• The Journal of Society for e-Business Studies
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• v.15 no.4
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• pp.1-19
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• 2010

Recently, it is necessary to process real time sensor data, which is generated from ubiquitous environments. Data, which are written by XML, are small, but, large volumes of data. Therefore, weneed to use an efficient method for processing a large amount of it. An XQuery has two types for sensor data: one is to get sensor identification and value from sensor data; the other is restructuring for user's convenience. Existing XQuery engines don't have efficient method for batch processing of sensor data. This paper proposed the twig query processing over reverse path summary, and we developed and applied restructuring batch processing method for real time processing of a large amount of sensor data. Finally, we do performance evaluation using XMark and RFID EPC data, and comparison analysis with MonetDB/XQuery and Berkeley DB XML.

• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.377-386
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• 2001

In spatial database system, spatial view is supported for efficient access method to spatial database and is managed by materialization and non-materialization technique. In non-materialization technique, repeated execution on the same query makes problems such as the bottle-neck effect of server-side and overloads on a network. In materialization technique, view maintenance technique is very difficult and maintenance cost is too high when the base table has been changed. In this paper, the SVMT (Spatial View Materialization Technique) is proposed by using R-tree re-construction. The SVMT is a technique which constructs a spatial index according to the distribution ratio of objects in spatial view. This ratio is computed by using a SVHR (Spatial View Height in R-tree) and SVOC (Spatial View Object Count). If the ratio is higher than the average, a spatial view is materialized and the R-tree index is re-used. In this case, the root node of this index is exchanged a node which has a MBR (Minimum Boundary Rectangle) value that can contains the whole region of spatial view at a minimum size. Otherwise, a spatial view is materialized and the R-tree is re-constructed. In this technique, the information of spatial view is managed by using a SVIT (Spatial View Information Table) and is stored on the record of this table. The proposed technique increases the speed of response time through fast query processing on a materialized view and eliminates additional costs occurred from repeatable query modification on the same query. With these advantages, it can greatly minimize the network overloads and the bottle-neck effect on the server.

• Spatial Information Research
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• v.8 no.1
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• pp.155-170
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• 2000

When we use R-Tree,a spatial index, to find objects matches some predicate, it often leads to an incorrect result of perform filtering step only with MBR. And , each candidates need to be inspected to conform if it really satisfies with given query, so called, 'refinement step'. In refinement step. we should perform disk I/O and expansive spatial operations which is the cause of increasing retrieval costs. Therefore, to minimize the number of candidate after filtering step, two-phase filtering methods were studied, but there was many problems such as inefficiency of filtering,maintenance of additional informations and reconstruction of data resulted from the loss of original information. So , in this paper, I propose an Extended R-Tree which provides ability to retrieve spatial objects only with some simple logical operations using Grid Table, truth table strong the information about the existence of spatial objects, in second filtering step. Consequently , this Extended R-Tree using Grid Filter has low cost of operation for filtering because of efficient second filtering step, and better filtering efficiency caused by high quality of approximation.

• Journal of Korea Multimedia Society
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• v.10 no.8
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• pp.1017-1028
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• 2007

In this paper we propose an effective method to represent, store, and retrieve video streams efficiently from a video database. We extract features from each video frame, normalize the feature values, and represent them as values in the range [0,1]. In this way a video frame with f features can be represented by a point in the f-dimensional space $[0,1]^f$, and thus the video stream is represented by a trail of points in the multidimensional space. The video stream is partitioned into video segments based on camera shots, each of which is represented by a trend vector which encapsulates the moving trend of points in a segment. The video stream query is processed depending on the comparison of those trend vectors. We examine our method using a collection of video streams that are composed of sports, news, documentary, and educational videos. Experimental results show that our trend vector representation reduces a reconstruction error remarkably (average 37%) and the retrieval using a trend vector achieves the high precision (average 2.1 times) while maintaining the similar response time and recall rate as existing methods.

• Spatial Information Research
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• v.19 no.1
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• pp.107-116
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• 2011

Due to development of environment of wireless Internet, location based services on the basis of spatial data have been increased such as real time traffic information as well as CNS(Car Navigation System) to provide mobile user with route guidance to the destination. However, the current application adopting the file-based system has limitation of managing and storing the huge amount of spatial data. In order to supplement this challenge, research which is capable of managing large amounts of spatial data based on embedded database system is surely demanded. For this reason, this study aims to suggest the lossless compression technique by using the hybrid approach of BytePacking and Lempel-Ziv which can be applicable in DBMS so as to save a mass spatial data efficiently. We apply the proposed compression technique to actual the Seoul and Inchcon metropolitan area and compared the existing method with suggested one using the same data through analyzing the query processing duration until the reconstruction. As a result of comparison, we have come to the conclusion that suggested technique is far more performance on spatial data demanding high location accuracy than the previous techniques.