• Information Systems Review
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• v.4 no.2
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• pp.209-222
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• 2002

EDI (Electronic Data Interchange) has been widely utilized to support Business Activities since it has such advantages as fast transfer of information, less documentation work, efficient information exchange etc. Recently e-business environment has urged the traditional EDI system to be changed to ebXML framework. To apply the ebXML framework to a certain industry, it is required to implement Business Process (BP), Core Component (CC), Collaboration Protocol Profile (CPP), Collaboration Protocol Agreement (CPA), Messaging system etc. We have selected the port and logistics industry as a target domain to apply ebXML framework, since the EDI usage ratio of it is relatively higher than other industries. In this paper, we have analyzed the current status of EDI system and transaction processes in the port and logistics industry. We have defined the business process that will be registered in the registry/repository, the main component of ebXML framework, using UN/CEFACT modeling methodology. And Business Collaborations, Business Transactions, Business Document Flows, Choreography, Pattern, etc. are represented using UML according to UN/ CEFACT modeling methodology, to apply ebXML Framework to the port and logistics distribution industry. Also we have suggested the meta methodology for applying the ebXML framework to other industries.

• The Journal of the Korea Contents Association
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• v.18 no.3
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• pp.70-80
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• 2018

Due to the recent development of social media and mobile devices, graph data have been using in various fields. In addition, caching techniques for reducing I/O costs in the process of large capacity graph data have been studied. In this paper, we propose a multi-layer caching scheme considering the connectivity of the graph, which is the characteristics of the graph topology, and the history of the past subgraph usage. The proposed scheme divides a cache into Used Data Cache and Prefetched Cache. The Used Data Cache maintains data by weights according to the frequently used sub-graph patterns. The Prefetched Cache maintains the neighbor data of the recently used data that are not used. In order to extract the graph patterns, their past history information is used. Since the frequently used sub-graphs have high probabilities to be reused, they are cached. It uses a strategy to replace new data with less likely data to be used if the memory is full. Through the performance evaluation, we prove that the proposed caching scheme is superior to the existing cache management scheme.

• Journal of the HCI Society of Korea
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• v.5 no.1
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• pp.37-43
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• 2010

Twitter has been continuously growing since October, 2006. Especially, not only the users and the number of messages have been increasing but also a new concept in social networking called 'micro blogging' has diffused. Within Korea, service such as 'me2day' has already been introduced and the improvement of internet accessibility within mobile devices is expected to expand the 'micro blogs'. In this point, this research is executed to study the new medium, 'micro blog'. To do so, we collected and analyzed Twitter logs of Korean users. Especially, we were curious about the extreme heavy users using Twitter, despite of the linguistic and cultural barrier of the foreign service. Who they are, why and how they use the 'micro blog'. First, we reviewed the general aspect of followers and messages by collecting a certain number of random samples. Using the Lorenz curve we found out that there was the imbalance within the users and based on this phenomenon we deducted an extreme heavy user group. In order to perform further analysis, log analysis was performed on the extreme heavy users. As the result, the users used multiple mobile and desktop 'Twitter' clients. The usage pattern was similar to that of internet usage time but was used during their "micro" time. The users using 'Twitter' not only to spread messages about important information, special events and emotions, but also as a habitual 'chatting tool' to express ordinary personal chats similar to SMS and IM services. In this research, it is proved that 68% of the total messages were ordinary personal chats. Also, with 24% of the total messages were retweets, we were able to find out that virtually connected 'people' and 'relationships' acted as the dominant trigger of their articulation.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.133-140
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• 2023

The performance of lithium ion batteries depends on the usage environment and the combination ratio of cathode materials. In order to develop a high-performance lithium-ion battery, it is necessary to manufacture the battery and measure its performance while varying the cathode material ratio. However, it takes a lot of time and money to directly develop batteries and measure their performance for all combinations of variables. Therefore, research to predict the performance of a battery using an artificial intelligence model has been actively conducted. However, since measurement experiments were conducted with the same battery in the existing published battery data, the cathode material combination ratio was fixed and was not included as a data attribute. In this paper, we define a training data model required to develop an artificial intelligence model that can predict battery performance according to the combination ratio of cathode materials. We analyzed the factors that can affect the performance of lithium-ion batteries and defined the mass of each cathode material and battery usage environment (cycle, current, temperature, time) as input data and the battery power and capacity as target data. In the battery data in different experimental environments, each battery data maintained a unique pattern, and the battery classification model showed that each battery was classified with an error of about 2%.

