• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2006.05a
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• pp.78-83
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• 2006

선택율 추정은 질의 최적화를 위한 기법중의 하나이다. 이동객체에 대한 기존 선택율 추정 기법은 시간에 따른 빈번한 이동객체의 위치 변화를 요약 정보에 반영하지 못함으로써 선택 율 추정시 많은 에러를 발생시키고 있다. 따라서 이 논문에서는 이동객체의 질의에 대한 선택율 추정을 위한 색인 기반의 히스토그램 기법을 제안하였다. 또한 제안된 기법의 구현과 평가를 통 해 제안된 기법의 성능을 분석하였다. 이 논문에서 제안된 기법은 차량 추적 시스템, 위치 기반 서비스, 응급 구조 서비스, 그리고 텔레매틱스 서비스 통과 같은 연속적으로 위치를 변경하는 이동객체의 정보를 실시간으로 관리하고 검색하는 응용분야에 활용 가능할 것이다.

• Journal of KIISE:Databases
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• v.32 no.5
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• pp.547-557
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• 2005

The purpose of selectivity estimation is to maintain the summary data in a very small memory space and to minimize the error of estimated value and query result. In case of estimating selectivity for large spatial data, the existing works need summary information which reflect spatial data distribution well to get the exact result for query. In order to get such summary information, they require a much memory space. Therefore In this paper, we propose a new technique cumulative density wavelet Histogram, called CDW Histogram, which gets a high accurate selectivity in small memory space. The proposed method is to utilize the sub-histograms created by CD histogram. The each sub-histograms are used to generate the wavelet summary information by applying the wavelet transform. This fact gives us good selectivity even if the memory sire is very small. The experimental results show that the proposed method simultaneously takes full advantage of their strong points - gets a good selectivity using the previous histogram in ($25\%\~50\%$) memory space and is superior to the existing selectivity estimation techniques. The proposed technique can be used to accurately quantify the selectivity of the spatial range query in databases which have very restrictive memory.

• The KIPS Transactions:PartD
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• v.11D no.2
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• pp.281-292
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• 2004

Selectivity estimation for spatial query is very important process used in finding the most efficient execution plan. Many works have been performed to estimate accurate selectivity. Although they deal with some problems such as false-count, multi-count, they can not get such effects in little memory space. Therefore, we propose a new technique called MW Histogram which is able to compress summary data and get reasonable results and has a flexible structure to react dynamic update. Our method is based on two techniques : (a) MinSkew partitioning algorithm which deal with skewed spatial datasets efficiently (b) Wavelet transformation which compression effect is proven. The experimental results showed that the MW Histogram which the buckets and wavelet coefficients ratio is 0.3 is lower relative error than MinSkew Histogram about 5%-20% queries, demonstrates that MW histogram gets a good selectivity in little memory.

• The KIPS Transactions:PartD
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• v.10D no.6
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• pp.927-934
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• 2003

Database query optimates the selectivety of a query to find the most efficient access plan. Multi-dimensional selectivity estimation technique is required for a query with multiple attributes because the attributes are not independent each other. Histogram is practically used in most commercial database products because it approximates data distributions with small overhead and small error rates. However, histogram is inadequate for a query with multiple attributes because it incurs high storage overhead and high error rates. In this paper, we propose a novel method for multi-dimentional selectivity estimation. Compressed information from a large number of small-sized histogram buckets is maintained using the discrete cosine transform. This enables low error rates and low storage overheads even in high dimensions. Extensive experimental results show adventages of the proposed approach.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10b
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• pp.188-190
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• 1998

데이터 베이스 관리 시스템에서는 질의 결과의 크기(selectivity)를 미리 예측하는 것이 필요하다. 질의 결과의 크기는 데이터의 분포 상태에 의해서 결정된다. 이러한 데이터의 분포 상태를 정확하게 예측하는 것이 매우 중요하다. 대부분의 데이터 베이스 관리 시스템에서는 이를 위하여 주기적으로 저장하고 있는 레코드에 대해서 히스토그램을 만들고 이용한다. 이 방법은 히스토그램의 저장공간이 적게 필요로 하고 선택도를 추정하는데 있어서 선택도 추정시 부가적인 계산이 필요하지 않은 장점이 있지만, 일정한 크기의 버켓내에서는 데이터들이 균일하게 분포한다는 가정을 함으로써 선택도 추정에 있어서 에러율이 높았다. 이에 본 논문에서는 커널 방법을 사용하여 버켓 내 데이터의 분포에 대하여 추정 함으로써 이를 해결하는 방법을 제시하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.34-36
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• 2005

