• Journal of Korea Spatial Information System Society
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• v.6 no.1 s.11
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• pp.59-71
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• 2004

Nearest neighbor query retrieves nearest located target objects, and is very frequently used in mobile environment. In this paper we propose a novel neatest neighbor query processing technique that is able to retrieve nearest located target object from the user who is continuously moving with a direction. The proposed method retrieves objects using the direction property of moving object as well as euclidean distance to target object. The proposed method is applicable to traffic information system, travel information system, and location-based recommendation system which require retrieving nearest located object.

• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.113-122
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• 2009

Since the traditional pattern mining methods only probe unspecified moving patterns that seem to satisfy users' requests among diverse patterns within the limited scopes of time and space, they are not applicable to problems involving the mining of optimal moving patterns, which contain complex time and space constraints, such as 1) searching the optimal path between two specific points, and 2) scheduling a path within the specified time. Therefore, in this paper, we illustrate some problems on mining the optimal moving patterns with complex time and space constraints from a vast set of historical data of numerous moving objects, and suggest a new moving pattern mining method that can be used to search patterns of an optimal moving path as a location-based service. The proposed method, which determines the optimal path(most frequently used path) using pattern frequency retrieved from historical data of moving objects between two specific points, can efficiently carry out pattern mining tasks using by space generalization at the minimum level on the moving object's location attribute in consideration of topological relationship between the object's location and spatial scope. Testing the efficiency of this algorithm was done by comparing the operation processing time with Dijkstra algorithm and $A^*$ algorithm which are generally used for searching the optimal path. As a result, although there were some differences according to heuristic weight on $A^*$ algorithm, it showed that the proposed method is more efficient than the other methods mentioned.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10c
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• pp.205-209
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• 2006

무선 네트워크의 기술 발달과 이동기기들의 많은 보급에 따라 GIS, LBS 등의 위치기반 서비스 등이 널리 사용되고 있다. 이러한 서비스를 수행하기위하여 방대한 양의 이동 객체들의 효율적인 관리, 검색에 대한 연구가 많이 이루어지고 있으며 이동 객체들의 특성과 사용목적에 따라 다양한 연구결과가 제시되고 있다. 본 논문에서는 시간에 따라 다양한 속도와 방향으로 이동하는 다차원 이동 공간 데이터를 위한 효율적인 색인 기법을 제시한다. 제안된 방법은 KDB-Tree와 QSF-Tree에 기반을 두고 있으며 색인 구조의 검색 성능을 향상시키기 위한 분할, 갱신 방법을 제시하여 시간에 따라 현재 속도와 방향이 다양하게 변화하는 데이터를 신속히 데이터를 색인하고 영역 질의 뿐만 아니라 미래 예측 질의에 대하여 현재 보유한 데이터 정보를 기반으로 미래 예측 결과를 신속히 산출할 수 있도록 한다.

• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.87-96
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• 2006

Selectivity estimation is one of the query optimization techniques. It is difficult for the previous selectivity estimation techniques for moving objects to apply the position change of moving objects to synopsis. Therefore, they result in much error when estimating selectivity for queries, because they are based on the extended spatial synopsis which does not consider the property of the moving objects. In order to reduce the estimation error, the existing techniques should often rebuild the synopsis. Consequently problem occurs, that is, the whole database should be read frequently. In this paper, we proposed a moving object histogram method based on quad tree to develop a selectivity estimation technique for moving object queries. We then analyzed the performance of the proposed method through the implementation and evaluation of the proposed method. Our method can be used in various location management systems such as vehicle location tracking systems, location based services, telematics services, emergency rescue service, etc in which the location information of moving objects changes over time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2153-2160
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• 2015

An analysis of hardware design conditions of moving object detection (MOD) algorithm is described, which is based on effective Gaussian mixture learning (EGML). A simulation model of EGML algorithm is implemented using OpenCV, and the effects of some parameter values on background learning time and MOD sensitivity are analyzed for various images. In addition, optimal design conditions for hardware implementation of EGML-based MOD algorithm are extracted from fixed-point simulations for various bit-widths of parameters. The proposed fixed-point model of the EGML-based MOD uses only half of the bit-width at the expense of the loss of MOD performance within 0.5% when compared with floating-point MOD results.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05c
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• pp.1543-1546
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• 2003

