• Journal of the Institute of Electronics Engineers of Korea TE
• /
• v.42 no.1
• /
• pp.35-40
• /
• 2005

Recently, the location based service for moving customers is becoming one of the most important service in mobile communication area and for moving object applications, there are lots of update operations and such update loads are concentrated on some particular area unevenly. The primary processing of LBS application is spatio-temporal range queries and to improve the throughput of spatio-temporal range queries, the time of disk I/O in query processing should be reduced. In this paper, we adopt non-uniform two-level grid index structure, which are designed to minimize update operations. We propose query scheduling technique using spatial relationship and time relationship and a combined spatio-temporal query processing method using time zone concepts to improve the throughput of query processing. Some experimental results are shown for range queries with different query range to show the performance tradeoffs of the proposed methods.

• The KIPS Transactions:PartD
• /
• v.13D no.7 s.110
• /
• pp.867-878
• /
• 2006

In the past few years, the research of sensor networks is forced dramatically. Specially, while the research for maintaining the power of a sensor is focused, we are also concerned nth query processing related with the optimization of multiple continuous queries for decreasing in unnecessary energy consumption of sensor networks. We present the fragmentation algorithm to solve the redundancy problem in multiple continuous queries that increases in the count or the amount of transmitting data in sensor networks. The fragmentation algorithm splits one query into more than two queries using the query index (QR-4ree) in order to reduce the redundant query region between a newly created query and the existing queries. The R*-tree should be reorganized to the QR-tree right to the structure suggested. In the result, we preserve 20 percentage of the total energy in the sensor networks.

• Journal of Digital Contents Society
• /
• v.10 no.2
• /
• pp.239-247
• /
• 2009

In simple topology in network system, query should be delivered to all linked peers for query processing. This causes waste of transmission band width and throughput of each peer. To overcome this, as well as query processing strategy, efficient routing technique to deliver query to proper peer is needed. For efficient routing, clustering of peers in P2P networks is important. Clustering of P2P network bases on that combines peers that have similar characteristics in same cluster reduces quantity of message in network than assign peer for cluster randomly. In this paper, we propose clustering techniques for ontology based P2P query processing. Similarity measure point, cluster index structure, and query processing steps in ontology based P2P cluster environment are proposed.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.4
• /
• pp.414-422
• /
• 2011

IR (Information Retrieval) systems have the methods that compare relationships between query and index to identify document that may be fit to the user's query keyword. However, the methods usually ignore the importance of relations that are not expressed in the query. Therefore, in this study, we describe how to refine the queries' relation from keyword and to reveal the hidden intent. A useful relationship between query and keyword in IR wth studied and we classified the tion fromrelation. Firstfromall, we did researchmrelated on semantic relationship and ontolhiical researchmin foreign and domestic research, and also analyzed semantic network practices, information retrieval technolhiy, extracted and classified the tion fromrelationships s' relasite's real-world datamin whichminformation retrieval technolhiin fare applied. Next, we souiht to solve the problems occurred frequently i' relasituation that searchers tioically face. I' relacurrent search technolhiy, the mesh searchmresult fare poured by simply comparn ina query with index terms. Therefore, the need for an intelligent search fittn inusers' intent is required. The relationships between two queries to re hiddee and identify relasearcher's intent have to be revealed. By analyzn inthe practical cthes s' queries and classifyn inthem into nine kind fromrelationship tion, we proposed the method to design relation revealn inand role namn i, and we have also illustrated limitations of that methods.

• Journal of Korea Multimedia Society
• /
• v.14 no.5
• /
• pp.669-680
• /
• 2011

R-trees are widely used in various areas such as geographical information systems, CAD systems and spatial databases in order to efficiently index multi-dimensional data. As data sets used in these areas grow in size and complexity, however, range query operations on R-tree are needed to be further faster to meet the area-specific constraints. To address this problem, there have been various research efforts to develop strategies for acceleration query processing on R-tree by using the buffer mechanism or parallelizing the query processing on R-tree through multiple disks and processors. As a part of the strategies, approaches which parallelize query processing on R-tree through Graphics Processor Units(GPUs) have been explored. The use of GPUs may guarantee improved performances resulting from faster calculations and reduced disk accesses but may cause additional overhead costs caused by high memory access latencies and low data exchange rate between GPUs and the CPU. In this paper, to address the overhead problems and to adapt GPUs efficiently, we propose a novel approach which uses a GPU as a buffer to parallelize query processing on R-tree. The use of buffer algorithm can give improved performance by reducing the number of disk access and maximizing coalesced memory access resulting in minimizing GPU memory access latencies. Through the extensive performance studies, we observed that the proposed approach achieved up to 5 times higher query performance than the original CPU-based R-trees.

