• The Journal of Society for e-Business Studies
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• v.14 no.1
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• pp.143-160
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• 2009

In determining the performance of OLAP database applications, the structure and the effective access methods to the underlying disk system is a significant factor. In recent years, hard disks are designed with multiple physical zones where seek times and data transfer rates vary across the zones. However, there is little consideration of multi-zone disks in previous works. Instead, they assumed a traditional disk model that comes with many simplifying assumptions such as an average seek-time and a single data transfer rate. In this paper, we propose a technique storing a set of materialized views into the multi-zoned disks in OLAP environment dealing with large sets of data. We first present the disk zoning algorithm of materialized views according to the access probabilities of each views. Also, we address the problem of storing views in the dynamic environment where data are updated continuously. Finally, through experiments, we prove the performance improvement of the proposed algorithm against the conventional methods.

• Journal of the Korea Society of Computer and Information
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• v.4 no.4
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• pp.69-76
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• 1999

Recently, it has been studied the methods to materialize and precompute the query results for complexed spatial aggregation queries with high response time and the popular use in spatial data warehouse. In this paper, we propose extended selective materialization algorithm and present the way to materialize selectively which is considered access frequency and computation time of spatial operation according to spatial measures of spatial views for improvement of existing selective materialization algorithms.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.408-419
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• 2003

Data warehouses are usually dedicated to the processing of quires issued by decision support system(DSS). The response time of DSS queries is typically several orders of magnitude higher than the one of OLTP queries. Since DSS queries are often submitted interactively, techniques for reducing their response time are important. The caching of query results is one such technique particularly well suited to the DSS environment. In this paper, we present a cache manager for such an environment. Specifically, we define a canonical form of query. The cache manager looks up a query based on the exact query match or using a suggested query split process if the query is found is non-canonical form or in canonical form, respectively. It dynamically maintains the cache content by employing a profit function which reflects in an integrated manner the query execution cost, the size of query result, the reference rate, the maintenance cost of each result due to updates of their base tables, and the frequency of such updates. We performed the experimental evaluation and it positively shows the performance benefit of our cache manager.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.46-48
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• 2003

최근 시공간 데이타에 대한 OLAP연산 효율을 증가시키기 위한 여러 가지 연구들이 행하여지고 있다. 이들 연구의 대부분은 다중트리구조에 기반하고 있다. 다중트리구조는 공간차원을 색인하기 위한 하나의 R-tree와 시간차원을 색인하기 위한 다수의 B-tree로 이루어져 있다. 하지만, 이러한 다중트리구조는 높은 유지비용과 불충분한 질의 처리 효율로 인해 현실적으로 시공간 OLAP연산에 적용하기에는 어려운 점이 있다. 본 논문에서는 이러한 문제를 근본적으로 개선하기 위한 접근 방법으로서 힐버트큐브(Hilbert Cube, H-Cube)를 제안하고 있다. H-Cube는 집계질의(aggregation query) 처리 효율을 높이기 위해 힐버트 곡선을 이용하여 셀들에게 완전순서(total-order)를 부여하고 있으며, 아울러 전통적인 누적합(prefix-sum) 기법을 함께 적용하고 있다. H-Cube는 적응적이며, 완전순서화되어 있으며, 또한 누적합을 이용한 셀 기반의 색인구조이다. 본 논문에서는 H-Cube의 성능 평가를 위해서 다양한 실험을 하였으며, 그 결과로서 유지비용과 질의 처리 효율성면 모두에서 다중트리구조보다 높은 성능 향상이 있음을 보인다.

• Journal of KIISE:Software and Applications
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• v.30 no.9
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• pp.821-828
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• 2003

The virtual angioscopy was implemented using MR angiography image of carotid artery Inside of the carotid artery is one of the body region not accessible by real optical endoscopy but can be visualized with virtual endoscopy. In order to determine the navigation path, we segmented the common carotid artery and internal carotid artery from the MR angiography image. We used the coordinates as a navigation path for virtual camera that were calculated from medial axis transformation. We used the perspective projection and marching cube algorithm to render the surface from volumetric MRA image data. A stroke occurs when brain cells die because of decreased blood flow to the brain. The carotid artery is the primary blood vessel that supplies the blood flow to the brain. Therefore, the carotid artery stenosis is the primary reason of stroke. The virtual angioscopy is highly recommended as a diagnosis tool with which the specific Place of stenosis can be identified and the degree of stenosis can be measured qualitatively. Also, the virtual angioscopy can be used as an education and training tool for endoscopist and radiologist.

• Journal of KIISE:Databases
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• v.29 no.4
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• pp.247-261
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• 2002

Aggregation is an important operation that affects the performance of OLAP systems. In this paper we define a new class of aggregation queries, called range-groupby queries, and present a method for processing them. A range-groupby query is defined as a query that, for an arbitrarily specified region of an n-dimensional cube, computes aggregations for each combination of values of the grouping attributes. Range-groupby queries are used very frequently in analyzing information in MOLAP since they allow us to summarize various trends in an arbitrarily specified subregion of the domain space. In MOLAP applications, in order to improve the performance of query processing, a method of maintaining precomputed aggregation results, called the prefix-sum array, is widely used. For the case of range-groupby queries, however, maintaining precomputed aggregation results for each combination of the grouping attributes incurs enormous storage overhead. Here, we propose a fast algorithm that can compute range-groupby queries with minimal storage overhead. Our algorithm maintains only one prefix-sum away and still effectively processes range-groupby queries for all possible combinations of the grouping attributes. Compared with the method that maintains a prefix-sum array for each combination of the grouping attributes in an n-dimensional cube, our algorithm reduces the space overhead by (equation omitted), while accessing a similar number of cells.

• Journal of KIISE:Software and Applications
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• v.28 no.10
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• pp.757-763
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• 2001

This paper propose a new clustering algorithm called G-CLUS based on the relative gravitation. In this method every instance has the same mass at first. the gravitations among instances make each instance move to the attractive direction gradually and eventually natural clusters are formed without the initial seed and the number of clusters. Our proposed method can determine the number of clusters via a process of gravitational agglomeration and it can reduce the sensitivity to outliers by using the resultant of gravitation. We also improved the computational complexity by applying the concept of a cube to the proposed algorithm. In our experiments, we show the behavior of instance movement clustering process for each model, clustering process and the results for an example data set, and the results of comparison between the other clustering algorithm and our proposed. method.