• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.230-232
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• 2011

Owing to the advent of digital devices which equipped with GPS, such as smartphone and tablet pc, a number of LBSNS applications have been released and even SNS applications serve various Location-Based Services. In twitter's case, the news of interesting area is provided to user not by being subscribed them automatically, but by being searched on web-site. This paper describes the system designed for users want to subscribe the local news without procedure like searching using operators. This system uses PBSM(Partition Based Spatial-Merge Join) which has no index for batch processing and against a massive query. The results from Spatial Join are stored in Materialized View then provided to user.

• Journal of KIISE:Databases
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• v.33 no.3
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• pp.310-326
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• 2006

In this paper, we deal with a method of efficiently processing continuous queries in a data stream environment. We classify previous query processing methods into two dual categories - data-initiative and query-initiative - depending on whether query processing is initiated by selecting a data element or a query. This classification stems from the fact that data and queries have been treated asymmetrically. For processing continuous queries, only data-initiative methods have traditionally been employed, and thus, the performance gain that could be obtained by query-initiative methods has been overlooked. To solve this problem, we focus on an observation that data and queries can be treated symmetrically. In this paper, we propose the duality model of data and queries and, based on this model, present a new viewpoint of transforming the continuous query processing problem to a multi-dimensional spatial join problem. We also present a continuous query processing algorithm based on spatial join, named Spatial Join CQ. Spatial Join CQ processes continuous queries by finding the pairs of overlapping regions from a set of data elements and a set of queries defined as regions in the multi-dimensional space. The algorithm achieves the effects of both of the two dual methods by using the spatial join, which is a symmetric operation. Experimental results show that the proposed algorithm outperforms earlier methods by up to 36 times for simple selection continuous queries and by up to 7 times for sliding window join continuous queries.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11c
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• pp.1367-1370
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• 2003

본 논문에서는 인덱스가 존재하지 않는 두 개의 입력 릴레이션에 대해서도 최적의 조인 연산을 수행할 수 있는 공간 해쉬 조인 알고리즘을 제안한다. 인덱스가 존재하지 않는 릴레이션의 처리에 사용하는 기존의 공간 해쉬 조인(SHJ: Spatial Hash Join)과 Scalable Sweeping-Rased Spatial Join(SSSJ) 알고리즘을 결합하여 SHJ 알고리즘의 단점으로 지적되고 있는 편향된(skewed) 데이터에 대한 조인 연산의 성능저하 문제를 개선한 수 있는 Spatial Hash Strip Join(SHSJ) 알고리즘을 제안한다. SHJ에서 편향된 데이터의 경우 해쉬 버킷의 오버플로우 처리를 위해 버킷 재분할 방법을 사용하고 있는데 반하여 본 논문에서 제안한 SHSJ 알괴리즘에서는 버킷의 재분할 처리 대신에 버킷에 데이터를 삽입하고, 조인 연산과정에서 오버플로우가 발생한 버킷에 대하여 SSSJ 알고리즘을 사용함으로써 편향된 입력 릴레이션의 처리 성능을 제고시킬 수 있도록 한다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.19-21
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• 2004

이 논문은 인덱스가 존재하지 않는 두 입력 테이블의 공간 조인 연산 과정 중 여과 단계 처리에 중점을 둔다. 관련 연구는 Spatial Hash Join(SHJ)과 Scalable Sweeping-Based Spatial Join(SSSJ) 알고리즘이 대표적이다. 하지만 조인을 위한 입력 테이블의 객체들이 편중 분포할 경우 성능이 급격히 저하되는 문제를 가지고 있다. 따라서, 이 논문에서는 이러한 문제를 해결하기 위해 기존 SHJ 알고리즘과 SSSJ 알고리즘의 특성을 이용한 Spatial Hash Strip Join(SHSJ) 알고리즘을 제안한다. 기존 SHJ 알고리즘과의 차이점은 입력 데이터 집합을 버킷에 할당할 때 버킷 용량에 제한을 두지 않는다는 점과 버킷의 조인 단계에서 I/O 성능의 향상을 위해 우수한 SSSJ 알고리즘을 사용한다는 것이다. 끝으로 이 논문에서 제안한 SHSJ 알고리즘의 성능은 실제 Tiger/line 데이터를 이용하여 실험한 결과 기존의 SHJ와 SSSJ 알고리즘 보다 편중된 입력 테이블의 조인 연산에 대해 월등히 우수함이 검증되었다.

