• Journal of Digital Convergence
• /
• v.10 no.11
• /
• pp.427-432
• /
• 2012

The article herein suggested a compound repository and a descriptive method to develop a compound knowledge process. A data target saved in a compound knowledge repository suggested in this article includes all compound knowledge meta data and digital resources, which can be divided into the three following factors according to the purpose: user roles, functional elements, and service ranges. The three factors are basic components to describe abstract models of repository. In this article, meta data of compound knowledge are defined by being classified into the two factors. A component stands for the property about a main agent, activity unit or resource that use and create knowledge, and a context presents the context in which knowledge object are included. An agent of the compound knowledge process performs classification, registration, and pattern information management of composite knowledge, and serves as data flow and processing between compound knowledge repository and user. The agent of the compound knowledge process consists of the following functions: warning to inform data search and extraction, data collection and output for data exchange in an distributed environment, storage and registration for data, request and transmission to call for physical material wanted after search of meta data. In this article, the construction of a compound knowledge repository for recommendation system to be developed can serve a role to enhance learning productivity through real-time visualization of timely knowledge by presenting well-put various contents to users in the field of industry to occur work and learning at the same time.

• The Journal of Korean society of community based occupational therapy
• /
• v.5 no.2
• /
• pp.43-55
• /
• 2015

Objective : This study was a meta-analysis of previous studies to examine the integration of related factors depression University students of Korea, and to determine the relative importance among the relevant factors based on it. Methods : 2000-2014 papers posted on the National Science and Technology Information Center (NDSL), Nurimedia (DBpia), Academic Research Information Service (RISS), Korea Research Information(KISS), provide the text of the Library of Congress were collected using the service. The Key words a 'University Student', 'Depression', 'Depression Factors' was used. Used the Down & Black level, evidence-based checklist was developed by the research (1998) (checklist) had analyzed the selected document metadata to assess the quality. Results : 47-studies selected research groups are divided into five factors(self-esteem, suicidal ideation, positive thinking, stresses, Internet and smartphone addiction). Using meta-analysis, we analyzed the effect sizes, statistical heterogeneity and publication amenities. As a result, the self-esteem of the five factors were not found heterogeneity. Effect size is a self-esteem and suicidal ideation "large effect size", positive thinking and stress "medium effect size", internet and smart phone addiction"small effect size". Conclusion : Self-esteem and suicidal ideation are among the factors associated with depression in University students of Korea was found that the most relevant. It identified the factors associated with depression in college students, and could utilized as basis for the prevention of depression.

• Journal of Internet Computing and Services
• /
• v.21 no.1
• /
• pp.169-178
• /
• 2020

The reductions of troops/human resources, and improvement in combat power have made Korean Department of Defense actively adapt 4th Industrial Revolution technology (Artificial Intelligence, Big Data). The defense information system has been developed in various ways according to the task and the uniqueness of each military. In order to take full advantage of the 4th Industrial Revolution technology, it is necessary to improve the closed defense datamanagement system.However, the establishment and usage of data standards in all information systems for the utilization of defense big data and artificial intelligence has limitations due to security issues, business characteristics of each military, anddifficulty in standardizing large-scale systems. Based on the interworking requirements of each system, data sharing is limited through direct linkage through interoperability agreement between systems. In order to implement smart defense using the 4th Industrial Revolution technology, it is urgent to prepare a system that can share defense data and make good use of it. To technically support the defense, it is critical to develop Multi Repository Meta-Data Management (MRMM) that supports systematic standard management of defense data that manages enterprise standard and standard mapping for each system and promotes data interoperability through linkage between standards which obeys the Defense Interoperability Management Development Guidelines. We introduced MRMM, and implemented by using vocabulary similarity using machine learning and statistical approach. Based on MRMM, We expect to simplify the standardization integration of all military databases using artificial intelligence and bigdata. This will lead to huge reduction of defense budget while increasing combat power for implementing smart defense.

• The Journal of Society for e-Business Studies
• /
• v.8 no.3
• /
• pp.131-141
• /
• 2003

For workflow systems using XML documents for saving workflow process definition and especially for XML-based worflow systems, a repository needed to provide control for XML documents. The repository allows uniform access to shared data and facilitates integration among tools in workfolw systems. An XML repository gives the best solution to maintain, exchane, and modify the workflow process definition meta data, which is in the form of XML documents . The XML repository, where users can lookup the worflow definition objects, serves as the metadata foundation of the workflow system. In this paper, we developed for XML Repository(called REPOWO : REPOsitory for Workflow) that can store, delete, search and transform XML documents. REPOWO is implemented by combining EJB components based on RDBMS. By REPOWO, workfolw process definitions, data type definitions and transition informations can be easily manage.

• The KIPS Transactions:PartD
• /
• v.15D no.4
• /
• pp.549-556
• /
• 2008

Web-based Enterprise Intranet System is developed Object-oriented programming language and data management is constructed using RDBMS. Between Object-oriented programming language and RDBMS bring about "Object-Relational Impedance Mismatch" due to heterogeneous paradigm. To solve this kinds of problems commonly use the ORM Framework that it is converting data between incompatible type systems in databases and object-oriented programming languages, uses complex mapping metadata. It causes difficult to develop and maintain because of inflexible in changes. This paper proposed persistence framework that solve the existing complexity of ORM framework and has more flexible in evolution of database table. This persistence framework is unnecessary meta information that connecting with entity of database table and the objects, it offers users convenience of maintenance and it allows far more flexible and affordable systems to be built because of automatically code generation. The result of testing based on the proposed persistence framework with Hibernate, iBATIS, It is similar response time with iBATIS and it has more about three times faster than Hibernate. But iBATIS has problems of mass data processing.

