• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.138-143
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• 2002

This paper describes a framework for extending GIS databases to support moving object data type and query language. The rapid progress of wireless communications, positioning systems, and mobile computing devices have led location-aware applications to be essential components for commercial and industrial systems. Location-aware applications require GIS databases system to represent moving objects and to support querying on the motion properties of objects. For example, fleet management applications may require storage of information about moving vehicles. Also, advanced CRM(Customer Relationship Management) applications may require to store and query the trajectories of mobile phone users. In this trend, maintaining consistent information about the location of continuously moving objects and processing motion-specific queries is challenging problem. We formally define a data model and query language for mobile objects that includes complex evolving spatial structure, and propose core algebra to process the moving object query language. Main profit of proposed moving objects query language and algebra is that proposed model can be constructed on the top of GIS databases.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.272-275
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• 2004

Recently, the services using position information of moving objects is embossed. Theses services needs the moving objects databases to manage moving object data with efficiency. To build the moving object databases, we must develop the moving object engine to mange, store, and search the spatio temporal data of moving object. The moving object engine has to support query syntax to search data that suitable for user need like LBS, Telematics, ITS, vehicle management system. In this paper, we design and implement the moving object engine to support service with moving object data. The moving object engine is able to support system environment that users are able to get the moving object data easily even they don't know complex data structure.

• Journal of KIISE:Databases
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• v.31 no.3
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• pp.219-231
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• 2004

Recently, together with increasing use of multimedia data, many works on moving objects in video databases have been made. Moving objects change visual features and spatial positions with the lapse of time in video data. And they arc related to the other objects or events. In this paper, we propose a new modeling and various query types of moving objects for content based retrieval in video databases. The proposed modeling represents visual features, moving trajectories and semantic contents related to objects. Therefore, it allows to process various query types. And we propose various query operators for the retrieval types. To show the superiority of our modoling, we implement the retrieval systems and compare it with the existing methods in terms of the supporting query types. The proposed method supports various query types and improves the efficiency of the query processing over the existing methods.

• Journal of KIISE:Databases
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• v.31 no.2
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• pp.150-162
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• 2004

Moving objects have characteristics that they change continuously their positions over time. The movement of moving objects should be stored on trajectories for processing past queries. Moving objects databases need to provide spatio-temporal index for handling moving objects queries like combined queries. Combined queries consist of a range query selecting trajectories within a specific range and a trajectory query extracting to parts of the whole trajectory. Access methods showing good performance in range queries have a shortcoming that the cost of processing trajectory Queries is high. On the other hand, trajectory-based index schemes like the TB-tree are not suitable for range queries because of high overlaps between index nodes. This paper proposes new TR(Trajectory Riving)-tree which is revised for efficiently processing the combined queries. This index scheme has several features like the trajectory preservation, the increase of the capacity of leaf nodes, and the logical trajectory riving in order to reduce dead space and high overlap between bounding boxes of nodes. In our Performance study, the number of node access for combined queries in TR-tree is about 25% less than the STR-tree and the TB-tree.

• Journal of KIISE:Databases
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• v.33 no.4
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• pp.396-408
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• 2006

Moving object databases manage a large amount of past location data which are accumulated as the time goes. To retrieve fast the past location of moving objects, we need index structures which consider features of moving objects. The KDB-tree has a good performance in processing range queries. Although we use the KDB-tree as an index structure for moving object databases, there has an over-split problem in the spatial domain since the feature of moving object databases is to increase the time domain. Because the over-split problem reduces spatial regions in the MBR of nodes inverse proportion to the number of splits, there has a problem that the cost for processing spatial-temporal range queries is increased. In this paper, we propose the dynamic split strategy of the KDB-tree to process efficiently the spatial-temporal range queries. The dynamic split strategy uses the space priority splitting method for choosing the split domain, the recent time splitting policy for splitting a point page to maximize the space utilization, and the last division policy for splitting a region page. We compare the performance of proposed dynamic split strategy with the 3DR-tree, the MV3R-tree, and the KDB-tree. In our performance study for range queries, the number of node access in the MKDB-tree is average 30% less than compared index structures.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.778-784
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• 2005

