• 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 Korea Spatial Information System Society
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• v.8 no.1 s.16
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• pp.1-16
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• 2006

Recently, human based computing environment for supporting users to concentrate only user task without sensing other changes from users is being progressively researched and developed. But middleware deletes steream data processed for reducing process load of massive information from RFID sensor in this computing. So, this kind of middleware have problems when user demands probability or statistics needed for data warehousing or data mining and when user demands very important stream data repeatedly but already discarded in the middleware every former time. In this paper, we designs Sensor Middleware Architecture on Multi Level Spatial DBMS with Snapshot and manage repeatedly required stream datas to solve reusing problems of historical stream data in current middleware. This system uses disk databse that manages historical stream datas filtered in middleware for requiring services using historical stream information as data mining or data warehousing from user, and uses memory database that mamages highly reuseable data as a snapshot when stream data storaged in disk database has high reuse frequency from user. For the more, this system processes memory database management policy in a cycle to maintain high reusement and rapid service for users. Our paper system solves problems of repeated requirement of stream datas, or a policy decision service using historical stream data of current middleware. Also offers variant and rapid data services maintaining high data reusement of main memory snapshot datas.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2007.05a
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• pp.234-245
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• 2007

The empirical studies on the implementation of data warehousing systems (DWS) are lacking while there exist a number of studies on the implementation of IS. This study intends to examine the factors affecting the implementation success of DWS. The study adopts the empirical analysis of the sample of 112 responses from DWS practitioners. The study results suggest several implications for researchers and practitioners. First, when the support from top management becomes great, the implementation success of DWS in organizational aspects is more likely. When the support from top management exists, users are more likely to be encouraged to use DWS, and organizational resistance to use DWS is well coped with increasing the possibility of implementation success of DWS. The support of resource increases the implementation success of DWS in project aspects while it is not significantly related to the implementation success of DWS in organizational aspects. The support of funds, human resources, and other efforts enhances the possibility of successful implementation of project; the project does not exceed the time and resource budgets and meet the functional requirements. The effect of resource support, however, is not significantly related to the organizational success. The user involvement in systems implementation affects the implementation success of DWS in organizational and project aspects. The success of DWS implementation is significantly related to the users' commitment to the project and the proactive involvement in the implementation tasks. users' task. The observation of the behaviors of competitors which possibly increases data quality does not affect the implementation success of DWS. This indicates that the quality of data such as data consistency and accuracy is not ensured through the understanding of the behaviors of competitors, and this does not affect the data integration and the successful implementation of DWS projects. The prototyping for the DWS implementation positively affects the implementation success of DWS. This indicates that the extent of understanding requirements and the communication among project members increases the implementation success of DWS. Developing the prototypes for DWS ensures the acquirement of accurate or integrated data, the flexible processing of data, and the adaptation into new organizational conditions. The extent of consulting activities in DWS projects increases the implementation success of DWS in project aspects. The continuous support for consulting activities and technology transfer enhances the adherence to the project schedule preventing the exceeding use of project budget and ensuring the implementation of intended system functions; this ultimately leads to the successful implementation of DWS projects. The research hypothesis that the capability of project teams affects the implementation success of DWS is rejected. The technical ability of team members and human relationship skills themselves do not affect the successful implementation of DWS projects. The quality of the system which provided data to DWS affects the implementation success of DWS in technical aspects. The standardization of data definition and the commitment to the technical standard increase the possibility of overcoming the technical problems of DWS. Further, the development technology of DWS affects the implementation success of DWS. The hardware, software, implementation methodology, and implementation tools contribute to effective integration and classification of data in various forms. In addition, the implementation success of DWS in organizational and project aspects increases the data quality and system quality of DWS while the implementation success of DWS in technical aspects does not affect the data quality and system quality of DWS. The data and systems quality increases the effective processing of individual tasks, and reduces the decision making times and efforts enhancing the perceived benefits of DWS.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.448-455
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• 2001

Recently, many enterprises have attempted to capitalize knowledge assets on data warehouse (DW). It has been recognized as strategic core process to create corporate competitive advantage and implement enterprise e-biz strategies. However, most approaches to represent knowledge and decision process have limits in considering various knowledge types, their relationships and continuity in knowledge formulation. In addition, they are so inclined to one side such as concept-oriented frameworks or technology-oriented ones. They lack universal and wide-ranging features. This paper presents a comprehensive methodology to accumulate knowledge capital on DW via a properly grained hypermedia model. The methodology consists of three phases: knowledge requirement elicitation, hypermedia modeling, and system implementation. A real-life case for medical DW development is presented to demonstrate the usefulness of the proposed methodology. This methodology is effective when an organization accumulates knowledge assets to put the corporate e-biz or cre-biz strategy into practice.

