• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.192-199
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• 2003

Agents in multi-agent systems (MAS ) are required to achieve their own goals. An agent s goal, however, can conflict with others either when agents compete with each other to achieve a common goal or when they have to use a set of limited resources to accomplish agents divergent goals. In either case, agents need to be designed to reach a mutual acceptable state where they can avoid any goal conflicts through negotiation with others to achieve their goals. In this paper, we consider a BDI agent architecture where belief, desire, and intention are the three major components for agents mental attitudes and represent resource-bounded BDI agents in logic programming framework. We propose a negotiation algorithm for BDI agents solving their problems without goal conflicts in distributed cooperative environments. Finally, we describe a simple scenario to show the effectiveness of the negotiation algorithm implemented in a negotiation meta-language.

• Journal of Advanced Navigation Technology
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• v.18 no.4
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• pp.401-408
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• 2014

Hadoop is composed with MapReduce programming model for distributed processing and HDFS distributed file system. Hadoop is suitable framework for big data processing, but processing of mass small files have many problems. The processing of mass small file in hadoop have problems to created one mapper per one file, and it have problems to needed many memory for store of meta information of file. This paper have comparison evaluation processing method of mass small file with various method in hadoop platform. The processing of general compression format is inadequate because of processing by one mapper regardless of data size. The processing of sequence and hadoop archive file is removed memory problem of namenode by compress and combine of small file. Hadoop archive file is faster then sequence file about combine time of small file. The processing using CombineFileInputFormat class is needed not combine of small file, and it have similar speed big data processing method.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.395-405
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• 2001

In this paper, we analyze workflow management system in programming language aspects. Many workflow characteristics such as workflow relevant data, workflow control structures, and workflow application invocations are studied and compared with those of distributed parallel programs. Although there exist minor differences between them, we found that there exist surprisingly many analogies between them. Based on this observation, we suggest to view workflow management system as distributed parallel program development platform. This new view on workflow management system provides users consistent view on workflow and workflow management system and with this view workflow management system designer can cope with arbitrary requests from the users keeping design consistency. Moreover the analogy between workflow and program provides a basis to apply program analysis techniques to the analysis of workflow.

• Journal of KIISE
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• v.43 no.1
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• pp.61-70
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• 2016

In recent years, there has been a need for techniques for large-scale ontology inference in order to infer new knowledge from existing knowledge at a high speed, and for a diversity of semantic services. With the recent advances in distributed computing, developments of ontology inference engines have mostly been studied based on Hadoop or Spark frameworks on large clusters. Parallel programming techniques using GPGPU, which utilizes many cores when compared with CPU, is also used for ontology inference. In this paper, by combining the advantages of both techniques, we propose a new method for reasoning large RDFS ontology data using a Spark in-memory framework and inferencing distributed data at a high speed using GPGPU. Using GPGPU, ontology reasoning over high-capacity data can be performed as a low cost with higher efficiency over conventional inference methods. In addition, we show that GPGPU can reduce the data workload on each node through the Spark cluster. In order to evaluate our approach, we used LUBM ranging from 10 to 120. Our experimental results showed that our proposed reasoning engine performs 7 times faster than a conventional approach which uses a Spark in-memory inference engine.

• Journal of KIISE
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• v.42 no.7
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• pp.852-859
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• 2015

As the amount of knowledge information significantly increases, a lot of progress has been made in the studies focusing on how to reason large scale ontology effectively at the level of RDFS or OWL. These reasoning methods are divided into TBox classifications and ABox realizations. A TBox classification mainly deals with integrity and dependencies in schema, whereas an ABox realization mainly handles a variety of issues in instances. Therefore, the ABox realization is very important in practical applications. In this paper, we propose a realization method for analyzing the constraint of the specified class, so that the reasoning system automatically infers the classes to which instances belong. Unlike conventional methods that take advantage of the object oriented language based distributed file system, we propose a large scale ontology reasoning method using spark, which is a functional programming-based in-memory system. To verify the effectiveness of the proposed method, we used instances created from the Wine ontology by W3C(120 to 600 million triples). The proposed system processed the largest 600 million triples and generated 951 million triples in 51 minutes (696 K triple / sec) in our largest experiment.

