• Journal of KIISE:Databases
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• v.27 no.2
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• pp.256-268
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• 2000

Multi-way spatial join is a nested expression of two or more spatial joins. It costs much to process multi-way spatial join, but there have not still reported the scheme of parallel processing of multi-way spatial join. In this paper, parallel processing of multi-way spatial join consists of parallel multi-way spatial filter and parallel spatial refinement. Parallel spatial refinement is executed by the following two steps. The first is the generation of a graph used for reducing duplication of both spatial objects and spatial operations from pairs candidate object table that are the results of multi-way spatial filter. The second is the parallel spatial refinement using that graph. Refinement using the graph is proved to be more efficient than the others. In task creation for parallel refinement, minimum duplication partitioning of the Spatial_Obicct_On_Node graph shows best performance.

• The KIPS Transactions:PartD
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• v.12D no.6 s.102
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• pp.837-848
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• 2005

A multi-scale database is a set of spatial database, covering same geographic area with different scales and it can be derived from pre-existing databases. In the derivation processes of a new multi-scale spatial database, the geometries and topological relations on the source database can be transformed and the transformation can be the cause of the lack of integrity Therefore, it is necessary to assess the transformation whether it is consistent or not after the derivation process of a new multi-scale database. Thus, we propose assessment methods for the topological consistency between a source database and a derived multi-scale database in this paper. In particular, we focus on the case that 2-dimensional objects are collapsed to 1-dimensional ones in the derivation process of a multi-scale database. We also describe implementation of the assessment methods and show the results of the implementation with experimental data.

• Journal of Preventive Medicine and Public Health
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• v.54 no.5
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• pp.301-308
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• 2021

Objectives: Despite its advantages, it is not yet common practice in Korea for researchers to investigate disease associations using spatio-temporal analyses. In this study, we aimed to review health-related epidemiological research using spatio-temporal analyses and to observe methodological trends. Methods: Health-related studies that applied spatial or spatio-temporal methods were identified using 2 international databases (PubMed and Embase) and 4 Korean academic databases (KoreaMed, NDSL, DBpia, and RISS). Two reviewers extracted data to review the included studies. A search for relevant keywords yielded 5919 studies. Results: Of the studies that were initially found, 150 were ultimately included based on the eligibility criteria. In terms of the research topic, 5 categories with 11 subcategories were identified: chronic diseases (n=31, 20.7%), infectious diseases (n=27, 18.0%), health-related topics (including service utilization, equity, and behavior) (n=47, 31.3%), mental health (n=15, 10.0%), and cancer (n=7, 4.7%). Compared to the period between 2000 and 2010, more studies published between 2011 and 2020 were found to use 2 or more spatial analysis techniques (35.6% of included studies), and the number of studies on mapping increased 6-fold. Conclusions: Further spatio-temporal analysis-related studies with point data are needed to provide insights and evidence to support policy decision-making for the prevention and control of infectious and chronic diseases using advances in spatial techniques.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.4
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• pp.45-56
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• 2010

This paper suggests constraint data modeling based on constraint data presentation techniques to perform complex spatial database operation naturally. We were able to identify the limitation of extendibility of dimension and non-equal framework via relevant research for former schema of spatial database and query processing. Therefore we described generalized tuple of spatial data and the definition of suggested constraint data modeling. Also we selected MLPQ/PReSTO tool among constraint database prototype and compare standard functionality of ARC/VIEW. Then we design scenario for spatial operation using MLPQ/PReSTO and we suggested application effect after query processing. Based on above explanation, we were able to identify that we can process spatial data naturally and effectively using simple constraint routine on same framework via constraint data modeling.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.593-596
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• 2002

일반적으로 공간질의 처리는 복잡한 대용량 공간데이타를 대상으로 수행하기 때문에 많은 비용과 시간이 필요하다. 특히, 클라이언트/서버 공간 데이터베이스 환경에서는 클라이언트가 자주 이용하는 질의를 반복하여 처리하는 경우에 서버의 부하가 증가되며 질의 응답시간도 길어지게 된다. 따라서 클라이언트/서버 공간 데이타베이스에서 클라이언트측의 공간질의를 효율적으로 처리하기 위해서는 데이터 캐싱 등의 기법이 필요하다. 이를 위하여 본 논문에서는 클라이언트에서 자주 이용되는 공간질의를 뷰로 정의하고 클라이언트에서 실체화하는 방법을 이용하여 데이터 캐싱을 처리한다. 그리고 클라이언트에 실체화된 뷰를 최대한으로 이용하기 위하여 질의 수정 기법을 적용한 공간질의 처리 방법을 제시한다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.06a
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• pp.434-436
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• 2008

