• International Commerce and Information Review
• /
• v.7 no.1
• /
• pp.55-74
• /
• 2005

Recently, many enterprises have attempted to construct data warehousing systems for decision-support. Data warehouse is an intelligent store of data that can aggregate vast amounts of information. Building DW requires two important development issues:(i) DW for the decision making of business users and (ii) metadata within it. Most DW development methodologies have not considered metadata development; it is necessary to adopt a DW development methodology which develops a DW and its metadata simultaneously. Metadata is a key to success of data warehousing system and is critical for implementing DW. That is, metadata is crucial documentation for a data warehousing system where users should be empowered to meet their own information needs; users need to know what data exists, what it represents, where it is located, and how to access it. Furthermore, metadata is used for extracting data and managing DW. However, metadata has failed because its management has been segregated from the DW development process. Metadata must be integrated with data warehousing systems. Without metadata, the decision support of DW is under the control of technical users. Therefore, integrating data warehouse with its metadata offers a new opportunity to create a more adaptive information system. Therefore, this paper proposes a DW development methodology from a metadata perspective. The proposed methodology consists of five phases: preparatory, requirement analysis, data warehouse (informational database) development, metastore development, and maintenance. To demonstrate the practical usefulness of the methodology, one case is illustrated

• Korean Journal of Computational Design and Engineering
• /
• v.22 no.1
• /
• pp.80-88
• /
• 2017

Product data analytics (PDA) is a data-driven analysis method that uses product data management (PDM) databases as its operational data. It aims to understand and evaluate product development processes indirectly through the analysis of product data from the PDM databases. To educate and train PDA efficiently, this study proposed an approach that employs courses for both product development and PDA in a class. The participant group for product development provides a PDM database as a result of their product development activities, and the other group for PDA analyses the PDM database and provides analysis result to the product development group who can explain causes of the result. The collaboration between the two groups can enhance the efficiency of the education and training course on PDA. This study also includes an application example of the approach to a graduate class on PDA and discussion of its result.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.20 no.2
• /
• pp.187-196
• /
• 2017

The size of raw data has dramatically increased due to the recent trend of Synthetic Aperture Radar(SAR) development plans for high resolution and high definition image acquisition. The large raw data has an impact on satellite operability due to the limitations of storage and transmission capacity. To improve the SAR operability, the SAR raw data shall be compressed before transmission to the ground station. The Block Adaptive Quantization (BAQ) algorithm is one of the data compression algorithm and has been used for a long time in the spaceborne SAR system. In this paper, an optimization method of BAQ threshold table is introduced using real SAR raw data to prevent the degradation of signal quality caused by data compression. In this manner, a new variation estimation strategy and a new threshold method for block type decision are introduced.

• The KIPS Transactions:PartD
• /
• v.11D no.1
• /
• pp.173-182
• /
• 2004

The research to estimate development effort of software has been progress. But, it is not easy gain that testing data for estimating of development effort. Also, if we get the testing data, it is important that analysis testing data. In this paper, we study the data analysis of software development effort using the 789 software development projects which developed in the 1990's. Software development scale and software development team site are various. Using the characteristic of factor, we have to study characteristic of data and we estimate the development effort step by step. First, we prove the difference of development effort with the 789 project data according to development type, development environment, the development language etc. Also, we execute the crosstabs analysis that team site and function point.

• Proceedings of the Korea Database Society Conference
• /
• 1998.09a
• /
• pp.448-474
• /
• 1998

Data warehouse (DW) is important for analytical processing. Metadata is a key to its architecture. This paper proposes an architecture that consists of seven components. To illustrate data warehouse environment (DWE), this Paper proposes taxonomies having four flows. on the basis or the taxonomies and metadata, this paper proposes a methodology for building the data warehouse and metadata simultaneously. This integrated development methodology (IDM) consists of seven phases: (i) preparatory phase, (ii) requirement analysis phase, (iii) data warehouse development phase, (iv) operational data store development phase, (v) data mart development phase, (vi) metastore development phase, and (vii) maintenance phase. A metastore system is Proposed to help develop metadata interactively. An illustrative example is investigated to demonstrate the usefulness of IDM.