• Journal of Korea Spatial Information System Society
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• v.10 no.4
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• pp.89-95
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• 2008

In this paper, we propose an integrated techniques for managing data quality in traffic data warehousing environments. We describe how to collect and construct the traffic data warehouses from the operational databases, such as FTMS and ARTIS. We explain how to configure the traffic data warehouses efficiently. Also, we propose a quality management techniques to provide high quality traffic data for various analytical transactions. Proposed techniques can contribute in providing high quality traffic data to the traffic related users and researcher, thus reducing data preprocessing and evaluation cost.

• Korean Management Science Review
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• v.20 no.2
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• pp.1-16
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• 2003

Previous research on data extraction and integration for data warehousing has concentrated mainly on the relational DBMS or partly on the object-oriented DBMS. Mostly, it describes issues related with the change data (deltas) capture and the incremental update by using the triggering technique of active database systems. But, little attention has been paid to data extraction approaches from other types of source systems like hierarchical DBMS, etc. and from source systems without triggering capability. This paper argues, from the practical point of view, that we need to consider not only the types of information sources and capabilities of ETT tools but also other factors of source systems such as operational characteristics (i.e., whether they support DBMS log, user log or no log, timestamp), and DBMS characteristics (i.e., whether they have the triggering capability or not, etc), in order to find out appropriate data extraction techniques that could be applied to different source systems. Having applied several different data extraction techniques (e.g., DBMS log, user log, triggering, timestamp-based extraction, file comparison) to S bank's source systems (e.g., IMS, DB2, ORACLE, and SAM file), we discovered that data extraction techniques available in a commercial ETT tool do not completely support data extraction from the DBMS log of IMS system. For such IMS systems, a new date extraction technique is proposed which first creates Index database and then updates the data warehouse using the Index database. We illustrates this technique using an example application.

• IE interfaces
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• v.15 no.2
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• pp.159-171
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• 2002

Data Warehouses integrate data from multiple heterogeneous information sources and transform them into a multidimensional representation for decision support applications. Data warehousing has emerged as one of the most powerful tools in delivering information to users. Most previous researches have focused on marketing, customer service, financing, and insurance industry. Further, relatively less research has been done on data warehouse systems in the complex manufacturing industry such as ship production, which is characterized complex product structures and production processes. In the ship production, data warehouse systems is a requisite for effective cost analysis because collecting and analysis of diverse and large of cost-related(material/production cost, productivity) data in its operational systems, was becoming increasingly cumbersome and time consuming. This paper proposes architecture of the data warehouse systems to support cost analysis in the ship production. Also, in order to illustrate the usefulness of the proposed architecture, the prototype system is designed and implemented with the object of the enterprise of producing a large-scale ship.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.204-214
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• 2017

Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.05a
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• pp.342-351
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• 2002

A personalized product recommendation is an enabling mechanism to overcome information overload occurred when shopping in an Internet marketplace. Collaborative filtering has been known to be one of the most successful recommendation methods, but its application to e-commerce has exposed well-known limitations such as sparsity and scalability, which would lead to poor recommendations. This paper suggests a personalized recommendation methodology by which we are able to get further effectiveness and quality of recommendations when applied to an Internet shopping mall. The suggested methodology is based on a variety of data mining techniques such as web usage mining, decision tree induction, association rule mining and the product taxonomy. For the evaluation of the methodology, we implement a recommender system using intelligent agent and data warehousing technologies.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.109-118
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• 2024

The world today is advancing towards a digital solution for every indusial domain varying from advanced engineering and medicine to training and management. The supply cycles can only be boosted via an effective management of the warehouse and a stronger hold over the logistics and inventory insights. RFID technology has been an open source tool for various MNCs and corporal organization who have progressed along a considerable drift on the charts. RFID is a methodology of analysing the warehouse and logistic data and create useful information in line to the past trends and future forecasts. The method has a high tactical accuracy and has been seen providing up to 99.57% accurate insights for the future cycle, based on the organizational capabilities and available resources. This paper discusses the implementation of RFID on field and provides results of datasets retrieved from controlled data of a practical warehouse and logistics system.