• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.177-182
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• 2017

To develop computer and communication in the information society, difficulties exist in managing the enormous data manually. Also, loss of data due to natural disasters or hacker attacks, generate a variety of disasters in the IT securities. Hence, there is an urgent need for an information protection management system in order to mitigate these incidents. Information Security Management System has various existing frameworks for IT disaster management. These include Cyber Security Framework, Risk Management Framework, ISO / IEC 27001: 2013, and COBIT 5.0. Each framework analyses and compares the entry for IT disaster recovery from among the various available data. In this paper, we describe a single integrated management scheme for fast resolution of IT disasters.

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

• Korean Journal of Computational Design and Engineering
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• v.11 no.2
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• pp.88-96
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• 2006

This paper introduces an approach to ontology-based framework for knowledge management in a product development domain. The participants in a product life cycle want to share the product knowledge without any heterogeneity. However, previous knowledge management systems do not have any conceptual specifications of their knowledge. We suggest the three levels of knowledge framework. First level is an axiom, which specifies the semantics of concepts and relations. Second level is a product development knowledge map. It defines the common domain knowledge which domain experts agree with. Third level is a specialized knowledge for domain, which includes three knowledge types; expert knowledge, engineering function and data-analysis-based knowledge. We propose an ontology-based knowledge framework based on the three levels of knowledge. The framework has a uniform representation; first order logic to increase integrity of the framework. We implement the framework using prolog and test example queries to show the effectiveness of the framework.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.8
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• pp.1463-1472
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• 2008

We propose Framework that (should) have data interoperability between Legacy DataBases using Proxy DataBases based on XMDR(eXtended Meta-Data Registry) Data Hub in this papers. It may occur some problems among data structure, semantics and other heterogeneous problems between interoperability of legacy DB on cooperation environment. Also, It is hard to keep consistency of Data that changes on realtime, regardless of data variety and type. In this paper, Using XMDR data hub, we solve the problem that was occurred by data integration and interoperability between legacy DB. We suggest the framework which are compatible with any class and type of interoperability-data and offer accurate information with consistency in real-time using proxy database.

• Spatial Information Research
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• v.22 no.6
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• pp.13-21
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• 2014

This study defines concept and service framework of Geo-Spatial Big Data(GSBD). The major concept of the GSBD is formulated based on the 7V characteristics: the general characteristics of big data with 3V(Volume, Variety, Velocity); Geo-spatial oriented characteristics with 4V(Veracity, Visualization, Versatile, Value). GSBD is the technology to extract meaningful information from Geo-spatial fusion data and support decision making responding with rapidly changing activities by analysing with almost realtime solutions while efficiently collecting, storing and managing structured, semi-structured or unstructured big data. The application area of the GSBD is segmented in terms of technical aspect(store, manage, analyze and service) and public/private area. The service framework for the GSBD composed of modules to manage, contain and monitor GSBD services is suggested. Such additional studies as building specific application service models and formulating service delivery strategies for the GSBD are required based on the services framework.

• Journal of the Korean Society for information Management
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• v.33 no.1
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• pp.293-316
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• 2016

This study aims to improve bibliographic data of public libraries by linking service data, which are produced out of library service programs. Service data collected from the seven award-winning public libraries were selected and analyzed. A Bibliographic Framework is used for linking bibliographic data and service data. Interfaces are also suggested for the two-way data linking. The results can be used to obtain 1) selective and value-added bibliographic data, 2) bibliographic data updated continuously throughout the lifecycle, 3) structured service data for preservation and sharing, and 4) bibliographic data linked to the additional external linked data.

• Journal of information and communication convergence engineering
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• v.8 no.2
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• pp.129-135
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• 2010

Wireless sensor networks (WSNs) are an emerging technology and offers economically viable monitoring solution to many challenging applications. However, deploying new technology in hostile environment, without considering security in mind has often proved to be unreasonably unsecured. Apparently, security techniques face many critical challenges in WSNs like data security and secrecy due to its hostile deployment nature. In order to resolve security in WSNs, we propose a novel and efficient secure framework called TriSec: a secure data framework for wireless sensor networks to attain high level of security. TriSec provides data confidentiality, authentication and data integrity to sensor networks. TriSec supports node-to-node encryption using PingPong-128 stream cipher based-privacy. A new PingPong-MAC (PP-MAC) is incorporated with PingPong stream cipher to make TriSec framework more secure. PingPong-128 is fast keystream generation and it is very suitable for sensor network environment. We have implemented the proposed scheme on wireless sensor platform and our result shows their feasibility.

• The Journal of Information Systems
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• v.26 no.2
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• pp.105-121
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• 2017

Purpose It is a major goal to improve the product yields during production operations in the manufacturing industry. Therefore, factory is trying to keep the good quality materials and proper production resources, also find the proper condition of facilities and manufacturing environment for yields improvement. Design/methodology/approach We propose the hybrid framework to analyze to dataset extracted from MES. Those data is about the alarm information generated from equipment, both measurement and equipment process value from production and cycle/pitch time measured from production data these covered products during production. We adapt a data warehousing techniques for organizing dataset, a logistic regression for finding out the significant factors, and a association analysis for drawing the rules which affect the product yields. And then we validate the framework by applying the real data generated from the discrete process in secondary cell battery manufacturing. Findings This paper deals with challenges to apply the full potential of modeling and simulation within CPPS(Cyber-Physical Production System) and Smart Factory implementation. The framework is being applied in one of the most advanced and complex industrial sectors like semiconductor, display, and automotive industry.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.116-118
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• 2018

This paper introduces oneM2M global IoT standard middleware platform based smart city service data model support and FIWARENGSI interworking for Orion Broker. Also, service data models implementation using flexContainer resource types and FIWARENGSI interworking using the Interworking Framework (TS-0033) from oneM2M Release 3 are also illustrated. In this system, data model element level access (e.g. for subscription/notification feature) is enabled, which is enhancement compared to contentInstance resource type before, and service agnostic FIWARE NGSI interworking is provided over the Interworking Framework for smart city platform data interworking.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.81-86
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• 2019

Deep learning is emerging as one of the best tool in processing data related to medical imaging. In our research work, we have proposed a deep learning based framework CNN (Convolutional Neural Network) for the classification of dysplastic tissue images. The CNN has classified the given images into 4 different classes namely normal tissue, mild dysplastic tissue, moderate dysplastic tissue and severe dysplastic tissue. The dataset under taken for the study consists of 672 tissue images of epithelial squamous layer of oral cavity captured out of the biopsy samples of 52 patients. After applying the data pre-processing and augmentation on the given dataset, 2688 images were created. Further, these 2688 images were classified into 4 categories with the help of expert Oral Pathologist. The classified data was supplied to the convolutional neural network for training and testing of the proposed framework. It has been observed that training data shows 91.65% accuracy whereas the testing data achieves 89.3% accuracy. The results produced by our proposed framework are also tested and validated by comparing the manual results produced by the medical experts working in this area.