• Journal of Broadcast Engineering
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• v.9 no.4 s.25
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• pp.305-321
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• 2004

With the advent of the digital broadcasting, the audiences can access a large number of TV programs and their information through the multiple channels on various media devices. The access to a large number of TV programs can support a user for many chances with which he/she can sort and select the best one of them. However, the information overload on the user inevitably requires much effort with a lot of patience for finding his/her favorite programs. Therefore, it is useful to provide the persona1ized broadcasting service which assists the user to automatically find his/her favorite programs. As the growing requirements of the TV personalization, we introduce our automatic user preference learning algorithm which 1) analyzes a user's usage history on TV program contents: 2) extracts the user's watching pattern depending on a specific time and day and shows our automatic TV program recommendation system using MPEG-7 MDS (Multimedia Description Scheme: ISO/IEC 15938-5) and 3) automatically calculates the user's preference. For our experimental results, we have used TV audiences' watching history with the ages, genders and viewing times obtained from AC Nielson Korea. From our experimental results, we observed that our proposed algorithm of the automatic user preference learning algorithm based on the Bayesian network can effectively learn the user's preferences accordingly during the course of TV watching periods.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.1-23
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• 2019

Big data refers to data that is difficult to store, manage, and analyze by existing software. As the lifestyle changes of consumers increase the size and types of needs that consumers desire, they are investing a lot of time and money to understand the needs of consumers. Companies in various industries utilize Big Data to improve their products and services to meet their needs, analyze unstructured data, and respond to real-time responses to products and services. The financial industry operates a decision support system that uses financial data to develop financial products and manage customer risks. The use of big data by financial institutions can effectively create added value of the value chain, and it is possible to develop a more advanced customer relationship management strategy. Financial institutions can utilize the purchase data and unstructured data generated by the credit card, and it becomes possible to confirm and satisfy the customer's desire. CRM has a granular process that can be measured in real time as it grows with information knowledge systems. With the development of information service and CRM, the platform has change and it has become possible to meet consumer needs in various environments. Recently, as the needs of consumers have diversified, more companies are providing systematic marketing services using data mining and advanced CRM (Customer Relationship Management) techniques. KB Kookmin Card, which started as a credit card business in 1980, introduced early stabilization of processes and computer systems, and actively participated in introducing new technologies and systems. In 2011, the bank and credit card companies separated, leading the 'Hye-dam Card' and 'One Card' markets, which were deviated from the existing concept. In 2017, the total use of domestic credit cards and check cards grew by 5.6% year-on-year to 886 trillion won. In 2018, we received a long-term rating of AA + as a result of our credit card evaluation. We confirmed that our credit rating was at the top of the list through effective marketing strategies and services. At present, Kookmin Card emphasizes strategies to meet the individual needs of customers and to maximize the lifetime value of consumers by utilizing payment data of customers. KB Kookmin Card combines internal and external big data and conducts marketing in real time or builds a system for monitoring. KB Kookmin Card has built a marketing system that detects realtime behavior using big data such as visiting the homepage and purchasing history by using the customer card information. It is designed to enable customers to capture action events in real time and execute marketing by utilizing the stores, locations, amounts, usage pattern, etc. of the card transactions. We have created more than 280 different scenarios based on the customer's life cycle and are conducting marketing plans to accommodate various customer groups in real time. We operate a smart offering system, which is a highly efficient marketing management system that detects customers' card usage, customer behavior, and location information in real time, and provides further refinement services by combining with various apps. This study aims to identify the traditional CRM to the current CRM strategy through the process of changing the CRM strategy. Finally, I will confirm the current CRM strategy through KB Kookmin card's big data utilization strategy and marketing activities and propose a marketing plan for KB Kookmin card's future CRM strategy. KB Kookmin Card should invest in securing ICT technology and human resources, which are becoming more sophisticated for the success and continuous growth of smart offering system. It is necessary to establish a strategy for securing profit from a long-term perspective and systematically proceed. Especially, in the current situation where privacy violation and personal information leakage issues are being addressed, efforts should be made to induce customers' recognition of marketing using customer information and to form corporate image emphasizing security.

