• Journal of KIISE:Databases
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• v.34 no.1
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• pp.84-97
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• 2007

Selectivity estimation techniques in query optimization have been used in commercial databases and histograms are popularly used for the selectivity estimation. Recently, the techniques for spatio-temporal databases have been restricted to existing temporal and spatial databases. In addition, the selectivity estimation techniques focused on time-series data such as moving objects. It is also impossible to estimate selectivity for range queries with a time interval. Therefore, we construct two histograms, CMH (current multidimensional histogram) and PMH (past multidimensional histogram), to estimate the selectivity of multidimensional sequence data in spatio-temporal databases and propose effective selectivity estimation methods using the histograms. Furthermore, we solve a problem about the range query using our proposed histograms. We evaluated the effectiveness of histograms for range queries with a time interval through various experimental results.

• Journal of KIISE:Databases
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• v.30 no.5
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• pp.464-475
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• 2003

few sample objects and compressed histogram information of image databases. The histogram information is used to estimate the selectivity of spherical range queries and a small number of sample objects is used to compensate the selectivity error due to the difference of the similarity measures between meta server and local image databases. An extensive experiment on a large number of image data demonstrates that our proposed method performs well in the distributed heterogeneous environment.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.214-223
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• 2006

Recently there is a need to store and process enormous spatial data in spatio-temporal databases. For effective query processing in spatio-temporal databases, selectivity estimation in query optimization techniques, which approximate query results when the precise answer is not necessary or early feedback is helpful, has been studied. There have been selectivity estimation techniques such as sampling-based techniques, histogram-based techniques, and wavelet-based techniques. However, existing techniques in spatio-temporal databases focused on selectivity estimation for future extent of moving objects. In this paper, we construct a new histogram, named T-Minskew, for query optimization of past spatio-temporal data. We also propose an effective selectivity estimation method using T-Minskew histogram and effective histogram maintenance technique to prevent frequent histogram reconstruction using threshold.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.49-57
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• 1994

진정한 의미의 다운사이징을 구현하기 위해서는 필요한 분산데이타베이스의 구현은 현재 여러가지 기술적인 문제점들을 안고 있다. 따라서 동시성 제어(concurrency control)와 갱신 (update propagation), 복구(recovery), 질의어 처리(query processing), 카달로그 관리(catalog management)등과 같은 분산 환경에서의 데이타베이스에 관련된 기술적인 문제점들을 해결하기 위해서는 어떠한 최적의 방법들을 개발해야만 한다. 이러한 방법들의 개발은 관계형 데이타베이스의 데이타관리 분류체계를 통한 대안들의 선택과 운용에 의해 가능할 것이다. 분산 환경하에서 사용되어질 수 있는 관계형 데이타베이스의 데이타관리 분류체계를 availability, expression, currency의 관점에서 Basic table과 view로 구분하여 정립해 보았다. Basic table은 current update가 필수적이므로 availability와 expression의 관점에서 분류하였고, view는 physical file의 존재성 유무와 시간적 실행의 차이를 기준으로 분류하였다. 그리고 이러한 분류기준에 따른 특성들을 분산데이타베이스 구축에 이용하는 방법들에 대해 이야기 하였다. 다시 말해 non-current materialized view 뿐만 아니라 current materialized view를 동시에 지원하는 하나의 distributed view update architecture를 개발하는 것에 대해 하나의 방법으로 제시하였다. 즉 immediate update와 deferred update는 current view를 이용하고 periodical update는 non-current view를 이용하여 100%의 distributed data resources를 관리 할 수 있는 효율적인 distributed system를 개발하는 것을 제시하였다. 본 논문은 데이타베이스론의 입장에서 아직 정립되어 있지 않은 분산 환경하에서의 관계형 데이타베이스의 데이타관리의 분류체계를 나름대로 정립하였다는데 그 의의가 있다. 또한 이것의 응용은 현재 분산데이타베이스 구축에 있어 나타나는 기술적인 문제점들을 어느정도 보완할 수 있다는 점에서 그 중요성이 있다.

