigate the enforcement strategy for Consumer Service marketing of an e-Commerce shopping mall. An e-CRM for a Cosmetic e-Commerce shopping mall, Data Warehousing(DW) component, analysis of data mining of the DW, and web applications and strategies had to developed for marketing of consumer service satisfaction. The major findings were as follows: An RFM analysis was used for consumer classification, which is a fundamental process of e-CRM application. The components of the DW were web sales data and consumer data fields. The visual process of consumer segmentations (superior consumer class) for e-CRM solutions is presented. The association analysis algorithm of data mining to up-selling and cross-selling indicates an association rule. These e-CRM results apply web DB marketing and operating principles to a shopping mall. Therefore, the system applications of e-CRM to Consumer services indicate a marketing strategy for consumer-oriented management.

• Journal of Korean Academy of Nursing
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• v.36 no.4
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• pp.652-661
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• 2006

Purpose. The purposes of this study were to apply data mining tool to nursing specific knowledge discovery process and to identify the utilization of data mining skill for clinical decision making. Methods. Data mining based on rough set model was conducted on a large clinical data set containing NMDS elements. Randomized 1000 patient data were selected from year 1998 database which had at least one of the five most frequently used nursing diagnoses. Patient characteristics and care service characteristics including nursing diagnoses, interventions and outcomes were analyzed to derive the meaningful decision rules. Results. Number of comorbidity, marital status, nursing diagnosis related to risk for infection and nursing intervention related to infection protection, and discharge status were the predictors that could determine the length of stay. Four variables (age, impaired skin integrity, pain, and discharge status) were identified as valuable predictors for nursing outcome, relived pain. Five variables (age, pain, potential for infection, marital status, and primary disease) were identified as important predictors for mortality. Conclusions. This study demonstrated the utilization of data mining method through a large data set with stan dardized language format to identify the contribution of nursing care to patient's health.

• Smart Structures and Systems
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• v.32 no.4
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• pp.267-279
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• 2023

Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.