• Journal of KIISE
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• v.44 no.5
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• pp.439-448
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• 2017

The workloads of large high-performance computing (HPC) scientific applications are steadily becoming "bursty" due to variable resource demands throughout their execution life-cycles. However, the over-provisioning of virtual resources for optimal performance during execution remains a key challenge in the scheduling of scientific HPC applications. While over-provisioning of virtual resources guarantees peak performance of scientific application in virtualized environments, it results in increased amounts of idle resources that are unavailable for use by other applications. Herein, we proposed a memory resource reconfiguration approach that allows the quick release of idle memory resources for new applications in OS-level virtualized systems, based on the applications resource-usage pattern profile data. We deployed a scientific workflow application in Docker, a light-weight OS-level virtualized system. In the proposed approach, memory allocation is fine-tuned to containers at each stage of the workflows execution life-cycle. Thus, overall memory resource utilization is improved.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.109-119
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• 2008

The number of card users has grown steadily after the adaption of smart card. Considering the diverse information from smart card data, the increase of card usage rate leads to various useful implications meaning in travel pattern analysis and transportation policy. One of the most important implications is the possibility that the data enables us to generate transit O/D tables easily. In the case of generating transit O/D tables from smart card data, it is necessary to filter data error and/or data missing. Also, the correction of data missing is an important procedure. In this study, it is examined to compute the level of data error and data missing, and to correct data missing for transit O/D generation.

• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.4
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• pp.43-61
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• 2011

We first develop a value ecosystem framework to model the SDP(Service Delivery Process) of web services. Since the web service has been evolving from the basic web architecture (e.g., traditional world wide web) to a prosuming platform based on virtualization technologies, the proposed framework of the value ecosystem focuses on capturing the key characteristics of SDP in each type of web services. Even though they share the basic elements such as PP(Platform Provider), CA(Customization Agency) and user group, the SDP in the traditional web services (so-called Web1.0 in this paper) is quite different from the most recent one (so-called Web2.0). In our value ecosystem, users are uniformly distributed over (0, ${\Delta}$), where ${\Delta}$��represents the variety level of users' preference on the web service level. PP and CA provide a standard level of web service(s) and prosuming service package, respectively. CA in Web1.0 presents a standard customization package($s_a$) at flat rate c, whereas PP and CA collaborate and provide customization service with a usage-based scheme. We employ a multi-stage game model to analyze and compare the SDPs in Web1.0 and Web2.0. Our findings through analysis and numerical simulations are as follows. First, the user group is consecutively segmented, and the pattern of the segmentations varies across Web1.0 and Web2.0. The standardized service level s (from PP) is higher in Web1.0, whereas the amount of information created in the value ecosystem is bigger in Web2.0. This indicates the role of CA would be increasingly critical in Web2.0: in particular, for fulfilling the needs of prosuming and service customization.

• The Journal of the Korea Contents Association
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• v.15 no.4
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• pp.153-167
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• 2015

This paper empirically analyzes specific characteristics of VOD users and their practical viewing patterns, using socio-demographic data of the current IPTV subscribers in combination with actual data of genre usage and payment. The findings revealed active viewing patterns of male, the unemployed, high earners and early adopters. In terms of preferences, households with large numbers of women prefer time shift contents, whereas households composed of more men or preschoolers prefer non-time shift contents. Likewise, the households that have more women or higher income had relatively a lot of experiences of purchasing time shift contents on one hand, but the households characterized by the larger numbers of family members or unemployed householder or young householder showed much willingness to pay premium contents on the other hand. Given the utilization of correct database, the findings offer useful information conducive to service promotion and marketing strategies to maximize VOD use in the practical dimension.