• Journal of KIISE:Databases
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• v.33 no.3
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• pp.283-298
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• 2006

The TPR-tree is the time-parameterized indexing scheme that supports the querying of the current and projected future positions of such moving objects by representing the locations of the objects with their coordinates and velocity vectors. If this index is, however, used in environments that directions and velocities of moving objects, such as vehicles, are very often changed, it increases the communication cost between the server and moving objects because moving objects report their position to the server frequently when the direction and the velocity exceed a threshold value. To preserve the communication cost regularly, there can be used a manner that moving objects report their position to the server periodically. However, the periodical position report also has a problem that lineal time functions of the TPR-tree do not guarantee the accuracy of the object's positions if moving objects change their direction and velocity between position reports. To solve this problem, we propose the query processing scheme and the data structure using road networks for predicting uncertainty positions of moving objects, which is reported to the server periodically. To reduce an uncertainty of the query region, the proposed scheme restricts moving directions of the object to directions of road network's segments. To remove an uncertainty of changing the velocity of objects, it puts a maximum speed of road network segments. Experimental results show that the proposed scheme improves the accuracy for predicting positions of moving objects than other schemes based on the TPR-tree.

• 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.33 no.3
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• pp.271-281
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• 2006

Recently the need for Location-Based Service (LBS) has increased due to the development and widespread use of the mobile devices (e.g., PDAs, cellular phones, labtop computers, GPS, and RFID etc). The core technology of LBS is a moving-objects database that stores and manages the positions of moving objects. To search for information quickly, the database needs to contain an index that supports both real-time position tracking and management of large numbers of updates. As a result, the index requires a structure operating in the main memory for real-time processing and requires a technique to migrate part of the index from the main memory to disk storage (or from disk storage to the main memory) to manage large volumes of data. To satisfy these requirements, this paper suggests a unified index scheme unifying the main memory and the disk as well as migration policies for migrating part of the index from the memory to the disk during a restriction in memory space. Migration policy determines a group of nodes, called the migration subtree, and migrates the group as a unit to reduce disk I/O. This method takes advantage of bulk operations and dynamic clustering. The unified index is created by applying various migration policies. This paper measures and compares the performance of the migration policies using experimental evaluation.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.2
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• pp.85-90
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• 2017

Various mobile devices are spreading in recent developments in mobile computing environments. Especially the popularity of mobile devices equipped with GPS has become widespread, and various application services utilizing location information are born. In this paper, we propose a system model for storing and managing the trajectory of moving objects, which is the set of location information of moving objects acquired in continuous time, and the UDF (User-Defined Functions) based trajectory index method which can quickly query the large data set of moving object and the Pre-Materialized table method. Then we compare and evaluate the performance of each method through experiments. Experimental results show that the Pre-Materialized table method is about 1.2 times faster than the UDF based trajectory index method on execution time.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.65-75
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• 2005

Moving-objects databases should efficiently support database queries that refer to the trajectories and positions of continuously moving objects. To improve the performance of these queries. an efficient indexing scheme for continuously moving objects is required. To my knowledge, range queries on current positions cannot be handled by the 3D R-tree and the TB-tree. In order to handle range queries on current and past positions. I modified the original 3D R-tree to keep the now tags. Most of spatio-temporal index structures suffer from the fact that they cannot efficiently process range queries past positions of moving objects. To address this issue. we propose an access method, called the Tagged Adaptive 3DR-tree (or just TA3DR-tree), which is based on the original 3D R-tree method. The results of our extensive experiments show that the Tagged Adaptive 3DR-tree outperforms the original 3D R-tree and the TB-tree typically by a big margin.

• ETRI Journal
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• v.29 no.5
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• pp.691-693
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• 2007

Existing methods to process continuous range queries are not scalable. In particular, as the number of continuous range queries on a large number of moving objects becomes larger, their performance degrades significantly. We propose a novel query indexing method called the projected attribute bit (PAB)-based query index. We project a two-dimensional continuous range query on each axis to get two one-dimensional bit lists. Since the queries are transformed to bit lists and query evaluation is performed by bit operations, the storage cost of indexing and query evaluation time are reduced significantly. Through various experiments, we show that our method outperforms the containment-encoded squares-based indexing method, which is one of the most recently proposed methods.

• 한국공간정보시스템학회:학술대회논문집
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• 2004.12a
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• pp.113-116
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• 2004

Assisted by high technologies of information and communication in storing and collecting moving object information, many applications have been developing technical methods to exploit databases of moving objects effectively and variously. Among them, today, Current and Anticipated Future Position Indexing methods manage current positions of moving objects in order to anticipate future positions of them or more complex future queries. They, however, strongly demand update performance as fast enough to guarantee certainty of queries as possible. In this paper, we propose a new indexing mettled derived from the TPR-tree that should has update performance considerably improved, we named it BUR-tree. In our method, index structure can be inserted, deleted, and updated with a number (or bulk) of objects simultaneously rather than one object at a time as in conventional methods. This method is intended to be applied to a traffic network in which vast number of objects, such as cars, pedestrians, moves continuously.

• Journal of Korea Spatial Information System Society
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• v.9 no.1
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• pp.1-13
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• 2007

The necessity of future index is increasing to predict the future location of moving objects promptly for various location-based services. A representative research topic related to future index is the probability trajectory prediction technique that improves reliability using the past trajectory information of moving objects in the road network environment. However, the prediction performance of this technique is lowered by the heavy load of extensive future trajectory search in long-range future queries, and its index maintenance cost is high due to the frequent update of future trajectory. Thus, this paper proposes the Probability Cell Trajectory-Tree (PCT-Tree), a cell-based future indexing technique for efficient long-range future location prediction. The PCT-Tree reduces the size of index by rebuilding the probability of extensive past trajectories in the unit of cell, and improves the prediction performance of long-range future queries. In addition, it predicts reliable future trajectories using information on past trajectories and, by doing so, minimizes the cost of communication resulting from errors in future trajectory prediction and the cost of index rebuilding for updating future trajectories. Through experiment, we proved the superiority of the PCT-Tree over existing indexing techniques in the performance of long-range future queries.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06c
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• pp.46-48
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• 2006

This paper addresses the problem of monitoring continuous k-nearest neighbor (k-NN) queries. Given a set of moving (or static) objects and a set of moving (or static) query points, monitoring continuous k-NN query retrieves and updates the closest k objects to a query point continually. In order to support location based services (LBSs) in highly dynamic environments, where objects and/or queries are frequently moving, monitoring continuous queries require real-time updated results when objects and/or queries change their locations. Thus, it is important to minimize time delay for maintaining up to date the results. In this paper, we present monitoring method to shorten time delay for updating continuous k-NN queries based on the notion of result region and the minimum bounding rectangle enclosing all objects in each cell, referred to as c-MBR, in the grid index structure. Simulations are conducted to show the efficiency of the proposed method.

• Journal of the Korea Society of Computer and Information
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• v.19 no.5
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• pp.71-78
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• 2014

The RFID-tag objects can be recognized by a distinct reader where it is installed. The RFID-tag objects are likely described as storages rather than the mobiles in the use of GPS. As RFID tags are large in number compared to moving objects, so the storing and retrieval costs are highly expensive. Here, two solutions for spatio-temporal model taking account of the feature in the tagged objects are proposed. First, the moving-tag objects are expressed by the terms "now" as well as "path location". Second, the size of storing index was noticeably reduced by not saving the tag information of palletizing products but mapping the tagged objects.