• Journal of the Institute of Convergence Signal Processing
• /
• v.9 no.4
• /
• pp.313-320
• /
• 2008

The dynamic clustering technique has some problems regarding energy consumption. In the cluster configuration aspect the cluster structure must be modified every time the head nodes are re-selected resulting in high energy consumption. Also, there is excessive energy consumption when a cluster head node receives identical data from adjacent cluster sources nodes. This paper proposes a solution to the problems described above from the energy efficiency perspective. The round-robin cluster header(RRCH) technique, which fixes the initially structured cluster and sequentially selects duster head nodes, is suggested for solving the energy consumption problem regarding repetitive cluster construction. Furthermore, the issue of redundant data occurring at the cluster head node is dealt with by broadcasting metadata of the initially received data to prevent reception by a sensor node with identical data. A simulation experiment was performed to verify the validity of the proposed approach. The results of the simulation experiments were compared with the performances of two of the must widely used conventional techniques, the LEACH(Low Energy Adaptive Clustering Hierarchy) and HEED(Hybrid, Energy Efficient Distributed Clustering) algorithms, based on energy consumption, remaining energy for each node and uniform distribution. The evaluation confirmed that in terms of energy consumption, the technique proposed in this paper was 29.3% and 21.2% more efficient than LEACH and HEED, respectively.

• The Journal of the Korea Contents Association
• /
• v.11 no.11
• /
• pp.49-61
• /
• 2011

In recent, library is considered as an integrated knowledge convergence center that can respond to various requests about information service of users. Therefor it is necessary to establish a novel information system based on information communications technologies of the era. In other words, it is currently required to develop mobile information service available in portable devices such as smart phones or tablet PCs, and to establish information system reflecting cloud computing, SaaS, Annotation, and Library 2.0 etc. In this paper we design and implement a library information system using collective intelligence and cloud computing. This information system can be adapted for the varieties of mobile service paradigm and abruptly increasing amount of electronic materials. Advantages of this concept model are resource sharing, multi-tenant supporting, configuration, and meta-data supporting etc. In addition it can offer software on-demand type user services. In order to test the performance of our system, we perform an effectiveness analysis and TTA authentication test. The average response time corresponding to variance of data reveals 0.692 seconds which is very good performance in timing effectiveness point of view. And we detect maturity level-3 or 4 authentication in TTA tests such as SaaS maturity, performance, and application programs.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.1
• /
• pp.169-177
• /
• 2020

In South Korea, the results of R&D in science and technology are submitted to the National Science and Technology Information Service (NTIS) in reports that have Korea national science and technology standard classification codes (K-NSCC). However, considering there are more than 2000 sub-categories, it is non-trivial to choose correct classification codes without a clear understanding of the K-NSCC. In addition, there are few cases of automatic document classification research based on the K-NSCC, and there are no training data in the public domain. To the best of our knowledge, this study is the first attempt to build a highly performing K-NSCC classification system based on NTIS report meta-information from the last five years (2013-2017). To this end, about 210 mid-level categories were selected, and we conducted preprocessing considering the characteristics of research report metadata. More specifically, we propose a convolutional neural network (CNN) technique using only task names and keywords, which are the most influential fields. The proposed model is compared with several machine learning methods (e.g., the linear support vector classifier, CNN, gated recurrent unit, etc.) that show good performance in text classification, and that have a performance advantage of 1% to 7% based on a top-three F1 score.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.6
• /
• pp.295-306
• /
• 2016

The purpose of this paper is both to analyze research trends and to predict industrial flows using the meta-data from the previous studies on data quality. There have been many attempts to analyze the research trends in various fields till lately. However, analysis of previous studies on data quality has produced poor results because of its vast scope and data. Therefore, in this paper, we used a text mining, social network analysis for time series network analysis to analyze the vast scope and data of data quality collected from a Web of Science index database of papers published in the international data quality-field journals for 10 years. The analysis results are as follows: Decreases in Mathematical & Computational Biology, Chemistry, Health Care Sciences & Services, Biochemistry & Molecular Biology, Biochemistry & Molecular Biology, and Medical Information Science. Increases, on the contrary, in Environmental Sciences, Water Resources, Geology, and Instruments & Instrumentation. In addition, the social network analysis results show that the subjects which have the high centrality are analysis, algorithm, and network, and also, image, model, sensor, and optimization are increasing subjects in the data quality field. Furthermore, the industrial connection analysis result on data quality shows that there is high correlation between technique, industry, health, infrastructure, and customer service. And it predicted that the Environmental Sciences, Biotechnology, and Health Industry will be continuously developed. This paper will be useful for people, not only who are in the data quality industry field, but also the researchers who analyze research patterns and find out the industry connection on data quality.

• The Journal of Society for e-Business Studies
• /
• v.27 no.1
• /
• pp.15-28
• /
• 2022

Due to recent expansion of online market such as clothing, utilizing customer review has become a major marketing measure. User review has been used as a tool of analyzing sentiment of customers. Sentiment analysis can be largely classified with machine learning-based and lexicon-based method. Machine learning-based method is a learning classification model referring review and labels. As research of sentiment analysis has been developed, multi-modal models learned by images and video data in reviews has been studied. Characteristics of words in reviews are differentiated depending on products' and customers' categories. In this paper, sentiment is analyzed via considering review data and metadata of products and users. Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Self Attention-based Multi-head Attention models and Bidirectional Encoder Representation from Transformer (BERT) are used in this study. Same Multi-Layer Perceptron (MLP) model is used upon every products information. This paper suggests a multi-modal sentiment analysis model that simultaneously considers user reviews and product meta-information.