• Computational Structural Engineering
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• v.4 no.1
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• pp.42-43
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• 1991

균열성암반의 모형화 기술은 계속적으로 보완발전되어 UDEC이 개발되었으며, 현재 UDEC의 최신판은 블록 내부를 다시 유한차분요소로 분할하여 블록의 소성거동(Mohr-Coulomb Model) 및 쪼개짐을 고려할 수 있고, 절리면에서의 유체흐름 및 유압의 발생, 그리고 열응력 해석 등 평면변형 문제의 정적해석과 지진 및 폭발하중을 고려한 동적해석이 가능하다. UDEC은 전처리 기능이 뛰어나 최소한의 입력데이타로써 전체 모형의 데이타를 자동생성시키며 절리면의 통계학적 자동생성 및 터널형상의 자동생성도 가능하다. UDEC은 실용적인 보강요소를 구비하여 Rock Bolt 뿐만 아니라 그라우트를 고려한 Cable Bolt를 모형화할 수 있으며 국부적인(Key Block)보강으로써 불연속체 전체의 안정을 검토할 수 있다.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.10
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• pp.569-578
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• 2003

Data dependencies are one of major hurdles to limit ILP(Instruction Level Parallelism), so several related works have suggested that the limit imposed by data dependencies can be overcome to some extent with use of the data value prediction. Hybrid value predictor can obtain the high prediction accuracy using advantages of various predictors, but it has a defect that same instruction has overlapping entries in all predictor. In this paper, we propose a new hybrid value predictor which achieves high performance by using the information of static and dynamic classification. The proposed predictor can enhance the prediction accuracy and efficiently decrease the prediction table size of predictor, because it allocates each instruction into single best-suited predictor during the fetch stage by using the information of static classification. Also, it can enhance the prediction accuracy because it selects a best- suited prediction method for the “Unknown”pattern instructions by using the dynamic classification mechanism. Simulation results based on the SimpleScalar/PISA tool set and the SPECint95 benchmarks show the average correct prediction rate of 85.1% by using the static classification mechanism. Also, we achieve the average correction prediction rate of 87.6% by using static and dynamic classification mechanism.

• Journal of KIISE:Databases
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• v.32 no.6
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• pp.567-584
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• 2005

Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from temporal data. Association rules and classification are applied to various applications which are the typical data mining problems. However, these approaches do not consider temporal attribute and have been pursued for discovering knowledge from static data although a large proportion of data contains temporal dimension. Also, data mining researches from temporal data treat problems for discovering knowledge from data stamped with time point and adding time constraint. Therefore, these do not consider temporal semantics and temporal relationships containing data. This paper suggests that temporal associative classification technique based on temporal class association rules. This temporal classification applies rules discovered by temporal class association rules which extends existing associative classification by containing temporal dimension for generating temporal classification rules. Therefore, this technique can discover more useful knowledge in compared with typical classification techniques.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.138-149
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• 2006

As the environment with sensor network and ubiquitous computing is emerged, there are many demands of handling continuous, fast data such as streaming data. As work about streaming data has begun, work about management of streaming data in Publish-Subscribe system is started. The recent emergence of XML as a standard for information exchange on Internet has led to more interest in Publish - Subscribe system. A filtering technique of streaming XML data in the existing Publish- Subscribe system is using some schemes based on automata and YFilter, which is one of filtering techniques, is very popular. YFilter exploits commonality among path queries by sharing the common prefixes of the paths so that they are processed at most one and that is using the top-down approach. However, because partial matching path queries interrupt the common prefix sharing and don't calculate from root, throughput of YFilter decreases. So we use sharing of commonality among path queries with the common postfixes of the paths and use the bottom-up approach instead of the top-down approach. This filtering technique is called as PoSFilter. And we verify this technique through comparing with YFilter about throughput.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.667-676
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• 2004

This paper uses the standard technique of data flow analysis to solve the problem of secure information-flow in core imperative programs. The existing methods tend to be overly conservative, giving “insecure” answers to many “secure” programs. The method described in this paper is designed to be more precise than previous syntactic approaches. The soundness of the analysis is proved.

• Journal of KIISE:Databases
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• v.33 no.3
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• pp.327-341
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• 2006

As we are getting to deal with more applications that generate streaming data such as sensor network, monitoring, and SDI (selective dissemination of information), active research is being conducted to support efficient processing of queries over streaming data. The applications on the Web environment like SDI, among others, require query processing over streaming XML data, and its investigation is very important because XML has been established as the standard for data exchange on the Web. One of the major problems with the previous systems that support query processing over streaming XML data is that they cannot deal adaptively with dynamically changing stream because they rely on static query plans. On the other hand, the stream query processing systems based on relational data model have achieved adaptiveness in query processing due to query operator routing. In this paper, we propose a system of adaptive query processing over streaming XML data in which the model of adaptive query processing over streaming relational data is applied. We compare our system with YFiiter, one of the representative systems that provide XML stream query processing capability, to show efficiency of our system.

