• Proceedings of the Korean Information Science Society Conference
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• 2006.06d
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• pp.262-264
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• 2006

최근 wired 및 wireless 환경에서 멀티미디어 데이터 전송에 대한 수요가 증가하고 있다. 또한 네트워크의 대역폭 효율성을 위해 멀티캐스트 전송이 요구되었고, 신뢰적인 멀티캐스트 전송 프로토콜인 RMT 프로토콜이 등장하게 되었다. FLUTE는 ALC 기반의 어플리케이션으로 LAN 환경 및 3GPP 및 DVB-H에서 멀티캐스트 콘텐츠 전송을 위해 사용하고 있다. FLUTE는 동일한 멀티캐스트 세션에서 FDT 인스턴스를 전송한 후에 콘텐츠를 전송한다. 그러나 FDT 인스턴스를 수신하지 못하면 콘텐츠를 수신할 수 없기 때문에, FDT 인스턴스 전송 이전에 멀티캐스트 세션에 Join해야 하는 문제가 발생한다. 본 논문에서는 이러한 문제를 해결하는 FLUTE을 이용한 멀티캐스트 콘텐츠 전송에서 Late-Join과 Late-Join 시 파일 복구 방안을 제안하였다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.3
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• pp.188-193
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• 2009

본 논문은 조립과 분리시스템이 혼합된 Fork-and-Join 시스템에서의 일정계획문제를 고려하고 있다. 최초 단계에서는 구성품단위로 분리가 발생하고 두 번째 단계에서는 부품생산단계에서 각 부품 또는 구성품이 서로 다른 설비와 경로를 통해 독립적으로 생산된 후 최종 조립단계로 이동하게 되고, 그곳에서 조립공정을 통해 제품으로 완성된다. 본 논문에서는 이러한 Fork-and-Join 시스템에서 최종완료시간(makespan)을 최소화 할 수 있는 발견적 해법을 제안하고 이 해법의 최악오차한계(worst-case error bound)가 2라는 것을 증명한다. 또한, 제안된 문제의 효과적인 3가지 하한값(lower bound)을 제시하고 다양한 수치실험을 통해 제안된 발견적 해법의 결과와 하한값과의 비교를 통해 제안된 해법이 성능이 우수함을 증명한다.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.6
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• pp.582-587
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• 2008

Structural join is one of the most typical techniques for evaluating XML path queries. Recent researches for structural joins focus on techniques of skipping unnecessary elements using the horizontal distribution information of elements that is indexed on a structure like B+ tree. However, those techniques make the structural join complicated and cannot guarantee efficient join processing due to the overhead of an index structure. In this paper, we propose a new structural join technique that exploits the level information of XML elements. Our technique can skip unnecessary elements using level information, which is vertical distribution information of elements. Through the experimental results, we show that our technique can evaluate structural joins efficiently.

• Journal of KIISE:Software and Applications
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• v.26 no.10
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• pp.1178-1178
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• 1999

A spatial join has the property that its execution time exponentially increases in proportion to the number of spatial objects. Recently, there have been many attempts for improving the performance of the spatial join by using parallel processing schemes, In the case of executing parallel spatial join using the parallel machine with shared disk architecture, the disk bottleneck of parallel processing of spatial join worsens in comparison with sequential spatial join. This paper presents the algorithms of task creation and assignment to reduce the disk bottleneck caused by accessing the shared disk at the same time, and to minimize message passing between processors, This paper proposes object caching which is a higher level of abstraction than page caching, and uses it to do creation and assignment of tasks according to temporal and spatial localities for minimizing disk access time. The object caching shows the performance improvement of 50%. The task creation and assignment using localities gives the gain of 30% and 20%. Overall performance evaluation of the proposed algorithms shows 7.2 times speed up than those of sequential execution of spatial joins.

• Journal of KIISE:Databases
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• v.29 no.1
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• pp.72-78
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• 2002

The distance join is a spatial join which finds data pairs in the order of distance between two spatial data sets using R-trees. The distance join stores node pairs in a priority queue, which are retrieved while traversing R-trees in a top-town manner, in the order of distance. This paper first shows that a priority strategy for the tied pairs in the priority queue during distance join processing has much effect on its performance, and then proposes an optimized secondary priority method. The experiments show that the proposed method is always better than the other methods in the performance perspectives.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.5153-5170
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• 2016

