• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.8B
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• pp.539-546
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• 2007

For better performance and to avoid member service annoyance that results due to joining-clients' waiting durations and time-outs when there are more than one client wanting to join concurrently for Personal Broadcasting System service, there is a need for improving concurrent member joining mechanism. For a more efficient and better performing, this paper apply Overlay Multicast based mini-Overlay Broadcasting Control Protocol(mOBCP) Algorithm on Personal Broadcasting System. mOBCP proposed is performance-effective mechanism, since it considers the case of how fast will children, concurrently, find and join new parents when paths to existing parents are in Failure. The performance comparison, in terms of tree construction time variation and Latency are done through simulations and the results conclude in favour of the Proposed mOBCP.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.177-194
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• 2007

Index selection is one of the most important decisions to take in the physical design of relational data warehouses. Indices reduce significantly the cost of processing complex OLAP queries, but require storage cost and induce maintenance overhead. Two main types of indices are available: mono-attribute indices (e.g., B-tree, bitmap, hash, etc.) and multi-attribute indices (join indices, bitmap join indices). To optimize star join queries characterized by joins between a large fact table and multiple dimension tables and selections on dimension tables, bitmap join indices are well adapted. They require less storage cost due to their binary representation. However, selecting these indices is a difficult task due to the exponential number of candidate attributes to be indexed. Most of approaches for index selection follow two main steps: (1) pruning the search space (i.e., reducing the number of candidate attributes) and (2) selecting indices using the pruned search space. In this paper, we first propose a data mining driven approach to prune the search space of bitmap join index selection problem. As opposed to an existing our technique that only uses frequency of attributes in queries as a pruning metric, our technique uses not only frequencies, but also other parameters such as the size of dimension tables involved in the indexing process, size of each dimension tuple, and page size on disk. We then define a greedy algorithm to select bitmap join indices that minimize processing cost and verify storage constraint. Finally, in order to evaluate the efficiency of our approach, we compare it with some existing techniques.

• Journal of Korea Spatial Information System Society
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• v.4 no.1 s.7
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• pp.39-46
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• 2002

In distributed spatial database systems, users nay issue a query that joins two relations stored at different sites. The sheer volume and complexity of spatial data bring out expensive CPU and I/O costs during the spatial join processing. This paper shows a new spatial join method which joins two spatial relation in a parallel way. Firstly, the initial join operation is divided into two distinct ones by partitioning one of two participating relations based on the region. This two join operations are assigned to each sites and executed simultaneously. Finally, each intermediate result sets from the two join operations are merged to an ultimate result set. This method reduces the number of spatial objects participating in the spatial operations. It also reduces the scope and the number of scanning spatial indices. And it does not materialize the temporary results by implementing the join algebra operators using the iterator. The performance test shows that this join method can lead to efficient use in terms of buffer and disk by narrowing down the joining region and decreasing the number of spatial objects.

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.591-602
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• 2003

Delay and discontinuance phenomenon of service are cause by sudden increase of the network communication amount and the quantity consumed of resources when Internet users are driven excessively to a conventional single large database sewer. To solve these problems, spatial database cluster consisted of several single nodes on high-speed network to offer high-performance is risen. But, research about spatial join operation that can reduce the performance of whole system in case process at single node is not achieved. So, in this paper, we propose efficient parallel spatial join processing method in a spatial database cluster system that uses data partitions and replications method that considers the characteristics of space data. Since proposed method does not need the creation step and the assignment step of tasks, and does not occur additional message transmission between cluster nodes that appear in existent parallel spatial join method, it shows performance improvement of 23% than the conventional parallel R-tree spatial join for a shared-nothing architecture about expensive spatial join queries. Also, It can minimize the response time to user because it removes redundant refinement operation at each cluster node.

• The KIPS Transactions:PartD
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• v.9D no.1
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• pp.21-30
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• 2002

The active database system introduces the active rules detecting specified state. As the condition evaluation of the active rules is performed every time an event occurs, the performance of the system has a great influence, depending on the conditions processing method. In this paper, we propose the conditions processing system with the preprocessor which determines the delta tree structure, constructs the classification tree, and generates the aggregate function table. Due to the characteristics of the active database through which the active rules can be comprehended beforehand, the preprocessor can be introduced. In this paper, the delta tree which can effectively process the join, selection operations, and the aggregate function is suggested, and it can enhance the condition evaluation performance. And we propose the classification tree which effectively processes the join operation and the aggregate function table processing the aggregate function which demands high cost. In this paper, the conditions processing system can be expected to enhance the performance of conditions processing in the active rules as the number of conditions comparison decreases because of the structure which is made in the preprocessor.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.732-741
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• 2001