• The Journal of the Korea Contents Association
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• v.8 no.12
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• pp.441-448
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• 2008

The molecular docking system needs a large amount of computation and requires super-computing power. Since the experiment requires a large amount of time, the experiment is conducted in the distributed environment or in the grid environment. Recently, researches on using parallel GPU of far higher performance than that of CPU in scientific computing have been very actively conducted. CUDA is an open technique by which a parallel GPU programming is made possible. This study proposes the molecular docking system using CUDA. It also proposes algorithm that parallels energy-minimizing-computation. To verify such experiments, this study conducted a comparative analysis on the time required for experimenting molecular docking in general CPU and the time and performance of the parallel GPU-based molecular docking which is proposed in this study.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06a
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• pp.218-220
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• 2012

인터넷 활용과 웹 어플리케이션의 개발이 증가함에 따라 처리해야하는 데이터의 양도 또한 증가하고 있다. 대량의 데이터를 효과적으로 처리하기 위한 방법 중 하나로 병렬처리 프로그래밍 모델인 맵리듀스가 있다. 하둡은 맵리듀스의 오픈소스 구현으로 대량의 데이터를 병렬로 처리하는 무료 자바 소프트웨어 프레임워크이다. 분산 파일 시스템을 사용하는 하둡에서는 처리하는 데이터가 다른 노드에 위치하는 데이터 로컬리티 문제가 전체 작업 수행시간의 증가를 야기하는 문제가 있다. 본 논문에서는 하둡에서의 데이터 로컬리티 문제를 해결하기 위한 데이터 복제기법을 제안한다. 제안하는 데이터 복제기법에서는 1) 라그랑지 보간법을 사용하여 과거 접근수를 이용한 미래 접근수를 예측하고, 2) 예측된 값을 Threshold값으로 설정하고, 3) 데이터 로컬리티 문제가 발생하였을 때, 복제사본을 생성할 것인지 캐시를 생성할 것인지를 결정하여 복제 사본의 수를 최적화 한다. 실험을 통해 단순히 복제사본 수를 증가시킴으로써 데이터 로컬리티를 향상을 이루어도 작업 완료시간이 감소하는 것이 아니라는 결과를 볼 수 있었고, 오버 런치로 인한 작업 완료시간 증가를 줄이기 위해 데이터 복제사본 수 최적화의 필요성을 확인할 수 있었다.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.969-979
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• 2004

Recently, large-scale enterprise information systems are commonly based on the multi-tiered middleware or frameworks to support such requirements as functional reuse, heterogeneous system resources, and multiple platforms. However, these multi-tiered or distributed multi-platform architecture incurs the interoperability issue of the components and metadata among the middleware. This paper introduces the Simple Object Application Framework (SOAF) which supports heterogeneous resources and platform-independent component-based development, with the abstract programming style of the object-oriented frameworks and the XML-SOAP based component persistence mechanism.

• Journal of Internet Computing and Services
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• v.3 no.2
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• pp.39-56
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• 2002

In distributed computing environments, there are many database applications that should share data each other such as data warehousing and data mining with autonomy on local databases. The first step to such applications is the integration of heterogeneous database schema, but there is no accepted common data model for the integration and also are difficulties on the construction of integration program. In this paper, we use the XML Schema for the representation of common data model and exploit XSLT for reducing the programming difficulties. We define the schema integration operations and develop a methodology for the semi-automatic schema integration according to schema conflicts types. Our integration method has benefits on standardization, extendibility on schema integration process comparing to existing methodologies.

• The KIPS Transactions:PartA
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• v.11A no.6
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• pp.439-450
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• 2004

Mobile agents are autonomous and dynamic entities that can migrate among various nodes in the network. Java's Jini framework facilitates mobile agent system development, providing hey features for distributed network programming. However, due to the security weakness, Jinil.0 service has a fundamental limitation on developing mobile agent systems which support secure remote communications. In this paper, we describe a Jini2.0-based secure mobile agent system named SecureJMoblet. On the top of Jini2.0, the system provides basic functionalities of a mobile agent system such as creation, transfer and control. In addition, with the SeureJS developed for secure JavaSpace service, SecureJMoblet supports a secure object repository and a reliable communication among mobile agents.