• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.101-104
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• 2007

Efficient Query processing and optimization are critical for reducing network traffic and decreasing latency of query when accessing and manipulating sensor data of large-scale sensor networks. Currently it has been studied in sensor database projects. These works have mainly focused on in-network query processing for sensor networks and assumes homogeneous sensor networks, where each sensor network has same hardware and software configuration. In this paper, we present a framework for efficient query processing over heterogeneous sensor networks. Our proposed framework introduces query processing paradigm considering two heterogeneous characteristics of sensor networks: (1) data dissemination approach such as push, pull, and hybrid; (2) query processing capability of sensor networks if they may support in-network aggregation, spatial, periodic and conditional operators. Additionally, we propose multi-query optimization strategies supporting cross-translation between data acquisition query and data stream query to minimize total cost of multiple queries. It has been implemented in WSN middleware, COSMOS, developed by ETRI.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.396-417
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• 2018

GR-tree and query aggregation techniques have been proposed for spatial query processing in conventional spatial query processing for wireless sensor networks. Although these spatial query processing techniques consider spatial query optimization, time query optimization is not taken into consideration. The index reorganization cost and communication cost for the parent sensor nodes increase the energy consumption that is required to ensure the most efficient operation in the wireless sensor node. This paper proposes itinerary-based R-tree (IR-tree) for more efficient spatial-temporal query processing in wireless sensor networks. This paper analyzes the performance of previous studies and IR-tree, which are the conventional spatial query processing techniques, with regard to the accuracy, energy consumption, and query processing time of the query results using the wireless sensor data with Uniform, Gauss, and Skew distributions. This paper proves the superiority of the proposed IR-tree-based space-time indexing.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.11-13
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• 2007

There has been much interest in stream query processing. Various index techniques and advanced join techniques have been proposed to efficiently process data stream queries. Previous proposals support rapid and advanced response to the data stream queries. However, the amount of data stream is increasing and the data stream query processing needs more speedup than before. In this paper, we proposed novel query processing techniques for large number of incoming documents stream. We proposed Dissection Technique for efficient query processing in the data stream environment. We focused on the dissection technique in join query processing. Our technique shows efficient operation performance comparing with the other proposal in the data stream. Proposed technique is applied to the sensor network system and XML database.

• Journal of Korea Spatial Information System Society
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• v.10 no.3
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• pp.19-30
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• 2008

Recently, the query processing algorithm in the field of spatial network database(SNDB) has been attracted by many Interests. But, there is little research on route-based queries. Since the moving objects move only in spatial networks, the efficient route-based query processing algorithms, like in-route nearest neighbor(IRNN), are essential for Location-based Service(LBS) and Telematics application. However, the existing IRNN query processing algorithm has a problem that it does not consider traffic jams in the road network. In this thesis, we propose an IRNN query processing algorithm which considers space restriction. Finally, we show that space-constrained IRNN query processing algorithm is efficient compared with the existing IRNN algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4908-4928
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• 2017

Recent research into wireless sensor network (WSN)-related technology that senses various data has recognized the need for spatio-temporal queries for searching necessary data from wireless sensor nodes. Answers to the queries are transmitted from sensor nodes, and for the efficient transmission of the sensed data to the application server, research on index processing methods that increase accuracy while reducing the energy consumption in the node and minimizing query delays has been conducted extensively. Previous research has emphasized the importance of accuracy and energy efficiency of the sensor node's routing process. In this study, we propose an itinerary-based R-tree (IR-tree) to solve the existing problems of spatial query processing methods such as efficient processing and expansion of the query to the spatio-temporal domain.

• Journal of Digital Contents Society
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• v.8 no.4
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• pp.541-546
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• 2007

Traditional query processing method is not efficient for continuous queries with large continuous stream data. This paper proposes a data-driven query processing method for stream data. The structure of query plan and query execution method are presented. With the proposed method, multiple query processing and sharing among queries can be achieved. Also query execution time can be reduced by storing partial results of query execution. This paper showed an example of query processing with XML data and XQuery query.

• Journal of KIISE:Databases
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• v.35 no.4
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• pp.356-367
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• 2008

As XML is gaining unqualified success in being adopted as a universal data representation and exchange format, particularly in the World Wide Web, the problem of querying XML documents poses interesting challenges to database researcher. Several structural XML query processing methods, including XISS and XR-tree, for past years, have been proposed for fast query processing. However, structural XML query processing has the problem of requiring expensive Join cost for twig path query Recently, sequence matching based XML query processing methods, including ViST and PRIX, have been proposed to solve the problem of structural XML query processing methods. Through sequence matching based XML query processing methods match structured queries against structured data as a whole without breaking down the queries into sub queries of paths or nodes and relying on join operations to combine their results. However, determining the structural relationship of ViST is incorrect because its numbering scheme is not optimized. And PRIX requires many processing time for matching LPS and NPS about XML data trees and queries. Therefore, in this paper, we propose efficient sequence matching method u sing the bottom-up query processing for efficient XML query processing. Also, to verify the superiority of our index structure, we compare our sequence matching method with ViST and PRIX in terms of query processing with linear path or twig path including wild-card('*' and '//').

• Journal of the Korea Society of Computer and Information
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• v.15 no.8
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• pp.149-156
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• 2010

As following the development of the USN technology, sensor node used in sensor network has capability of quick data process and storage to support efficient network configuration is enabled. In addition, tree-based structure was transformed to cluster in the construction of sensor network. However, query processing based on existing tree structure could be inefficient under the cluster-based network. In this paper, we suggest energy efficient query processing mechanism using filtering through data attribute classification in cluster-based sensor network. The suggestion mechanism use advantage of cluster-based network so reduce energy of query processing and designed more intelligent query dissemination. And, we prove excellence of energy efficient side with MATLab.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.79-86
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• 2009

The tuple of constraint database consists of constraint logical formula and it could process the presentation and query of the constraint database simply. Query operation processing shall include the constraint formula between related tuple such as selection, union, intersection of spatial data through the constraint database. However, this could produce the increasing of duplicated or unnecessary data. Hence, it will drive up the cost as per quality. This paper identified problems regarding query processing result in the constraint database. Also this paper suggested the tuple minimization summary method for result relation and analyzed the effects for efficient query processing. We were able to identify that the effectiveness of the query processing was enhanced by eliminating unnecessary constraint formula of constraint relation using the tuple minimization method.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.76-81
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• 2015

Research on top-k query processing algorithms for analyzing big data have been spotlighted recently. However, because existing top-k query processing algorithms do not provide an efficient index structure, they incur high query processing costs and cannot support various types of queries. To solve these problems, we propose a top-k query processing algorithm using a view selection method based on a grid index. The proposed algorithm reduces the query processing time by retrieving the minimum number of grid cells for the query range, by using a grid index-based view selection method. Finally, we show from our performance analysis that the proposed scheme outperforms an existing scheme, in terms of both query processing time and query result accuracy.