• Journal of KIISE:Databases
• /
• v.30 no.5
• /
• pp.438-450
• /
• 2003

Spatial joins find pairs of objects that overlap with each other. In spatial joins using indexes, original-space indexes such as the R-tree are widely used. An original-space index is the one that indexes objects as represented in the original space. Since original-space indexes deal with sizes of objects, it is difficult to develop a formal algorithm without relying on heuristics. On the other hand, transform-space indexes, which transform objects in the original space into points in the transform space and index them, deal only with points but no sites. Thus, spatial join algorithms using these indexes are relatively simple and can be formally developed. However, the disadvantage of transform-space join algorithms is that they cannot be applied to original-space indexes such as the R-tree containing original-space objects. In this paper, we present a novel mechanism for achieving the best of these two types of algorithms. Specifically, we propose a new notion of the transform-space view and present the transform-space view join algorithm(TSVJ). A transform-space view is a virtual transform-space index based on an original-space index. It allows us to interpret on-the-fly a pre-built original-space index as a transform-space index without incurring any overhead and without actually modifying the structure of the original-space index or changing object representation. The experimental result shows that, compared to existing spatial join algorithms that use R-trees in the original space, the TSVJ improves the number of disk accesses by up to 43.1% The most important contribution of this paper is to show that we can use original-space indexes, such as the R-tree, in the transform space by interpreting them through the notion of the transform-space view. We believe that this new notion provides a framework for developing various new spatial query processing algorithms in the transform space.

• Journal of KIISE:Databases
• /
• v.27 no.2
• /
• pp.247-255
• /
• 2000

Traditional object decomposition techniques do not decompose spatial objects dynamically during spatial joins, because the object decomposition is very expensive. In this paper, we propose a modified object decomposition technique that can be applied in PBSM(Partition Based Spatial Merge-Join). In real-life data, there are much differences among the sizes of objects. We decompose only large objects with great effects on spatial joins. This technique decreases the decomposition cost of objects during spatial joins and enables efficient filter-refinement steps. Experiments show that the PBSM used with our proposed method performs significantly better than the traditional PBSM.

• Journal of KIISE:Databases
• /
• v.29 no.1
• /
• pp.72-78
• /
• 2002

The distance join is a spatial join which finds data pairs in the order of distance between two spatial data sets using R-trees. The distance join stores node pairs in a priority queue, which are retrieved while traversing R-trees in a top-town manner, in the order of distance. This paper first shows that a priority strategy for the tied pairs in the priority queue during distance join processing has much effect on its performance, and then proposes an optimized secondary priority method. The experiments show that the proposed method is always better than the other methods in the performance perspectives.

• The KIPS Transactions:PartD
• /
• v.8D no.4
• /
• pp.347-360
• /
• 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 KIISE:Databases
• /
• v.27 no.2
• /
• pp.238-246
• /
• 2000

The distance join has been introduced previously, which finds nearest pairs in the order of distance incrementally among two spatial data sets built with multidimensional indexes like R-trees. We propose efficient K-distance joins when the number(K) of pairs to find is preset. Especially, we develop a distance join algorithm with bi-directional expansion and optimized plane sweeping using selection method of sweep axis and direction. The experiments on real spatial data sets show that the proposed algorithm is much better than the former algorithms.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.309-312
• /
• 2005

The need for LBS (Loc,ation Based Services) is increasing due to the wnespread of mobile computing devices and positioning technologies~ In LBS, there are many applications that need to manage moving objects (e.g. taxies, persons). The moving object join operation is to make pairs with spatio-temporal attribute for two sets in the moving object database system. It is import and complicated operation. And processing time increases by geometric progression with numbers of moving objects. Therefore efficient methods of spatio-temporal join is essential to moving object database system. In this paper, we apply spatial join methods to moving objects join. We propose two kind of join methods with TB- Tree that preserves trajectories of moving objects. One is depth first traversal spatio-temporaljoin and another is breadth-first traversal spatio-temporal join. We show results of performance test with sample data sets which are created by moving object ,generator tool.

• Spatial Information Research
• /
• v.18 no.1
• /
• pp.77-87
• /
• 2010

Spatio-temporal join operators are essential to the management of spatio-temporal data such as moving objects. For example, the join operators are parts of processing to analyze movement of objects and search similar patterns of moving objects. Various studies on spatio-temporal join queries in outdoor space have been done. Recently with advance of indoor positioning techniques, location based services are required in indoor space as well as outdoor space. Nevertheless there is no one about processing of spatio-temporal join query in indoor space. In this paper, we introduce continuous spatio-temporal self-join queries in indoor space and propose a method of processing of the join queries over stream data of moving objects. The continuous spatio-temporal self-join query is to update the joined result set satisfying spatio-temporal predicates continuously. We assume that positions of moving objects are represented by symbols such as a room or corridor. This paper proposes a data structure, called Candidate Pairs Buffer, to filter and maintain massive stream data efficiently and we also investigate performance of proposed method in experimental study.

• Journal of Korea Spatial Information System Society
• /
• v.6 no.1 s.11
• /
• pp.3-17
• /
• 2004

With the rapid growth of the Internet geographic information services through the WWW such as a location-based service and so on. Web GISs (Geographic Information Systems) have also come to be a cluster-based architecture like most other information systems. That is, in order to guarntee high quality of geographic information service without regard to the rapid growth of the number of users, web GISs need cluster-based architecture that will be cost-effective and have high availability and scalability. This paper proposes the design of the cluster-based web GIS with high availability and scalability. For this, each node within a cluster-based web GIS consists of main memory spatial databases which accomplish role of caching by using data declustering and the locality of spatial query. Not only simple region queries but also the proposed system processed spatial join queries effectively. Compare to the existing method. Parallel R-tree spatial join for a shared-Nothing architecture, the result of simulation experiments represents that the proposed spatial join method achieves improvement of performance respectively 23% and 30% as data quantity and nodes of cluster become large.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.7
• /
• pp.3578-3593
• /
• 2017

Given two collections of objects that carry both spatial and textual information in the form of tags, a $\text\underline{S}patio$-$\text\underline{T}extual$-based object $\text\underline{S}imilarity$ $\text\underline{JOIN}$ (ST-SJOIN) retrieves the pairs of objects that are textually similar and spatially close. In this paper, we have proposed a block index-based approach called BIST-JOIN to facilitate the efficient ST-SJOIN processing. In this approach, a dual-feature distance plane (DFDP) is first partitioned into some blocks based on four segmentation schemes, and the ST-SJOIN is then transformed into searching the object pairs falling in some affected blocks in the DFDP. Extensive experiments on real and synthetic datasets demonstrate that our proposed join method outperforms the state-of-the-art solutions.

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