• Proceedings of the Korea Association of Information Systems Conference
• /
• 2003.11a
• /
• pp.55-63
• /
• 2003

Workflow Management System (WFMS) is a software system to support an efficient execution, control and management of complex business processes. Since traditional commercial systems mainly focus on automating processes, they don't have methods for enhancing the task performer's efficiency. In this paper, we propose a new method of executing business processes more efficiently in that a whole process is scheduled considering the degree of the participants' workload. The method allows managing the largest constraints among constituent resources of the process. We utilize DBR scheduling techniques to develop the method. We first consider the differences between workflow process models and DBR application models, and then develop the modified drum, buffer and rope. This leads us to develop WF-DBR (WorkFlow-DBR) that can control the proper size of the task performers' work list and arrival rate of process instances. Use of WF-DBR improves the efficiency of the whole process as well as the participants' working condition. We then carry out a set of simulation experiments and compare the effectiveness of our approach with that of scheduling techniques used in existing systems.

• Agribusiness and Information Management
• /
• v.4 no.1
• /
• pp.32-40
• /
• 2012

In recent times, NFC technology adaptations for smartphones have been increasing. This study proposes the adaptation of agri-food business models based on NFC technology and presents the basic technological characteristics of NFC. An NFC tag can store more information than prior tagging technology methods, such as QR codes, and provides a better user experience. Based on the unique features of NFC, this study suggests an NFC business model application for the agri-food business.

• Journal of Intelligence and Information Systems
• /
• v.3 no.2
• /
• pp.33-55
• /
• 1997

A data model is a diagram that describes the properties of different categories of data and the associations among them within a business or information system. In spite of its importance and usefulness, data modeling activity requires not only a lot of time and effort but also extensive experience and expertise. The data models for similar business areas are analogous to one another. Therefore, it is reasonable to reuse the already-developed data models if the target business area is similar to what we have already analyzed before. In this research, we develop a case-based reasoning system for data model reuse, which we shall call CB-DM Reuser (Case-Based Data Model Reuser). CB-DM Reuse consists of four subsystems : the graphic user interface to interact with end user, the data model management system to build new data model, the case base to store the past data models, and the knowledge base to store data modeling and data model reusing knowledge. We present the functionality of CB-DM Reuser and show how it works on real-life a, pp.ication.

• International journal of advanced smart convergence
• /
• v.8 no.2
• /
• pp.56-67
• /
• 2019

The aim of this study is to develop Artificial Intelligence (AI) based business models of media firms. We define AI and discuss 'AI activity model'. The practices of the efficiency model are home equipment-based personalization and media content recommendation. The practices of the expert model are media content commissioning, content rights negotiation, copyright infringement, and promotion. The practices of the effectiveness model are photo & video auto-tagging and auto subtitling & simultaneous translation. The practices of the innovation model are content script creation and metadata management. The related use cases from 2012 to 2017 are introduced along the four activity models of AI. In conclusion, we propose for media companies to fully utilize the AI for transforming from traditional to successful digital media firms.

• Journal of Information Processing Systems
• /
• v.17 no.1
• /
• pp.75-88
• /
• 2021

Automobile credit business has developed rapidly in recent years, and corresponding default phenomena occur frequently. Credit default will bring great losses to automobile financial institutions. Therefore, the successful prediction of automobile credit default is of great significance. Firstly, the missing values are deleted, then the random forest is used for feature selection, and then the sample data are randomly grouped. Finally, six prediction models of support vector machine (SVM), random forest and k-nearest neighbor (KNN), logistic, decision tree, and artificial neural network (ANN) are constructed. The results show that these six machine learning models can be used to predict the default of automobile credit. Among these six models, the accuracy of decision tree is 0.79, which is the highest, but the comprehensive performance of SVM is the best. And random grouping can improve the efficiency of model operation to a certain extent, especially SVM.

• International Journal of Computer Science & Network Security
• /
• v.21 no.12
• /
• pp.228-234
• /
• 2021

The article defines the problems of forming the models of government regulation of clustering, marketing management and labor market in the context of globalization, business bankruptcy risk and services market development. The clustering models based on the optimal partner network cooperation were proposed in order to ensure the strategic development of territories, to attract budget leading enterprises and to support small businesses. A descriptive model of government regulation of clustering, marketing management and labor market in the context of globalization, business bankruptcy risk and Covid-19 was determined.