• Journal of Korea Multimedia Society
• /
• v.6 no.3
• /
• pp.384-396
• /
• 2003

Hybrid query processing method is used for preventing server overload that is created by heavy user connection in Web GIS. In Hybrid query processing method, both server and client participate in spatial query processing. But, Hybrid query processing method is restricted in scalability of server and it can't be fundamentally solution for server overload. So, it is necessary for Web GIS to be brought in web clustering technique. In this thesis, we propose load-balancing method that uses proximity of query region. In this paper, we create tile groups that have relation each tile in same group is very close, and forward client request to the server that can have maximum rate of buffer reuse with considering characteristic of spatial query. With out load balancing method, buffet in server is optimized for exploring spatial index tree and increase rate of buffer reuse, so it can be reduced amount of disk access and increase system performance.

• Journal of Korean Society for Geospatial Information Science
• /
• v.20 no.2
• /
• pp.81-91
• /
• 2012

As the recent development of the ubiquitous computing environment, u-GIS is being highlighted as the core technology of the ubiquitous computing environment, and thereby, studies on spatio-temporal data are being actively conducted. In this u-GIS environment, it is still difficult for existing mobile devices to efficiently manage the massive spatio-temporal data of u-GIS that are increasing day by day. Therefore, this paper develops a spatio-temporal query processing system for mobile devices in order to solve the problem. The system provides various spatio-temporal operators to insert/delete/update/search spatio-temporal data and supports a query optimization function that uses a spatio-temporal index for the flash memory and a spatio-temporal histogram for guaranteeing query execution speed. Lastly, by applying the spatio-temporal query processing system developed in this paper to the virtual scenario, this paper has proved that the system can be utilized in various application fields necessary to process spatio-temporal data in the mobile environment.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.4
• /
• pp.157-162
• /
• 2022

Today, most data is stored in a database (DB). In the DB environment, the users requests the DB to find the data they wants. Similarity Query has predicate that explained by a similarity. However, in the process of processing the similarity query, it is difficult to use an index that can reduce the range of processed records, so the cost of calculating the similarity for all records in the table is high each time. To solve this problem, this paper defines a lightweight similarity function. The lightweight similarity function has lower data filtering accuracy than the similarity function, but consumes less cost than the similarity function. We present a method for reducing similarity query processing cost by using the lightweight similarity function features. Then, Chebyshev distance is presented as a lightweight similarity function to the Euclidean distance function, and the processing cost of a query using the existing similarity function and a query using the lightweight similarity function is compared. And through experiments, it is confirmed that the similarity query processing cost is reduced when Chebyshev distance is applied as a lightweight similarity function for Euclidean similarity.

• Journal of KIISE:Databases
• /
• v.32 no.4
• /
• pp.405-415
• /
• 2005

Indexing techniques are used to implement selective tuning in wireless environments Indices are broadcast together with data to help mobile clients locate the required information. As a result, clients stay in doze mode most of the time. The drawback of this solution is that broadcast cycles are lengthened due to additional index information. In location-aware mobile services(LAMSs), it is important to reduce the query response time, since a late query response nay contain out-of-date information. In this paper, we present a broadcast-based spatial query processing method (BBS) designed to support k-NN query processing. In the BBS, broadcasted data objects are sorted sequentially based on their locations, and the server broadcasts the location dependent data along with an index segment. The performance of this scheme is investigated in relation to various environmental variables, such as the distributions of the data objects, the average speed of the clients and the size of the service area.

• Journal of the Korea Convergence Society
• /
• v.7 no.6
• /
• pp.43-48
• /
• 2016

Wireless sensor networks (WSN) can be more reliable and easier to program and use with the help of sensor database management systems (SDMS). SDMS establish a user-friendly SQL-based interface to process declarative user-defined queries over sensor readings from WSN. Typical queries in SDMS are ad-hoc snapshot queries and long-running, continuous queries. In SDMSs queries are flooded to all nodes in the sensor net, and query results are sent back from nodes that have qualified results to a base station. For query flooding to all nodes, and result flooding to the base station, a lot of communication energy consuming is required. This paper suggests an efficient in-network index solution, named Distributed Information Gathering (DIG) to process range queries in a sensor net environment that can save energy by reducing query and result flooding.