• Genomics & Informatics
• /
• v.1 no.2
• /
• pp.65-74
• /
• 2003

Data mining differs primarily from traditional data analysis on an important dimension, namely the scale of the data. That is the reason why not only statistical but also computer science principles are needed to extract information from large data sets. In this paper we briefly review data mining, its characteristics, typical data mining algorithms, and potential and ongoing applications of data mining at biopharmaceutical industries. The distinguishing characteristics of data mining lie in its understandability, scalability, its problem driven nature, and its analysis of retrospective or observational data in contrast to experimentally designed data. At a high level one can identify three types of problems for which data mining is useful: description, prediction and search. Brief review of data mining algorithms include decision trees and rules, nonlinear classification methods, memory-based methods, model-based clustering, and graphical dependency models. Application areas covered are discovery compound libraries, clinical trial and disease management data, genomics and proteomics, structural databases for candidate drug compounds, and other applications of pharmaceutical relevance.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.39 no.3
• /
• pp.139-146
• /
• 2016

This study proposes a methodology to analyse social networks among participating design engineers during product development projects. The proposed methodology enables product development managers or the participating design engineers to make a proper decision on product development considering the performance of participating design engineers. It considers a product development environment where an integrated product data management (PDM) system manages the product development data and associated product development processes consistently in its database, and all the design engineers share the product development data in the PDM database for their activities in the product development project. It provides a novel approach to build social networks among design engineers from an operational product development data in the PDM database without surveys or monitoring participating engineers. It automatically generates social networks among the design engineers from the product data and relationships specified by the participants during the design activities. It allows analysts to gather operational data for their analysis without additional efforts for understanding complex and unstructured product development processes. This study also provides a set of measures to evaluate the social networks. It will show the role and efficiency of each design engineers in the social network. To show the feasibility of the approach, it suggests an architecture of social network analysis (SNA) system and implemented it with a research-purpose PDM system and R, a statistical software system. A product configuration management process with synthetical example data is applied to the SNA system and it shows that the approach enables analysts to evaluate current position of design engineers in their social networks.

• Communications of Mathematical Education
• /
• v.36 no.2
• /
• pp.253-286
• /
• 2022

This study is a study in which four teachers from the same school who participated in a teacher learning community program at the school field developed interdisciplinary linkage and convergence data using Plato as a collaborative circle in ethics and mathematics subjects. In particular, this study aimed to develop practical and shareable lesson materials. The data development procedure was developed according to the following four procedures. 'Development of data development plan, data development, verification of development data, and development of final data that reflects the verification opinions' At this time, in the data development stage, a theme-based design model was applied and developed. In addition, the development data were verified by conducting CVR verification for field teachers to focus on the validity and class applicability, and the final data were presented after the development data being revised to reflect the verification results. This study not only introduced the developed data, but also described the procedure of the data development process and the trial and error and concerns of the developers in the process to provide information on the nature of basic research to other field researchers who attempt data development.

• Toxicological Research
• /
• v.18 no.2
• /
• pp.167-174
• /
• 2002

Historical control data have been shown to be valuable in the proper interpretation and validation of reproductive toxicology studies. The present data were compiled from rat fertility and early embryonic development studies conducted at Korea Institute of Toxicology during the 1994∼2001 period. These data were assembled in order to provide background information for the general and reproductive data collected in 11 fertility and early embryonic development studies using Sprague-Dawley rats obtain-ing from the Breeding Facility, Korea Institute of Toxicology, Korea. A total of 274 males and 274 females were used in these studies during the eight-year period. Parameters of fertility and early embryonic development included clinical sign, body weights, food consumption, organ weights, estrus cycle, copulation index, precoital time, fertility index, pregnancy index, sperm parameters, and early embryonic development parameters. Most of the values were comparable to the previous historical control data reported by other investigators. These data can be wed not only as a historical data base for the meaningful interpretation of data from reproductive and developmental toxicity studies, but also as a contribution to biological characterization of Sprague-Dawley rats.

• Korean Journal of Social Welfare
• /
• v.54
• /
• pp.245-272
• /
• 2003

Despite the importance of the program development activities and the necessity of the systematical investigation on the features of the program development activities in the social service agencies, it has been observed that recent social work studies have ignored an important study area of program development, including the activities of collecting data in the process of program development in social service agencies. Therefore, this study was undertaken to investigate salient features of the activities of collecting data for program development in the social welfare centers in Korea. A questionnaire was constructed with three parts, including a dependent variable and 6 independent variables, and 201 questionnaires were collected from 353 agencies during two months. As the result of the descriptive analyses, the five noticeable features were found, (1) the activities of collecting data for program development in the agencies are very active; (2) staff in his/her twenties are in charge of program development; (3) diverse data are collected in the process of program development (4) hard data are more collected than soft data in the process of program development; (5) the respondents are more despondent on knowledge learned from individual studies than knowledge learned from academic institutes. Multiple regressions were applied to analyze the relationship between independent variables and three kind of dependent variables - total feature of data collecting, collecting hard data, collecting soft data. The result showed that the total feature of data collecting was critically influenced by social workers' autonomy, openness, and knowledge learned from academic institutes, and workload. The activities of collecting hard data was influenced by the above variables, except social workers' workload, The activities of collecting soft data were influenced by the social workers' autonomy, openness, and knowledge learned from academic institutes, and workload. Major findings were discussed and several suggestions were made for future research and improvement of the program development in social welfare centers.