• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.06a
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• pp.67-74
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• 2008

현재의 센서 네트워크 시스템은 공간적 정보를 배제한 센서 데이터스트림에 대한 저장 및 검색 방안에 대한 연구에 치중되어 있다. 하지만, 이러한 센서 네트워크가 공간적 정보와 결합하게 되면 훨씬 더 많은 응용과 의미 있는 데이터로 가공될 수 있다. 본 논문은 GeoSensor Network에서 공간적 정보와 데이터스트림이 결합된 공간 데이터스트림 정의 및 공간 데이터스트림간 조인 전략들과 그에 따른 조인 전략들 간의 비용을 추정하는 비용 모델을 제시하였다. 공간 데이터스트림간 조인 전략을 위해 Nested Loop 조인, Grid File, R-tree 알고리즘을 사용하였고, 단방향 Nested Loop 조인, 단방향 Grid 조인, 단방향 R-tree 조인 기법들을 조합하여 조인 전략들 간의 비용을 추정하였다.

• Journal of KIISE:Databases
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• v.30 no.6
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• pp.593-605
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• 2003

Since spatial join processing consumes much time, several algorithms have been proposed to improve spatial join performance. The M-way R-tree join (MRJ) is a join algorithm which synchronously traverses M R-trees in the M-way spatial join. In this paper, we introduce indirect predicates which do not directly come from the multi-way join conditions but are indirectly derived from them. By applying the concept of indirect predicates to MRJ, we improve the performance of MRJ. We call such a multi-way R-tree join algorithm using indirect predicates indirect predicate filtering (IPF). Through experiments using synthetic data and real data, we show that IPF significantly

• Journal of KIISE:Databases
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• v.31 no.4
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• pp.352-361
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• 2004

Spatial joins find all pairs of spatial objects that satisfy a given spatial relationship. In this paper, we propose the transformation-based spatial partition join algorithm (TSPJ), a new spatial join algorithm that performs join in the transform space without using indexes. Since the existing algorithms deal with extents of spatial objects in the original space, they either need to replicate the spatial objects or have a relatively complex partition structure-resulting in degrading performance. In contrast, TSPJ transforms objects in the original space into points in the transform space and deals only with points having no extents. The transformation does not incur any additional overhead. Thus, our algorithm has advantages over existing ones in that it obviates the need for replicating spatial objects, and its partition structure is simple. As a result, it always has better performance compared with existing algorithms. Extensive experiments show that TSPJ improves performance by 20.5∼38.0% over the existing algorithms compared.

• Journal of the Korean housing association
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• v.12 no.4
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• pp.195-203
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• 2001

The purpose of this study is to analyze the characteristics of Mario Botta's residential design. The characteristics of Mario Botta's residential design are following as : 1. The residential design of Mario Botta planed from 1960' to 1970's is based on the style composition method of modern design, specially by the influence of Le Corbusier and Louis I. Kahn. Botta's residential design of the 1980's is generally affected by A. Palladio, and is characterized as the representation of classic standard. The residential design of the 1980's shows the establishment level of Mario Botta's residential design, and produces particular Botta's facade. 2. The characteristics of Mario Botta's spatial composition on residential design could be implicated by the spatial distribution of each floor which is divided by three floors, simple and primitive volume which has a symmetry, opening through the massive elimination n the center of front, double skin, the contrast between void and solid, and the inflow of light through the top-light. 3. Most Botta's residential designs have Loggia in the center of facade working as a buffer space which joins outside and inside space together. They also lead the spatial continuous flow. The inside space is combined by the light coming through top-light and vertical opening. 4. They have high accomplishment in complicating details. They show the outside design of a horizontal belt according to the module of concrete block and the decorated design using compositional materials such as an unevenness piled up askew concrete block .

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.418-429
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• 2001

Recently, a GIS has been applicable to the most important computer applications such as urban information systems and transportation information systems. These applications require spatial operations for an efficient management of a large volume of data. In particular, a spatial join among basic operations has the property that its response time is increased exponentially according to the number of spatial objects included in the operation. Therefore, it is not proper to the systems demanding the fast response time. To satisfy these requirements, the efficient parallel processing of spatial joins has been required. In this paper, the efficient method for creating and allocating tasks to balance statically the load of each processor in a parallel spatial join is presented. A task graph is developed in which a vertex weight is calculated by the cost model I have proposed. Then, it is partitioned through a graph partitioning algorithm. According to the experiments in CC16 parallel machine, our method made an improvement in the static load balance by decreasing the variance of a task execution time on each processor.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.373-383
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• 2001

