• Journal of KIBIM
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• v.10 no.3
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• pp.43-53
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• 2020

BIM-based Cloud platform gained popularity coupled with the convergence of Fourth Industrial Revolution technology. However, most of the previous work has not guaranteed sufficient efficiency to meet user requirements according to BIM service. Furthermore, the Cloud environment is only used as a server and it does not consider cloud characteristics. For the processing of High Capacity Data like BIM and using seamless BIM service, Resource management technology is required in the cloud environment. In this paper, to solve the problems, we propose a BIM platform for BaaS and an efficient resource allocation scheme. We also proved the efficiency of resource for the proposed scheme by using existing schemes. By doing this, the proposed scheme looks forward to accelerating the growth of the BaaS through improving the user experience and resource efficiency.

• Structural Engineering and Mechanics
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• v.37 no.1
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• pp.17-37
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• 2011

This paper presents a new Internet-based computational framework for the realistic simulation of multi-scale response of structural systems. Two levels of parallel processing are involved in this frame work: multiple local distributed computing environments connected by the Internet to form a cluster-to-cluster distributed computing environment. To utilize such a computing environment for a realistic simulation, the simulation task of a structural system has been separated into a simulation of a simplified global model in association with several detailed component models using various scales. These related multi-scale simulation tasks are distributed amongst clusters and connected to form a multi-level hierarchy. The Internet is used to coordinate geographically distributed simulation tasks. This paper also presents the development of a software framework that can support the multi-level hierarchical simulation approach, in a cluster-to-cluster distributed computing environment. The architectural design of the program also allows the integration of several multi-scale models to be clients and servers under a single platform. Such integration can combine geographically distributed computing resources to produce realistic simulations of structural systems.

• KIISE Transactions on Computing Practices
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• v.22 no.11
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• pp.601-613
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• 2016

With the increasing need for big data processing, building a shared big data processing platform is important to minimize time and monetary costs. In shared big data processing, multitenancy is a major requirement that needs to be addressed, in order to provide a single isolated personal big data platform for each user, but to share the underlying hardware is shared among users to increase hardware utilization. In this paper, we explore two well-known shared big data processing platform models. One is to use a native Hadoop cluster, and the other is to build a virtual Hadoop cluster for each user. For each model we verified whether it is sufficient to support multi-tenancy. We also present a method to complement unsupported multi-tenancy features in a native Hadoop cluster model. Lastly we built prototype platforms and compared the performance of both models.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.633-636
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• 2004

웹 서비스는 이질적인 응용 시스템들 사이의 연동 및 통합을 위한 표준화된 수단을 제공한다. 본 논문에서는 기존 웹 서비스들을 이용하여 정의되는 복합 웹 서비스를 효율적으로 실행하기 위한 방안으로서 사용자 에이전트를 이용한 분산 처리 시스템 구조 및 처리 방법을 제안한다. 본 시스템은 웹 서비스들의 통신 QoS 및 복합 웹 서비스의 부하 등을 고려하여 복합 웹 서비스의 호출 및 통합 작업을 사용자 에이전트에게 동적으로 위임하여 분산 처리함으로써 복합 웹 서비스의 성능 및 가용성을 향상시킨다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.2B
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• pp.117-128
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• 2003

The network management becomes more complicated due to the growth of network technology and introduction of a large number of new multimedia services. TINA has appeared as a concept for the advanced network management system platform using the information technology such as distributed processing and object oriented modeling. Since TINA is on the basis of DPE (Distributed Processing Environment), it can manage networks and services for open telecommunications. In this paper, we propose an implementation strategy of the MIB/MIT to federate various CORBA objects in CORBA based network management system implementing TINA DPE. The proposed strategy is novel in the sense that it can support the distribution of MIB/MIT that is well matched with the distributed component architecture of TINA DPE, and it can also support the scoping and filtering services on the MIT using CORBA Naming Service.