최근에는 GPS(Global Positioning System)와 무선 데이터 전송 능력이 있는 휴대용 전화기의 보급 및 이동/무선 컴퓨팅 기술의 발달로 인해 이를 이용한 응용 서비스에 대한 관심이 고조되고 있다. 특히, 이동 차량, PDA, 휴대용 전화기, 노트북 컴퓨터 등을 이용한 위치기반서비스(LBS: Location Based Services)가 무선 인터넷 시장의 중요한 이슈가 되고 있다. 위치 정보를 기반으로 다양한 위치기반서비스를 제공할 수 있는 플랫폼은 향후 무선 인터넷 응용의 핵심기술이므로, 모바일 환경에서는 이러한 이동 객체들의 위치정보를 효과적으로 관리할 수 있어야 한다. 따라서 이 논문에서는 현재 모바일 환경에서 위치기반서비스 관리 및 제공과정에서 얻어지는 데이터 형태를 살펴보고, 기존의 이동 객체 색인들을 이용하여 실세계에서 얻어지는 데이터를 처리하였을 때의 문제점들을 검토한다. 피리고 이러한 문제를 해결할 수 있는 색인방법으로 동일한 시공간 정보를 갖는 이동 객체들을 블록단위로 저장하는 색인 구조를 제시함으로써 전체 색인 크기를 줄여 중복검색을 해결하고 디스크 I/O수를 줄이도록 설계한다.

• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.237-248
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• 2009

Recently, based on dynamic location or mobility of moving object, many researches on pattern mining methods actively progress to extract more available patterns from various moving patterns for development of location based services. The performance of moving pattern mining depend on how analyze and process the huge set of spatio-temporal data. Some of traditional spatio-temporal pattern mining methods[1-6,8-11]have proposed to solve these problem, but they did not solve properly to reduce mining execution time and minimize required memory space. Therefore, in this paper, we propose new spatio-temporal pattern mining method which extract the sequential and periodic frequent moving patterns efficiently from the huge set of spatio-temporal moving data. The proposed method reduces mining execution time of $83%{\sim}93%$ rate on frequent moving patterns mining using the moving sequence tree which generated from historical data of moving objects based on hash tree. And also, for minimizing the required memory space, it generalize the detained historical data including spatio-temporal attributes into the real world scope of space and time using spatio-temporal concept hierarchy.

• The KIPS Transactions:PartD
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• v.11D no.2
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• pp.351-360
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• 2004

Most mobile agents are migrated to many mobile agent systems by the sequential node migration method. However. in this case, if some problems such as host's fault or obstacle etc. happened, mobile agent falls infinity walt or orphan states. Therefore, it is difficult to get an expectation effect as use of other distribution technologies because the required time for networking between nodes increases. And so, many researches have been performed to solve this problems. However, most of methods decide node migration based on passive routing table or detour hosts which have some problems. Actually, the researches for reducing the total required time for networking are insufficient yet. In this paper, to reduce the required time for networking of mobile agent we design an active routing table based on the information of implemented objects which are registered in the meta-table of naming agent. And also, for user's keyword, we propose an replication model that replicates many agent object according to the information and number of object references corresponding to meta-table. Replicated objects are migrated to mobile agent systems in parallel and it provides minimized required time for networking.

• Journal of Korea Spatial Information System Society
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• v.10 no.3
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• pp.67-78
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• 2008

With the rapid development of wireless communication and mobile equipment, many applications for location-based services have been emerging. Moving objects such as vehicles and ships change their positions over time. Moving objects have their moving path, called the trajectory, because they move continuously. To monitor the trajectory of moving objects in a large scale database system, an efficient Indexing scheme to processed queries related to trajectories is required. In this paper, we focus on the issues of minimizing the dead space of index structures. The Minimum Bounding Boxes (MBBs) of non-leaf nodes in trajectory-preserving indexing schemes have large amounts of dead space since trajectory preservation is achieved at the sacrifice of the spatial locality of trajectories. In this thesis, we propose entry relocating techniques to reduce dead space and overlaps in non-leaf nodes. we present performance studies that compare the proposed index schemes with the TB-tree and the R*-tree under a varying set of spatio-temporal queries.

• Journal of KIISE:Databases
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• v.31 no.5
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• pp.492-504
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• 2004

Recently, as growing of interest for LBS(location-based services) techniques, lots of works on moving objects that continuously change their information over time, have been performed briskly. Also, researches for NN(nearest neighbor) query which has often been used in LBS, are progressed variously However, the results of conventional NN Query processing techniques may be invalidated as the query and data objects move. Therefore, they are usually meaningless in moving object management system such as LBS. To solve these problems, in this paper we propose a new nearest neighbor query processing technique, called CTNN, which is possible to meet accurate and continuous query processing for moving objects. Our techniques include an Approximate CTNN(ACTNN) technique, which has quick response time, and an Exact CTNN(ECTNN) technique, which makes it possible to search nearest neighbor objects accurately. In order to evaluate the proposed techniques, we experimented with various datasets. Experimental results showed that the ECTNN technique has high accuracy, but has a little low performance for response time. Also the ACTNN technique has low accuracy comparing with the ECTNN, but has quick response time The proposed techniques can be applied to navigation system, traffic control system, distribution information system, etc., and specially are most suitable when both data and query are moving objects and when we already know their trajectory.