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.591-602
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• 2003

Delay and discontinuance phenomenon of service are cause by sudden increase of the network communication amount and the quantity consumed of resources when Internet users are driven excessively to a conventional single large database sewer. To solve these problems, spatial database cluster consisted of several single nodes on high-speed network to offer high-performance is risen. But, research about spatial join operation that can reduce the performance of whole system in case process at single node is not achieved. So, in this paper, we propose efficient parallel spatial join processing method in a spatial database cluster system that uses data partitions and replications method that considers the characteristics of space data. Since proposed method does not need the creation step and the assignment step of tasks, and does not occur additional message transmission between cluster nodes that appear in existent parallel spatial join method, it shows performance improvement of 23% than the conventional parallel R-tree spatial join for a shared-nothing architecture about expensive spatial join queries. Also, It can minimize the response time to user because it removes redundant refinement operation at each cluster node.

• Journal of Korea Spatial Information System Society
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• v.10 no.4
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• pp.59-66
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• 2008

GeoSensor network means sensor network infra and related software of specific form monitoring a variety of circumstances over geospatial. And these GeoSensor network is implemented by mixing data stream with spatial attribute, spatial relation. But, until a recent date sensor network system has been concentrated on a store and search method of sensor data stream except for a spatial information. In this paper, we propose a definition of spatial data stream and its join strategy model at GeoSensor network, which combine data stream with spatial data. Spatial data stream s defining in this paper are dynamic spatial data stream of a moving object type and static spatial data stream of a fixed type. Dynamic spatial data stream is data stream transmitted by moving sensor as GPS, while static spatial data stream is generated by joining a data stream of general sensor and a relation with location values of these sensors. This paper propose joins of dynamic spatial data stream and static spatial data stream, and cost models estimating join cost. Finally, we show verification of proposed cost models and performance by join strategy.

• Journal of Korea Spatial Information System Society
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• v.9 no.3
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• pp.67-80
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• 2007

Recently, many studies on query processing algorithms has been done for spatial networks, such as roads and railways, instead of Euclidean spaces, in order to efficiently support LBS(location-based service) and Telematics applications. However, both a closest pairs query and an e-distance join query require a very high cost in query processing because they can be answered by processing a set of POIs, instead of a single POI. Nevertheless, the query processing cost for closest pairs and e-distance join queries is rapidly increased as the number of k (or the length of radius) is increased. Therefore, we propose both a closest pairs query processing algorithm and an e-distance join query processing algorithm using a POI-based materialization technique so that we can process closest pairs and e-distance join queries in an efficient way. In addition, we show the retrieval efficiency of the proposed algorithms by making a performance comparison of the conventional algorithms.

• 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.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.665-676
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• 2001

The most expensive spatial operation in patial database in a spatial join which computes a combined table of which tuple consists of two tuples of the two tables satisgying 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 requiremetns of interactive users. It is usually appropriate to use parallel processing to improve the performance of spatial join processing. in spatial database the fixed grids which consist of the regularly partitioned cells can be employed the previous works on the spatial joins have not studied the parallel processing of spatial joins using fixed grids. This paper has presented an analytical cost model that estimates the comparative performance of a parallel spatial join algorithm based on the fixed grids in terms of the number of MBR comparisons. disk accesses, and message passing, Several experiments on the synthetic and real datasets show that the proposed analytical model is very accurate. This most model is also expected to used for implementing a very important DBMS component, Called the query processing optimizer.

• Spatial Information Research
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• v.16 no.2
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• pp.219-236
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• 2008

Data stream collected from sensors contain a large amount of useful information including causality relationships. The causality join query for data stream is to retrieve a set of pairs (cause, effect) from streams of data. A part of causality pairs may however be lost from the query result, due to the delay from sensors to a data stream management system, and the limited size of sliding windows. In this paper, we first investigate spatial, temporal, and spatio-temporal aspects of the causality join query for data stream. Second, we propose several strategies for sliding window management based on these observations. The accuracy of the proposed strategies is studied by intensive experiments, and the result shows that we improve the accuracy of causality join query in data stream from simple FIFO strategy.