1차부터 3차에 이르는 국가지리정보체계 사업을 추진해 오는 동안 가장 큰 결과물 중의 하나가 정부 부처들이 각기 구축한 산림, 지적, 토지이용 등과 같은 공간정보 데이터베이스들이다. 이들 데이터베이스의 공유를 통해 자원효율성을 높이기 위해서는 연계 통합 방안이 필요하다. 본 연구는 데이터베이스의 물리적 통합(강결합 방식)이 아니라, 약결합방식에 의한 데이터베이스 연계시스템을 통해 통합서비스를 제공하는 시스템을 소개한다. 이 시스템은 데이터베이스 연계 중요성이 증가하는 시점에서 다양한 요구에 대응하는 데이터를 생성 관리함으로서 정보의 재사용성을 증가시키고, 정보의 최신성을 유지시킨다는 점에서 의의가 있다.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.150-152
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• 1999

본 연구에서는 원격탐사 데이터베이스를 구축하기 위한 요구사항들을 분석하고, 원격탐사 자료의 메타 데이터 스키마를 설계하고 이를 구현한다. 특히, 현행 지형 공간 표준화 스펙을 따르는 자료구조가 연구되고, OMEGA(Object Management systEem for Geo-Spatial Applications) 객체지향 데이터베이스 관리시스템을 사용하여 SAIF(Spatial Archive and Interchange Format)를 기반으로 한 스키마 구현에 관해 기술한다. 이 연구는 차세대 데이터베이스 기술(예를 들면, 복잡한 데이터 타입 지원, 병렬처리, 사용자 편의성 인터페이스, 그리고 웹(Web) 데이터베이스 기술)에 대한 사전 기초 연구로써 실험적인 대용량 데이터베이스를 구축하기 위하여 수행되어 졌다.

• 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.

• Spatial Information Research
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• v.6 no.2
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• pp.125-132
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• 1998

The I/O performance for spatial queries is extremely important since the handling of huge amount of multidimensional data is required in spatial databases for geographic information systems. Therefore, we describe representative spatial access methods handling complex spatial objects, z-transform B tree, KDB tree, R tree, MAX tree, to increase I/O performance. In addition, we measure the performance of spatial indexing schemes by testing against various realistic data and query sets. Results from the benchmark test indicates that MAX outperforms other indexing schemes on insertion, range query, spatial join. MAX tree is expected to use as index scheme organizing storage system of GIS in the future.

• The KIPS Transactions:PartD
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• v.12D no.4 s.100
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• pp.543-552
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• 2005

As random access memory chip gets cheaper, it becomes affordable to realize main memory-based database systems. The disk-based spatial indexing techniques, however, cannot direct apply to main memory databases, because the main purpose of disk-based techniques is to reduce the number of disk accesses. In main memory-based indexing techniques, the node access time is much faster than that in disk-based indexing techniques, because all index nodes reside in a main memory. Unlike disk-based index techniques, main memory-based spatial indexing techniques must reduce key comparing time as well as node access time. In this paper, we propose an efficient spatial index structure for main memory-based databases, called Tmr-tree. Tmr-tree integrates the characteristics of R-tree and T-tree. Therefore, Nodes of Tmr-tree consist of several entries for data objects, main memory pointers to left and right child, and three additional fields. First is a MBR of a self node, which tightly encloses all data MBRs (Minimum Bounding Rectangles) in a current node, and second and third are MBRs of left and right sub-tree, respectively. Because Tmr-tree needs not to visit all leaf nodes, in terms of search time, proposed Tmr-tree outperforms R-tree in our experiments. As node size is increased, search time is drastically decreased followed by a gradual increase. However, in terms of insertion time, the performance of Tmr-tree was slightly lower than R-tree.