In the relational database systems the join operation is one of the most time-consuming query operations. Many parallel join algorithms have been developed 개 reduce the execution time Multiple hash join algorithm using allocation tree is one of the most efficient ones. However, it may have some delay on the processing each node of allocation tree, which is occurred in tuple-probing phase by the difference between one page reading time of outer relation and the processing time of already read one. This delay problem was solved by using the concept of synchronization of page execution time with we had proposed In this paper the effects of the performance improvements in each node of the allocation tree are extended to the whole allocation tree and the performance evaluation about that is processed. In addition we propose an efficient algorithm for multiple hash joins in limited number of processor environments according to the relationship between the number of input relations in the allocation tree and the number of processors allocated to the tree. Finally. we analyze the performance by building the analytical cost model and verify the validity of it by various performance comparison with previous method.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.45-52
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• 2020

This paper proposes a solution for storage and retrieval problems for Resource Description Framework (RDF) data utilizing a key-value Solid State Device (SSD), considering storage, retrieval performance, and security. We propose a two-step compression algorithm to separate logical relationship and true values from RDF data-sets using the key-value SSD. This improves not only compression and storage efficiency but also storage security. We also propose a hybrid retrieval structure based on R∗-tree to enhance retrieval efficiency and implement a sort-merge join algorithm, and discuss factors affecting R∗-tree retrieval efficiency. Finally, we show the proposed approach is superior to current compression, storage, and retrieval approaches, obtaining target results faster while requiring less space, and competitive in terms of versatility, flexibility and security.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.70-72
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• 2003

대용량 데이터베이스로부터 순차 패턴을 발견하는 문제는 지식 발견 또는 데이터 마이닝(Data Mining) 분야에서 주요한 패턴 추출 문제이다. 순차 패턴은 추출 기법에 있어 연관 규칙의 Apriori 알고리즘과 비슷한 방식을 사용하며 그 과정에서 시퀀스는 해쉬 트리 구조를 통해 다루어 진다. 이러한 해쉬 트리 구조는 항목들의 정렬과 데이터 시퀀스의 지역성을 무시한 저장 구조로 단순 검색을 통한 다수의 복잡한 포인터 연산수행을 기반으로 한다. 본 논문에서는 이러한 해쉬 트리 구조의 단정을 보완한 다단게 선형 배치 트리(MLLT, Multi-level Linear Location Tree)를 제안하고, 다단계 선형 배치 트리를 이용한 효율적인 마이닝 메소드(MLLT-Join)를 소개한다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.67-69
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• 2002

여러 사이트에서 구축된 공간 데이터를 효율적으로 관리 및 공유하기 위해서는 대용량의 정보를 처리할 수 있는 분산 공간 데이터베이스 시스템의 사용이 필수적이다. 이러한 분산 공간 데이터베이스 시스템상에서의 분산 공간 죠인 질의는 공간 데이터의 대용량성과 그 복잡성으로 인하여 공간 연산의 지펴져 부하와 네트워크상의 전송 부하를 발생시킨다. 이러한 문제를 해결하기 위해 세미죠인 기반의 공간 죠인 기법들이 제안되었으나 공간 죠인 연산을 특정 서버에서만 수행하여 병목현상을 발생시키기 때문에 결국 질의 처리시간이 증가된다. 본 논문은 이러한 분산 공간 데이터베이스 시스템에서 수행 비용이 많이 드는 원격 사이트간의 공간 죠인 연산에 대해 R+-tree 공간 색인을 사용하여 병렬적으로 수행하는 기법을 제안한다. 본 기법은 R+-tree 공간 색인을 이용하여 공간 죠인 연산의 대상이 되는 릴레이션들을 중첩이 없는 두개의 릴레이션들로 분할한 후 질의 수행에 참여하는 두 서버에 죠인 연산을 분배하고 병렬적으로 처리하여 결과들을 병합한 다음 클라이언트에게 전송만다. 본 기법은 릴레이션을 분할하여 각 서버에서 공간 죠인 연산을 병렬적으로 처리하므로 처리 비용을 절반으로 감소시키며. R+-tree의 영역에 해당하는 객체들만 죠인 연산에 참여하게 함으로써 네트워크 전송 비용을 감소시킨다

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.2
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• pp.178-194
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• 2009

Current group key agreement protocols(often tree-based) involve unnecessary delays because members with low-performance computer systems can join group key computation. These delays are caused by the computations needed to balance a key tree after membership changes. An alternate approach to group key generation that reduces delays is the dynamic prioritizing mechanism of filtering low performance members in group key generation. This paper presents an efficient tree-based group key agreement protocol and the results of its performance evaluation. The proposed approach to filtering of low performance members in group key generation is scalable and it requires less computational overhead than conventional tree-based protocols.