The benefit of the scalability and flexibility inherent in cloud computing motivates clients to upload data and computation to public cloud servers. Because data is placed on public clouds, which are very likely to reside outside of the trusted domain of clients, this strategy introduces concerns regarding the security of sensitive client data. Thus, to provide sufficient security for the data stored in the cloud, it is essential to encrypt sensitive data before the data are uploaded onto cloud servers. Although data encryption is considered the most effective solution for protecting sensitive data from unauthorized users, it imposes a significant amount of overhead during the query processing phase, due to the limitations of directly executing operations against encrypted data. Recently, substantial research work that addresses the execution of SQL queries against encrypted data has been conducted. However, there has been little research on top-k join query processing over encrypted data within the cloud computing environments. In this paper, we develop an efficient algorithm that processes a top-k join query against encrypted cloud data. The proposed top-k join processing algorithm is, at an early phase, able to prune unpromising data sets which are guaranteed not to produce top-k highest scores. The experiment results show that the proposed approach provides significant performance gains over the naive solution.

• Journal of KIISE:Databases
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• v.34 no.5
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• pp.420-436
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• 2007

The choice of an effective indexing method is crucial to guarantee the performance of the spatial join operator which is heavily used in geographical information systems. The $R^*$-tree based method is renowned as one of the most representative indexing methods. In this paper, we propose an efficient spatial join technique based on the DOT(Double Transformation) index, and compare it with the spatial Join technique based on the $R^*$-tree index. The DOT index transforms the MBR of an spatial object into a single numeric value using a space filling curve, and builds the $B^+$-tree from a set of numeric values transformed as such. The DOT index is possible to be employed as a primary index for spatial objects. The proposed spatial join technique exploits the regularities in the moving patterns of space filling curves to divide a query region into a set of maximal sub-regions within which space filling curves traverse without interruption. Such division reduces the number of spatial transformations required to perform the spatial join and thus improves the performance of join processing. The experiments with the data sets of various distributions and sizes revealed that the proposed join technique is up to three times faster than the spatial join method based on the $R^*$-tree index.

• Journal of KIISE:Databases
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• v.35 no.1
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• pp.54-66
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• 2008

A spatio-temporal join is an expensive operation that is commonly used in spatio-temporal database systems. In order to generate an efficient query plan for the queries involving spatio-temporal join operations, it is crucial to estimate accurate selectivity for the join operations. Given two dataset $S_1,\;S_2$ of discrete data and a timestamp $t_q$, a spatio-temporal join retrieves all pairs of objects that are intersected each other at $t_q$. The selectivity of the join operation equals the number of retrieved pairs divided by the cardinality of the Cartesian product $S_1{\times}S_2$. In this paper, we propose aspatio-temporal histogram to estimate selectivity of spatio-temporal join by extending existing geometric histogram. By using a wide spectrum of both uniform dataset and skewed dataset, it is shown that our proposed method, called Spatio-Temporal Histogram, can accurately estimate the selectivity of spatio-temporal join. Our contributions can be summarized as follows: First, the selectivity estimation of spatio-temporal join for discrete data has been first attempted. Second, we propose an efficient maintenance method that reconstructs histograms using compression of spatial statistical information during the lifespan of discrete data.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.5
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• pp.365-369
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• 2009

Overlapping windows are generally used for queries to process continuous data streams. Nevertheless, existing approaches discussed join algorithms only for basic types of windows such as tumbling windows and tuple-driven windows. In this paper, we propose an efficient join algorithm for overlapping windows, which are considered as a more general type of windows. The proposed algorithm is based on an incremental window join. It focuses on producing join results continuously when the memory overflow frequently occurs. It consists of (1) a method to use both of the incremental and full joins selectively, (2) a victim selection algorithm to minimize latency of join processing and (3) an idle time professing algorithm. We show through our experiments that the selective use of incremental and full joins provides better performance than using one of them only.

• The KIPS Transactions:PartD
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• v.15D no.5
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• pp.669-680
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• 2008

There are various studies about AOP(Aspect-Oriented Programming) which modularizes cross-cutting concerns like logging and security effectively. But, we need to utilize techniques which specify the information of aspects modularizing cross-cutting concerns and detailed join information between aspects and targets. We propose a specification technique for aspects which focuses on clarifying the join information between aspects and targets. The technique includes the activities of specifying aspects, defining priority, specifying join information, and specifying pointcuts. We describe the join matrix which represents relationships of aspects and targets and the pointcut specification which is made by analyzing the commonality of join points. The proposed specification technique supports detailed information of the aspects and the join information between aspects and targets so that we can use detailed information to implement aspects.