• Journal of Digital Contents Society
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• v.18 no.7
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• pp.1341-1346
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• 2017

In this paper, we designed the open service platform based on integrated type of individual customized service and intelligent information technology with individual's complex attributes and requests. First, the data collection phase is proceed quickly and accurately to repeat extraction, transformation and loading. The generated data from extraction-transformation-loading process module is stored in the distributed data system. The data analysis phase is generated a variety of patterns that used the analysis algorithm in the field. The data processing phase is used distributed parallel processing to improve performance. The data providing should operate independently on device-specific management platform. It provides a type of the Open API.

• Journal of Advanced Research in Ocean Engineering
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• v.3 no.1
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• pp.32-40
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• 2017

Recently, the amount of data to be processed and the complexity thereof have been increasing due to the development of information and communication technology, and industry's interest in such big data is increasing day by day. In the shipbuilding and offshore industry also, there is growing interest in the effective utilization of data, since various and vast amounts of data are being generated in the process of design, production, and operation. In order to effectively utilize big data in the shipbuilding and offshore industry, it is necessary to store and process large amounts of data. In this study, it was considered efficient to apply Hadoop and R, which are mostly used in big data related research. Hadoop is a framework for storing and processing big data. It provides the Hadoop Distributed File System (HDFS) for storing big data, and the MapReduce function for processing. Meanwhile, R provides various data analysis techniques through the language and environment for statistical calculation and graphics. While Hadoop makes it is easy to handle big data, it is difficult to finely process data; and although R has advanced analysis capability, it is difficult to use to process large data. This study proposes a big data platform based on Hadoop for applications in the shipbuilding and offshore industry. The proposed platform includes the existing data of the shipyard, and makes it possible to manage and process the data. To check the applicability of the platform, it is applied to estimate the weights of offshore structure topsides. In this study, we store data of existing FPSOs in Hadoop-based Hortonworks Data Platform (HDP), and perform regression analysis using RHadoop. We evaluate the effectiveness of large data processing by RHadoop by comparing the results of regression analysis and the processing time, with the results of using the conventional weight estimation program.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.241-246
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• 2020

Hadoop is a framework to process large data sets in a distributed way across clusters of nodes. It has been a popular platform to process big data, but in recent years, other platforms became competitive ones depending on the characteristics of the application. Spark is one of distributed platforms to enable real-time data processing and improve overall processing performance over Hadoop by introducing in-memory processing instead of disk I/O. Whereas Hadoop is designed to work on Java and data analysis is processed using Java API, Spark provides a variety of APIs with Scala, Python, Java and R. In this paper, the goal is to find out whether the APIs of different programming languages af ect the performances in Spark. We chose two popular APIs: Python and Scala. Python is easy to learn and is used in AI domain in a wide range. Scala is a programming language with advantages of parallelism. Our experiment shows much faster processing with Scala API than Python API. For the performance issues on AI-based analysis, further study is needed.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.4
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• pp.207-212
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• 2003

System analysis programs have been developed by several different research groups. Especially, the programming languages and the developing environments of algorithm modules and user interface modules are different. And therefore, the differences have degraded interoperability and reusability of the system analysis modules. in order to solve this problems, a general binary interface has designed and the component based on the interface has developed as well. However, sometimes each interface is uncompatible because those are designed on the particular vendor. In this paper, we deals with XML web service, Sort of distributed processing architecture, which is not restricted by not only the existing internet standard but also any Programming language or any vendor. Because of its platform independent, each module can be updated and extended independently.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.351-359
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• 2014

The log data generated by security equipment have been synthetically analyzed on the ESM(Enterprise Security Management) base so far, but due to its limitations of the capacity and processing performance, it is not suited for big data processing. Therefore the another way of technology on the big data platform is necessary. Big Data platform can achieve a large amount of data collection, storage, processing, retrieval, analysis, and visualization by using Hadoop Ecosystem. Currently ESM technology has developed in the way of SIEM (Security Information & Event Management) technology, and to implement security technology in SIEM way, Big Data platform technology is essential that can handle large log data which occurs in the current security devices. In this paper, we have a big data platform Hadoop Ecosystem technology for analyzing the security log for sure how to implement the system model is studied.