• 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.

• Proceedings of the KSRS Conference
• /
• 2004.10a
• /
• pp.287-290
• /
• 2004

In the moving object database system, spatiotemporal join is very import operation when we process join moving objects. Processing time of spatio-temporal join operation 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 propose spatio-temporal join algorithm with TB-Tree that preserves trajectories of moving objects, and show result of test. We first present basic algorithm, and propose cpu-time tunning algorithm and IO-time tunning algorithm. We show result of test with data set created by moving object generator tool.

• 제어로봇시스템학회:학술대회논문집
• /
• 1989.10a
• /
• pp.1016-1021
• /
• 1989

In this paper, we introduce a recursively-defined natural join operation as well as well-known object composition operations (union, intersection) for composing CAD database objects. Then, we will discuss how to realize these operations by the message passing computing mechanism. Next, we will discuss what kind of behaviours (methods) are preserved under our natural join operations. Finally, we investigate mathematical properties about the relationships among several object composition operations (natural join, union and intersection).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.25 no.4
• /
• pp.289-297
• /
• 2007

Normaly, the map generlization methode has been used for the making of small scale map using a large scale map. The object join methode is consumed of a lots of processing time and the manual process. The object Join technique used in the NGI based on the digital map ver. 1.0 have problems of poor Joining rate and a lots of processing time. This study has improved the object Join technique considering of the geometry and attribute information for the digital map ver. 2.0. Using improved technique increased joining rate of object and reduced the processing time.

• 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:Software and Applications
• /
• v.26 no.10
• /
• pp.1178-1178
• /
• 1999

A spatial join has the property that its execution time exponentially increases in proportion to the number of spatial objects. Recently, there have been many attempts for improving the performance of the spatial join by using parallel processing schemes, In the case of executing parallel spatial join using the parallel machine with shared disk architecture, the disk bottleneck of parallel processing of spatial join worsens in comparison with sequential spatial join. This paper presents the algorithms of task creation and assignment to reduce the disk bottleneck caused by accessing the shared disk at the same time, and to minimize message passing between processors, This paper proposes object caching which is a higher level of abstraction than page caching, and uses it to do creation and assignment of tasks according to temporal and spatial localities for minimizing disk access time. The object caching shows the performance improvement of 50%. The task creation and assignment using localities gives the gain of 30% and 20%. Overall performance evaluation of the proposed algorithms shows 7.2 times speed up than those of sequential execution of spatial joins.

• Journal of information and communication convergence engineering
• /
• v.16 no.2
• /
• pp.99-105
• /
• 2018

k-Nearest neighbor join (k-NN Join) is a computationally intensive algorithm that is designed to find k-nearest neighbors from a dataset S for every object in another dataset R. Most related studies on k-NN Join are based on single-computer operations. As the data dimensions and data volume increase, running the k-NN Join algorithm on a single computer cannot generate results quickly. To solve this scalability problem, we introduce the locality-sensitive hashing (LSH) k-NN Join algorithm implemented in Spark, an approach for high-dimensional big data. LSH is used to map similar data onto the same bucket, which can reduce the data search scope. In order to achieve parallel implementation of the algorithm on multiple computers, the Spark framework is used to accelerate the computation of distances between objects in a cluster. Results show that our proposed approach is fast and accurate for high-dimensional and big data.

• 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.27 no.2
• /
• pp.256-268
• /
• 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.

• Journal of KIISE:Databases
• /
• v.34 no.5
• /
• pp.420-436
• /
• 2007

The choice of an effective indexing method is crucial to guarantee the performance of the spatial join operator which is heavily used in geographical information systems. The $R^*$-tree based method is renowned as one of the most representative indexing methods. In this paper, we propose an efficient spatial join technique based on the DOT(Double Transformation) index, and compare it with the spatial Join technique based on the $R^*$-tree index. The DOT index transforms the MBR of an spatial object into a single numeric value using a space filling curve, and builds the $B^+$-tree from a set of numeric values transformed as such. The DOT index is possible to be employed as a primary index for spatial objects. The proposed spatial join technique exploits the regularities in the moving patterns of space filling curves to divide a query region into a set of maximal sub-regions within which space filling curves traverse without interruption. Such division reduces the number of spatial transformations required to perform the spatial join and thus improves the performance of join processing. The experiments with the data sets of various distributions and sizes revealed that the proposed join technique is up to three times faster than the spatial join method based on the $R^*$-tree index.