Miniaturization of computing devices, and advances in wireless communication and positioning systems will create a wide and increasing range of database applications such as location-based services, tracking and transportation systems that has to deal with Moving Objects. Various types of queries could be posted to moving objects, including past, present and future queries. The key problem is how to model the location of moving objects and enable Database Management System (DBMS) to predict the future location of a moving object. It is obvious that there is a need for an innovative, generic, conceptually clean and application-independent approach for spatio-temporal handling data. This paper presents behavioral aspect of the spatio-temporal databases for managing and querying moving objects. Our objective is to impelement and extend the Spatial TAU (STAU) system developed by Dr.Pelekis that provides spatio-temporal functionality to an Object-Relational Database Management System to support modeling and querying moving objecs. The results of the impelementation are demonstrated in this paper.

• Journal of KIISE:Databases
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• v.31 no.3
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• pp.252-260
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• 2004

Recently, the TPR-tree has been proposed to support spatio-temporal queries for moving objects. Subsequently, various methods using the TPR-tree have been intensively studied. However, although the TPR-tree is one of the most popular access methods in spatio-temporal databases, any cost model for the TPR-tree has not yet been proposed. Existing cost models for the spatial index such as the R-tree do not accurately ostinato the number of disk accesses for spatio-temporal queries using the TPR-tree, because they do not consider the future locations of moving objects. In this paper, we propose a cost model of the TPR-tree for moving objects for the first time. Extensive experimental results show that our proposed method accurately estimates the number of disk accesses over various spatio-temporal queries.

• Journal of KIISE:Databases
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• v.30 no.1
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• pp.14-26
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• 2003

Moving objects' trajectories play an important role in indexing video data on their content and semantics for content-based video retrieval. In this paper, we propose new similar sub-trajectory retrieval schemes based on k-warping algorithm for efficient retrieval on moving objects' trajectories in video data. The proposed schemes are fixed-replication similar sub-trajectory retrieval(FRSR) and variable-replication similar sub-trajectory retrieval(VRSR). The former can replicate motions with a fixed number for all motions being composed of the trajectory. The latter can replicate motions with a variable number. Our schemes support multiple properties including direction, distance, and time interval as well as a single property of direction, which is mainly used for modeling moving objects' trajectories. Finally, we show from our experiment that our schemes outperform Li's scheme(no-warping) and Shan's scheme(infinite-warping) in terns of precision and recall measures.

• 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 KIISE:Databases
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• v.35 no.5
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• pp.447-458
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• 2008

In this paper, we address an efficient processing scheme for k-nearest neighbor queries to retrieve k static objects in road network databases. Existing methods cannot expect a query processing speed-up by index structures in road network databases, since it is impossible to build an index by the network distance, which cannot meet the triangular inequality requirement, essential for index creation, but only possible in a totally ordered set. Thus, these previous methods suffer from a serious performance degradation in query processing. Another method using pre-computed network distances also suffers from a serious storage overhead to maintain a huge amount of pre-computed network distances. To solve these performance and storage problems at the same time, this paper proposes a novel approach that creates an index for moving objects by approximating their network distances and efficiently processes k-nearest neighbor queries by means of the approximate index. For this approach, we proposed a systematic way of mapping each moving object on a road network into the corresponding absolute position in the m-dimensional space. To meet the triangular inequality this paper proposes a new notion of average network distance, and uses FastMap to map moving objects to their corresponding points in the m-dimensional space. After then, we present an approximate indexing algorithm to build an R*-tree, a multidimensional index, on the m-dimensional points of moving objects. The proposed scheme presents a query processing algorithm capable of efficiently evaluating k-nearest neighbor queries by finding k-nearest points (i.e., k-nearest moving objects) from the m-dimensional index. Finally, a variety of extensive experiments verifies the performance enhancement of the proposed approach by performing especially for the real-life road network databases.