The distance join is a spatial join which finds data pairs in the order of distance when associating two spatial data sets. This paper proposes several methods to optimize the adaptive multi-stage distance join, presented in [1]. First, we optimize the sweeping index formula which is used for selecting sweeping axis during plane sweeping. Second, to improve the performance of a priority queue used for maintaining node pairs, we propose to use the maximum distance of a node pair as the second priority of the queue. Moreover, we compare trade-offs in estimating the cut-off distance between under uniformity assumption of data distribution and non-uniformity assumption. The experiments show that the proposed methods greatly improve the performance of the algorithm in CPU cost as well as in I/O cost.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.13-15
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• 2001

최근 병렬 시스템을 이용하여 공간 조인의 성능 방안에 연구가 진행되고 있다. 그렇지만 프로세서의 수가 증가할수록 병렬 처리에 의한 프로세서의 효율성은 급격히 떨어진다. 이것은 병렬 공간 조인을 수행할 경우 순차 공간 조인 보다 디스크 병목 현상과 메시지 전송 오버헤드가 심하게 발행하기 때문이다. 이 논문에서는 공유 디스크 구조에서 다중 프로세서의 디스크 동시 접근으로 인한 병목 현상을 환화하고, 메시지 전송을 최소화하기 위한 태스크 할당 기법을 제안하였다. 제안한 태스크 할당 기법을 두 가지 공간 조인 방법에 각각 적용하여 디스크 접근 횟수와 메시지 전송 횟수의 감소 효과를 실험으로 평가하였다. MIMD 구조 및 공유디스크 방식의 병렬 시스템에서의 다양한 실험에서 이 논문에서 제안한 준동적 태스크 할당 기법이 정적 할당과 동적 할당 기법에 비해 우수함을 보였다.

• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.347-360
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• 2001

The most expensive spatial operation in spatial databases is a spatial join which computes a combined table of which tuple consists of two tuples of the two tables satisfying a spatial predicate. Although the execution time of sequential processing of a spatial join has been so far considerably improved, the response time is not tolerable because of not meeting the requirements of interactive users. It is usually appropriate to use parallel processing to improve the performance of spatial join processing. However, as the number of processors increases, the efficiency of each processor decreases rapidly because of the disk bottleneck and the overhead of message passing. This paper proposes the method of task allocation to soften the disk bottleneck caused by accessing the shared disk at the same time, and to minimize message passing among processors. In order to evaluate the performance of the proposed method in terms of the number of disk accesses and message passing, we conduct experiments on the two kinds of parallel spatial join algorithms. The experimental tests on the MIMD parallel machine with shared disks show that the proposed semi-dynamic task allocation method outperforms the static and dynamic task allocation methods.

• Journal of Korea Spatial Information System Society
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• v.9 no.2
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• pp.25-34
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• 2007

Existing approaches that select an order for the join of three or more data streams have always used the simple heuristics. For their disadvantage - only one factor is considered and that is join selectivity or arrival rate, these methods lead to poor performance and inefficiency In some applications. The graph-based sliding window multi -join algorithm with optimal join sequence is proposed in this paper. In this method, sliding window join graph is set up primarily, in which a vertex represents a join operator and an edge indicates the join relationship among sliding windows, also the vertex weight and the edge weight represent the cost of join and the reciprocity of join operators respectively. Then the optimal join order can be found in the graph by using improved MVP algorithm. The final result can be produced by executing the join plan with the nested loop join procedure, The advantages of our algorithm are proved by the performance comparison with existing join algorithms.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.984-987
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• 2018

In an Internet of Thing (IoT) environment, entities with different attributes and capacities are going to be connected in a highly connected fashion. Specifically, not only the mechanical and electronic devices but also other entities such as people, locations and applications are connected to each other. Understanding and managing these connections play an important role for businesses, which identify opportunities for new IoT services. Traditional approach for storing and querying IoT data is used of a relational database management system (RDMS) such as MySQL or MSSQL. However, using RDMS is not flexible and sufficient for handling heterogeneous IoT data because these data have deeply complex relationships which require nested queries and complex joins on multiple tables. In this paper, we propose a graph model for constructing a graph database of heterogeneous IoT data. Graph databases are purposely-built to store highly connected data with nodes representing entities and edges representing the relationships between these entities. Our model fuses social graph, spatial graph, and things graph, and incorporates the relationships among them. We then present a case study which applies our model for representing data from a Smart Campus using Neo4J platform. Through the results of querying to answer real questions in Smart Campus management, we show the viability of our model.