• Journal of Information Science Theory and Practice
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• v.11 no.3
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• pp.79-91
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• 2023

The storage of data documentation for Myanmar pagodas has various issues, and its retrieval method causes problems for users and libraries. This study utilized a mixed-methods approach, combining qualitative and quantitative methods to investigate pagoda data management in Myanmar libraries. The study aims to achieve the following objectives: to study the library collection management of pagodas in Myanmar, to investigate the management of pagoda data in Myanmar libraries, and to identify the pagoda data requirements for metadata development from the library professional perspective. The study findings revealed several challenges facing librarians and library users in accessing and managing Myanmar pagoda data, including limited stocks and retrieval tools, difficulty in accessing all available data online, and a lack of a centralized database or repository for storing and retrieving pagoda data. The study recommends the establishment of metadata criteria for managing a set of pagoda data and improving access to technology to address these challenges.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.119-124
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• 2004

Real data as web log file tend to be incomplete. But we have to find useful knowledge from these for optimal decision. In web log data, many useful things which are hyperlink information and web usages of connected users may be found. The size of web data is too huge to use for effective knowledge discovery. To make matters worse, they are very sparse. We overcome this sparse problem using Markov Chain Monte Carlo method as multiple imputations. This missing value imputation changes spare web data to complete. Our study may be a useful tool for discovering knowledge from data set with sparseness. The more sparseness of data in increased, the better performance of MCMC imputation is good. We verified our work by experiments using UCI machine learning repository data.

• Korean Journal of Computational Design and Engineering
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• v.5 no.3
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• pp.232-241
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• 2000

Even though we have greater chances to accomplish successful collaborative design by utilizing recent proliferation of networks, current practices do not fully take advantage of the information infrastructure. There are so much data over the networks, but not enough knowledge about the data is available to users. The main objectives of the product data interface system proposed in this paper are to capture more knowledge on managing product data and to provide users effective search capability. We define the metadata model for product data defined in STEP AP 203 and manage the metadata from product data in a repository system. Because we utilize the standard formats such as STEP for product data and RDF for metadata, the proposed approach can be applied to various fields of industries independently on commercial products.

• IE interfaces
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• v.23 no.3
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• pp.213-220
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• 2010

As the market becomes increasingly globalized and competition among companies increases in severity, various specialized organizations are participating across the process plant lifecycle, including the stages of design, construction, operation and maintenance, and dismantlement, in order to ensure efficiency and elevate competitiveness. In this regard, it is an important technical issue to develop services or information systems for sharing process plant data among participating organizations. ISO 15926 is an international standard for integration of lifecycle data for process plants including oil and gas facilities. ISO 15926 Part 7, a part of the ISO 15926 standard, specifies an implementation method called a facade that uses Web Services and ontology technologies for constructing plant data repositories and related services, with the aim of sharing lifecycle data of process plants. This paper discusses the ISO 15926-based prototype facade implemented for storing equipment data of nuclear power plants and servicing the data to interested organizations.

• BMB Reports
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• v.50 no.1
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• pp.12-19
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• 2017

Traditionally, biologists have devoted their careers to studying individual biological entities of their own interest, partly due to lack of available data regarding that entity. Large, high-throughput data, too complex for conventional processing methods (i.e., "big data"), has accumulated in cancer biology, which is freely available in public data repositories. Such challenges urge biologists to inspect their biological entities of interest using novel approaches, firstly including repository data retrieval. Essentially, these revolutionary changes demand new interpretations of huge datasets at a systems-level, by so called "systems biology". One of the representative applications of systems biology is to generate a biological network from high-throughput big data, providing a global map of molecular events associated with specific phenotype changes. In this review, we introduce the repositories of cancer big data and cutting-edge systems biology tools for network generation, and improved identification of therapeutic targets.

• Knowledge Management Research
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• v.18 no.4
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• pp.287-306
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• 2017

This research focus on the effect of developers' participation structure in knowledge creation and knowledge sharing activities in open source software development projects. Based on preferential selection theory, hypotheses of relationship between a developers' concentration of knowledge creation/sharing activities and collaboration performance was derived. To verify the hypotheses, we use the Gini coefficient in the commit contribution of the developers (knowledge creation) and the centralization index in the repository issue network (knowledge sharing network). Using social network analysis, this paper calculates centralization index from developers in the issue boards in each repository based on data from 837 repositories in GitHub, a leading open source software development platform. As a result, instead of all developers creating and sharing knowledge equally, only a few of developers creating and sharing knowledge intensively further improve the performance of the open collaboration. In other words, a few developers predominantly providing commit and actively responding to issues raised from other developers enhance the project performance. The results of this study are expected to be used by developers who manage open source software project as a governance strategy, which could improve the performance of open collaboration.

• Journal of Nuclear Fuel Cycle and Waste Technology
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• v.1 no.1
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• pp.9-27
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• 2013

Nagra was established in 1972 by the Swiss nuclear power plant operators and the Federal Government to implement permanent and safe disposal of all types of radioactive waste generated in Switzerland. The Swiss Nuclear Energy Act specifies that these shall be disposed of in deep geological repositories. A number of different geological formations and sites have been investigated to date and an extended database of geological characteristics as well as data and state-of-the-art methodologies required for the evaluation of the long-term safety of repository systems have been developed. The research, development, and demonstration activities are further supported by the two underground research facilities operating in Switzerland, the Grimsel Test Site and the Mont Terri Project, along with very active collaboration of Nagra with national and international partners. A new site selection process was approved by the Federal Government in 2008 and is ongoing. This process is driven by the long-term safety and feasibility of the geological repositories and is based on a step-wise decision-making approach with a strong participatory component from the affected communities and regions. In this paper a brief history and the current status of the Swiss radioactive waste management program are presented and special characteristics that may be useful beyond the Swiss program are highlighted and discussed.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2350-2364
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• 1999

In this paper, we propose a method of class library repository design for provide reuser the object-oriented C++ class component. To class library design, we started by studying the characteristics of a reusable component. We formally defined the reusable component model using an entity relationship model. This formal definition has been directly used as the database schema for storing the reusable component in a repository. The reusable class library may be considered a knowledge base for software reuse. Thus, we used that Enumerative classification of breakdown of knowledge based. And another used classification is clustering of based on class similarity. The class similarity composes member function similarity and member data similarity. Finally, we have designed class library for hierarchical inheritance mechanism of object-oriented concept Generalization, Specialization and Aggregation.

• KIISE Transactions on Computing Practices
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• v.22 no.5
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• pp.235-239
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• 2016

'Commit-bug link', the link between commit history and bug reports, is used for software maintenance and defect prediction in bug tracking systems. Previous studies have shown that the links are automatically detected based on text similarity, time interval, and keyword. Existing approaches depend on the quality of commit history and could thus miss several links. In this paper, we proposed a technique to link commit and bug report using not only messages of commit history, but also the similarity of files in the commit history coupled with bug reports. The experimental results demonstrated the applicability of the suggested approach.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.679-683
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• 2007

Statistical learning theory by Vapnik consists of support vector machine(SVM), support vector regression(SVR), and support vector clustering(SVC) for classification, regression, and clustering respectively. In this algorithms, SVC is good clustering algorithm using support vectors based on Gaussian kernel function. But, similar to SVM and SVR, SVC needs to determine kernel parameters and regularization constant optimally. In general, the parameters have been determined by the arts of researchers and grid search which is demanded computing time heavily. In this paper, we propose a differential evolution based SVC(DESVC) which combines differential evolution into SVC for efficient selection of kernel parameters and regularization constant. To verify improved performance of our DESVC, we make experiments using the data sets from UCI machine learning repository and simulation.