• The Journal of Fisheries Business Administration
• /
• v.33 no.2
• /
• pp.75-98
• /
• 2002

As a contribution to developing fishery stock assessment, optimum sustainable yield and its international standards such as MSY, MEY, and dynamic MEY for six recommended fisheries are developed using bio-economic models. For selecting the appropriate model, five models - Schaefer, Schnute, Walters and Hilborn, Fox, and CY&P models are tested in effort and catch data of six species. Surprisingly all the models except the CY&P model failed to satisfy statistical standards such as goodness-of-fitness and reliability. Generally, the CY&P model holds good fitness and statistically significant level for all of six fisheries. However, the CY&P model for squid, where the intrinsic growth rate is high, could not explain MSY, MEY, and dynamic MEY appropriately. This study makes a contribution to develop the modified model for the intrinsic growth rate of 1. The reformulated model represents the results reasonably even though the estimated equation has not good fitness. Although most of the CY&P models appear to have good fits and validated results for some cases, these models also seem to be quite sensitive to parameters which means a more stable model should be developed and data should carefully be handled. In particular biological and technical interactions such as multispecies, predator prey relationship, age structure and mortality should be taken into account. In addition, economic factors and fishing efforts such as price, cost, technical change and a reasonable function of fishing input should simultaneously be considered.

• Journal of the Korean Home Economics Association
• /
• v.38 no.9
• /
• pp.19-32
• /
• 2000

The purpose of this study was to discuss previous studies and theories on family business. This study were to examine the dominant trends in the precious research, and to introduce several conceptual frameworks and models, such as family business theory, family resource management theory, theory of household adjustment and adaptation, and family business viability model. This study will be helpful in understanding of the relationship between family and business, and the work-family interface, and in developing policies and programs that assist family business and enhance the well-being for family members who are involved in family business.

• Journal of Fashion Business
• /
• v.15 no.6
• /
• pp.176-203
• /
• 2011

In today's intensifying global competition, Korean fashion industry is relying on only qualitative data for feasibility study of future projects and developmental plan. This study was conducted in order to support establishment of a scientific and rational management system that reflects market demand. First, fashion market size was limited to the total amount of expenditure for fashion clothing products directly purchased by Koreans for wear during 6 months in spring and summer and 6 months in autumn and winter. Fashion market forecasting model was developed using statistical forecasting method proposed by previous research. Specifically, time series model was selected, which is a verified statistical forecasting method that can predict future demand when data from the past is available. The time series for empirical analysis was fashion market sizes for 8 segmented markets at 22 time points, obtained twice each year by the author from 1998 to 2008. Targets of the demand forecasting model were 21 research models: total of 7 markets (excluding outerwear market which is sensitive to seasonal index), including 6 segmented markets (men's formal wear, women's formal wear, casual wear, sportswear, underwear, and children's wear) and the total market, and these markets were divided in time into the first half, the second half, and the whole year. To develop demand forecasting model, time series of the 21 research targets were used to develop univariate time series models using 9 types of exponential smoothing methods. The forecasting models predicted the demands in most fashion markets to grow, but demand for women's formal wear market was forecasted to decrease. Decrease in demand for women's formal wear market has been pronounced since 2002 when casualization of fashion market intensified, and this trend was analyzed to continue affecting the demand in the future.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.8
• /
• pp.297-309
• /
• 2021

In this study, to capture the skewness and kurtosis detected in both conditional and unconditional return distributions of the stock markets of Kazakhstan and Russia, two versions of normal mixture GARCH models are employed. The data set consists of daily observations of the Kazakhstan and Russia stock prices, and world crude oil price, covering the period from 1 June 2006 through 1 March 2021. From the empirical results, incorporating the long memory effect on the returns not only provides better descriptions of dynamic behaviors of the stock market prices but also plays a significant role in improving a better understanding of the return dynamics. In addition, normal mixture models for time-varying volatility provide a better fit to the conditional densities than the usual GARCH specifications and has an important advantage that the conditional higher moments are time-varying. This implies that the volatility skews implied by normal mixture models are more likely to exhibit the features of risk and the direction of the information flow is regime-dependent. The findings of this study contain useful information for diverse purposes of cross-border stock market players such as asset allocation, portfolio management, risk management, and market regulations.