• Journal of KIISE:Databases
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• v.30 no.2
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• pp.176-183
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• 2003

Various techniques including histograms, sampling and parametric techniques have been proposed to estimate query result sizes for the query optimization. Histogram-based techniques are the most widely used form for the selectivity estimation in relational database systems. However, in the spatio-temporal databases for the moving objects, the continual changes of the data distribution suffer the direct utilization of the state of the art histogram techniques. Specifically for the future queries, we need another methodology that considers the updated information and keeps the accuracy of the result. In this paper we propose a novel approach based upon the duality and the marginal distribution to construct a histogram with very little time since the spatio-temporal histogram requires the data distribution defined by query predicates. We use data synopsis method in the dual space to construct spatio-temporal histograms. Our method is robust to changing data distributions during a certain period of time while the objects keep the linear movements. An additional feature of our approach supports the dynamic update incrementally and maintains the accuracy of the estimated result.

• 한국공간정보시스템학회:학술대회논문집
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• 1999.06a
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• pp.231-246
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• 1999

일반적으로 스키마 통합이란 각 응용들의 관점에 따라 다르게 표현된 스키마들을 하나의 스키마로 통합하는 것을 말한다. GIS 데이타베이스는 일반 데이타베이스와는 달리 개체에 대한 속성 정보 뿐만 아니라 기하 등과 같은 공간 정보를 가지고 있다. 특히, GIS 응용에서 정의된 개체들의 공간 표현은 관점에 따라 다양한 기하 서브스키마들로 표현된다. 따라서 GIS 데이타베이스를 위한 스키마 통합에서 서로 다른 기하 서브스키마들을 통합하는 것이 무엇보다 중요하다. 본 논문에서는 GIS 데이타베이스의 스키마통합을 위해서 기하 서브스키마들의 통합 문제를 중점적으로 다룬다 먼저 기하 서브스키마들의 통합에서 발생하는 충돌들을 개체의 기하를 중심으로 동일 개체간의 기하 충돌과 다른 개체간의 기하 충돌로 분류하여 정의한다. 그리고 이 충돌들을 해결하기 위한 충돌 해결 방법으로 각 충돌들의 특성을 고려하여 상위 레벨의 기하 서브스키마 선택, 기하의 다중 표현, 구성 요소의 공유, 개체들 간의 상세화 등을 제시한다. 또한, 통합된 스키마에 대하여 가상 스키마를 표현하는 방법을 제시하여, 통합되기 전의 스키마들을 통합된 스키마로부터 유도될 수 있음을 보인다. 본 논문에서는 GIS 데이타베이스 스키마를 기술하기 위하여 ISO/TC211의 개념적 스키마 언어인 EXPRESS-G를 이용한다.

• Convergence Security Journal
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• v.3 no.3
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• pp.7-18
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• 2003

Under database system environment, to accomplish database security is to ascertain the security threats and to choose the policy and mechanism of treating them. This study suggests the type and character of security threat and general method of prevention and cutting off the threats. Also, this study suggests the method of realization of Oracle security and additionally shows the method of Oracle security framework implemention. As an example, the verification method of Oracle security implementions are shown.

• Journal of the Korean Society for information Management
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• v.10 no.2
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• pp.111.1-111.1
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• 1993

There are two types of bibliographic database, the one IS bibliographic database with creativlty. theother is bibliographic database without creativlty, The purpose of this paper is to investigate the problemsof bibliographic database with creatlvity by giving consideration of copyright law and the problems ofbihographic database w~thout creat~vity by sui genem system. Thus thls paper provided the methods thatresolve the problems of the current copyright law in order to protect all types of database being producedby way of the cooperation between the libraries and the information service centers. Secondly, this paperprovlded the legal devlces to protect bibliographic database produced by the libraries wh~ch cannot beprotected due to the lack of the creatlvity in the selection or arrangement of the material.

• Journal of the Korean Society for information Management
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• v.10 no.2
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• pp.111-143
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• 1993

There are two types of bibliographic database, the one IS bibliographic database with creativlty. the other is bibliographic database without creativlty, The purpose of this paper is to investigate the problems of bibliographic database with creatlvity by giving consideration of copyright law and the problems of bihographic database w~thout creat~vity by sui genem system. Thus thls paper provided the methods that resolve the problems of the current copyright law in order to protect all types of database being produced by way of the cooperation between the libraries and the information service centers. Secondly, this paper provlded the legal devlces to protect bibliographic database produced by the libraries wh~ch cannot be protected due to the lack of the creatlvity in the selection or arrangement of the material.

• 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.