• Journal of KIISE:Software and Applications
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• v.36 no.1
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• pp.10-20
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• 2009

As unit testing should be performed repeatedly and continuously, it is a high-cost software development activity. Although there are many studies on unit test automation, there are less studies on automated test case generation which are worthy of note. In this paper, we discuss a study on automated test data generation from source codes and indicate algorithms for each stage. We also show some issues of test data generation and introduce an automated test data generating tool: CUTIG. As CUTIG generates test data not from require specifications but from source codes, software developers could generate test data when specifications are insufficient or discord with real implementation. Moreover we hope that the tool could help software developers to reduce cost for test data preparation.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06d
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• pp.52-54
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• 2006

최근 데이타 중심 저장 방식의 센서 네트워크에서 다차원 범위 질의를 위한 인덱스들이 제시되고 있다. 기존에 제시된 다차원 범위 질의 인덱스는 일반적으로 다차원 속성 도메인과 센서 노드의 공간 도메인을 직접 매핑하여 데이타를 관리하는 구조로 되어있다. 그러나, 이러한 구조는 센서 노드의 공간 도메인을 정적으로 분할하기 때문에 센서 노드를 포함하지 않는 영역이 생성되어 데이타 저장 및 질의 처리에서 불필요한 통신이 발생하는 문제가 있다. 본 논문은 이러한 문제를 해결하기 위해 센서 노드의 공간 도메인이 센서 노드를 포함하도록 센서 네트워크 영역을 동적으로 분할하는 다차원 범위 질의 인덱스를 제안한다. 제안하는 인덱스는 센서 노드의 위치에 따라 센서 네트워크 영역을 동적으로 분할하여 데이타 저장 및 질의 처리시 목적 영역으로의 라우팅 경로를 최적화한다. 그리고, 분할된 영역은 모두 센서 노드를 포함함으로 센서 노드에서 발행하는 저장 부하를 분산시켜 전체 네트워크에서 발생하는 전체 통신비용을 줄인다. 실험 결과 제안한 인덱스는 DIM보다 전체 센서 네트워크와 hotspot의 통신비용에서 각각 최대 35%, 60%의 성능 향상을 보였다.

• Journal of KIISE:Databases
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• v.31 no.3
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• pp.261-273
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• 2004

Advances in information and communication technologies have been creating new classes of applications in the area of databases. For example, in moving object databases, which track positions of a lot of objects, or stream databases, which process data streams from a lot of sensors, data Processed in such database systems are usually changed very rapidly and continuously. However, traditional database systems have a problem in processing these rapidly and continuously changing data because they suppose that a data item stored in the database remains constant until It is explicitly modified. The problem becomes more serious in the R-tree, which is a typical index structure for multidimensional data, because modifying data in the R-tree can generate cascading node splits or merges. To process frequent updates more efficiently, we propose a novel update technique for the R-tree, which we call the leaf-update technique. If a new value of a data item lies within the leaf MBR that the data item belongs, the leaf-update technique changes the leaf node only, not whole of the tree. Using this leaf-update manner and the leaf-access hash table for direct access to leaf nodes, the proposed technique can reduce update cost greatly. In addition, the leaf-update technique can be adopted in diverse variants of the R-tree and various applications that use the R-tree since it is based on the R-tree and it guarantees the correctness of the R-tree. In this paper, we prove the effectiveness of the leaf-update techniques theoretically and present experimental results that show that our technique outperforms traditional one.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.1
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• pp.107-115
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• 2002

We propose a new hybrid value predictor which achieves high performance by combining several predictors. Because the proposed hybrid value predictor can update the prediction table speculatively, it efficiently reduces the number of mispredicted instructions due to stale data. Also, the proposed predictor can enhance the prediction accuracy and efficiently decrease the hardware cost of predictor, because it allocates instructions into the best-suited predictor during instruction fetch stage by using the information of static classification which is obtained from the profile-based compiler implementation. For the 16-issue superscalar processors, simulation results based on the SimpleScalar/PISA tool set show that we achieve the average prediction rates of 73% by using speculative update and the average prediction rates of 88% by adding static classification for the SPECint95 benchmark programs.