• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.41-49
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• 2000

In this paper, we propose a new method of indexing and searching based on object-specific features at different semantic levels for video retrieval. A moving trajectory model is used as an indexing key for accessing the individual object in the semantic level. By tracking individual objects with segmented data, we can generate motion trajectories and set model parameters using polynomial curve fitting. The proposed searching scheme supports various types of queries including query by example, query by sketch, and query on weighting parameters for event-based video retrieval. When retrieving the interested video clip, the system returns the best matching event in the similarity order.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.345-349
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• 2004

In order to index exactly moving objects(vehicle, mobile phone, PDA, etc.) in the mobile database, continuous updates of their locations are inevitable as well as time-consuming. The studies of pure spatial indices have focused on the efficient retrievals. However, the acquisition and management of the terminal Location of moving objects are more important than the efficiency of the query processing in the moving object databases. Therefore, it will be need to adopt parallel processing system for the moving object databases which should maintain the object's current location as precise as possible. This paper proposes a architecture of spatial indexing mobile objects using multiple processors. More precisely, we newly propose a method of splitting buckets using the properties of moving objects in order to minimize the number of database updates. We also propose a acquisition method for gathering the location information of moving objects and passing the information of the bucket extents in order to reduce the amount of passed messages between processors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.1
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• pp.451-458
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• 2011

In order to provide the optimal location based services such as the optimal moving path search or the scheduling pattern prediction, the extraction of significant moving pattern which is considered the temporal and spatial properties of the location-based historical data of the moving objects is essential. In this paper, for the extraction of significant moving pattern we propose the location generalization method which translates the location attributes of moving object into the spatial scope information based on $R^*$-tree for more efficient patterning the continuous changes of the location of moving objects and for indexing to the 2-dimensional spatial scope. The proposed method generates the moving sequences which is satisfied the constraints of the time interval between the spatial scopes using the generalized spatial data, and extracts the significant moving patterns using them. And it can be an efficient method for the temporal pattern mining or the analysis of moving transition of the moving objects to provide the optimal location based services.

• Journal of Digital Contents Society
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• v.15 no.4
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• pp.527-532
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• 2014

Recently, due to the proliferation of mobile smart devices, the inovation of bigdata, which analyzes and processes massive data collected from various sensors implaned in smart devices, expands to LBSs. Many location prediction techniques for moving objects have been studied in literature. However, as the majority of studies perform location prediction which depends on specific applications, they hardly reflect the technical requirements of next-generation spatio-temporal information services. Therefore, this paper proposes the design of general-purpose distributed moving object prediction query processing framework that is capable of performing primitive and various types of queries effectively based on massive spatio-temporal data of moving objects in real-world space networks.

• Journal of KIISE:Databases
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• v.30 no.3
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• pp.320-335
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• 2003

Location-Based Services(LBS) give rise to location-based queries of which results depend on the locations of moving objects. One of important applications of LBS is to examine tracks of continuously moving objects. Moving objects databases need to provide 3-dimensional indexing for efficiently processing range queries on the movement of continuously changing positions. An extension of the 2-dimensional R-tree to include time dimension shows low space utilization and poor search performance, because of high overlap of index nodes and their dead space. To solve these problems, we propose a new R-tree based indexing technique, namely TR-tree. To increase storage utilization, we assign more entries to the past node by using the unbalanced splitting policy. If two nodes are highly overlapped, these nodes are forcibly merged. It is the forced merging policy that reduces the dead space and the overlap of nodes. Since big line segments can also affect the overlap of index nodes to be increased, big line segments should be clipped by the clipping policy when splitting overfull nodes. The TR-tree outperforms the 3DR-tree and TB-tree in all experiments. Particularly, the storage utilization of the TR-tree is higher than the R-tree and R*-tree.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.2057-2064
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• 2010

With the development of wireless network technology, the location information of a spatiotemporal object which changes their location is used in various application. Each spatiotemporal object has many location information, hence it is inefficient to search all trajectory information of spatiotemporal objects for a range query. In this paper, we propose an efficient method which divides a trajectory and stores its division data on restricted storage space. Using suboptimal split algorithm, an extended split algorithm that minimizes the volume of EMBRs(Extended Minimum Bounding Box) is designed and simulated. Our experimental evaluation confirms the effectiveness and efficiency of our proposed splitting policy

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2188-2194
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• 2011

With the development of wireless network technology, Storing the location information of a spatiotemporal object was very necessary. Each spatiotemporal object has many unnecessariness location information, hence it is inefficient to search all trajectory information of spatiotemporal objects. In this paper, we propose an efficient method which increase searching efficiency. Using EMBR(Extend Minimun Bounding Rectangle), an LinearMarge split algorithm that minimizes the volume of MBRs is designed and simulated. Our experimental evaluation confirms the effectiveness and efficiency of our proposed splitting policy.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.2
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• pp.30-41
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• 2006

This paper proposes an e-logistics system supporting efficient vehicle moving trajectory management. Recent advances in wireless communications have given rise to a number of location-based services including logistics vehicle tracking, cellular phone user's location finding, and location-based commerce. Logistics systems typically entail tracking vehicles for purposes of the logistics center knowing the whereabouts of the vehicles and/or consignments. Moreover, storing and managing location trajectory of continuously moving vehicles and consignments is necessary for supporting efficient logistics plan and consignment. The proposed system is able to manage spatial objects in GIS as well as logistic information in the mobile environment. And for the efficiently managing and retrieving of transporting trajectory of logistics, we extend previous moving object indexing method, TB-Tree, to use multi-version framework and evaluate data updating performance. It is able to apply the proposed method to develop mobile contents services based on continuously changing location of moving object in the mobile environment.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.879-888
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• 2006

Recently many researchers have focused on management of Historical trajectories of moving objects in Euclidean spaces due to numerous sizes of accumulated data over time. However, the movement of moving objects in real applications generally has some constraints, for example vehicles on roads can only travel along connected road networks. In this paper, we propose an indexing method for trajectories of moving objects on road networks in order to process the network-based spatiotemporal range query. Our method contains the connect information of road networks to use the network distance for query processing, deals with trajectories which are represented by road segments in road networks, and manages them using multiple R-trees assigned per each road segment. Furthermore, it has a structure to be able to share R-tree among several road segments in large road networks. Consequently, we show that our method takes about 30% less in node accesses for the network-based spatiotemporal range query processing than other methods based on the Euclidean distance by experiments.

• Proceedings of the Korea Contents Association Conference
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• 2014.11a
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• pp.35-36
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• 2014

이 논문에서는 다수 노드로 구성되는 클러스터 환경을 위한 인-메모리 이동객체 분산 색인기법을 제안한다. 제안하는 방법은 Spark Stream의 D-strem모델을 사용하여 처리율 저하등의 문제를 유할 할 수 있는 잠금 기반의 동시성 제어방법을 배제한다.