선택도 추정 기법은 상용 데이터베이스에서 질의 최적화를 위해 많이 사용하고 있다. 그 중 선택도 추정 기법에 가장 많이 사용되고 있는 기법은 히스토그램이다. 최근 시공간 데이터베이스 관련 연구에서 시간$\cdot$공간 데이터베이스의 선택도 추정 기법이 활발하게 이루어지고 있다. 이 히스토그램 추정 기법이 과거에서 현재시점까지 범위 질의 수행을 성공적으로 이루어지고 있지만 대량의 데이터들을 효율적으로 관리하기에는 저장오버헤드가 너무 크다. 본 논문에서는 시공간데이터베이스에서 성공적으로 선택도 추정을 다룬 히스토그램 추정 기법을 보완하여 과거 이력데이터들의 저장을 효율적으로 할 수 있는 압축기법을 제안한다. 현재 객체에 대해서는 기존 연구에서 성공적으로 이루어진 히스토그램 기반 추정 기법을 응용하고 과거 이력데이터에 대해서는 압축기법인 웨이블렛을 응용하여 선택도추정의 오류율과 저장오버헤드의 향상이 기대된다.

• The Korean Journal of Applied Statistics
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• v.14 no.2
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• pp.429-437
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• 2001

단순 이단계 표본 추출의 경우에 최적 선택률은 Hansen과 Hurwitz(1949)에 의하여 구하여졌다. 그러나 통계청에서 실시하는 표본조사등은 층화 이단계 추출을 한다. 따라서 실제적인 필요성에 의하여 층화 2단계 표본 설계를 시도 하였다. 층화 이단계 표본추출시에 주어진 비용아래서 모총계의 추정량의 분산을 최소로 하는 최적의 선택확률(optimum selection probability), 표본추출율과 부차 표본추출율을 Lagrangean 승수법에 의하여 구한다.

• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.87-96
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• 2006

Selectivity estimation is one of the query optimization techniques. It is difficult for the previous selectivity estimation techniques for moving objects to apply the position change of moving objects to synopsis. Therefore, they result in much error when estimating selectivity for queries, because they are based on the extended spatial synopsis which does not consider the property of the moving objects. In order to reduce the estimation error, the existing techniques should often rebuild the synopsis. Consequently problem occurs, that is, the whole database should be read frequently. In this paper, we proposed a moving object histogram method based on quad tree to develop a selectivity estimation technique for moving object queries. We then analyzed the performance of the proposed method through the implementation and evaluation of the proposed method. Our method can be used in various location management systems such as vehicle location tracking systems, location based services, telematics services, emergency rescue service, etc in which the location information of moving objects changes over time.

• The KIPS Transactions:PartD
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• v.11D no.4
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• pp.983-990
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• 2004

Multiple-count problem is occurred when rectangle objects span across several buckets. The CD histogram is a technique which selves this problem by keeping four sub-histograms corresponding to the four points of rectangle. Although It provides exact results with constant response time, there is still a considerable issue. Since it is based on a query window which aligns with a given grid, a number of errors nay be occurred when it is applied to real applications. In this paper, we propose selectivity estimation techniques using the generalized cumulative density histogram based on two probabilistic models : \circled1 probabilistic model which considers the query window area ratio, \circled2 probabilistic model which considers intersection area between a given grid and objects. Our method has the capability of eliminating an impact of the restriction on query window which the existing cumulative density histogram has. We experimented with real datasets to evaluate the proposed methods. Experimental results show that the proposed technique is superior to the existing selectivity estimation techniques. Furthermore, selectivity estimation technique based on probabilistic model considering the intersection area is very accurate(less than 5% errors) at 20% query window. The proposed techniques can be used to accurately quantify the selectivity of the spatial range query on rectangle objects.

• 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.