• Journal of KIISE:Databases
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• v.36 no.4
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• pp.280-291
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• 2009

Skyline Query processing is important to wireless sensor applications in order to process multi-dimensional data efficiently. Most skyline researches about sensor network focus on minimizing the energy consumption due to the battery powered constraints. In order to reduce energy consumption, Filtering Method is proposed. Most existing researches have assumed a snapshot skyline query processing and do not consider continuous queries and use data generated in ancestor node. In this paper, we propose an energy efficient method called Bottom up filtering tuple selection for continuous skyline query processing. Past skyline data generated in child nodes are stored in each sensor node and is used when choosing filtering tuple. We also extend the algorithms, called Support filtering tuple(SFT) that is used when we choose the additional filtering tuple. There is a temporal correlation between previous sensing data and recent sensing data. Thus, Based on past data, we estimate current data. By considering this point, we reduce the unnecessary communication cost. The experimental results show that our method outperforms the existing methods in terms of both data reduction rate(DRR) and total communication cost.

• Journal of KIISE:Databases
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• v.36 no.6
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• pp.476-485
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• 2009

In sensor networks, many methods have been proposed to process in-network aggregation effectively. Unlike general aggregation queries, skyline query processing compares multi-dimensional data for the result. Therefore, it is very difficult to process the skyline queries in sensor networks. It is important to filter unnecessary data for energy-efficient skyline query processing. Existing approach like MFTAC restricts unnecessary data transitions by deploying filters to whole sensors. However, network lifetime is reduced by energy consumption for many false positive data and filters transmission. In this paper, we propose a bottom up filtering-based skyline query processing algorithm of in-network for reducing energy consumption by filters transmission and a PBFiltering technique for improving performance of filtering. The proposed algorithm creates the skyline filter table (SFT) in the data gathering process which sends from sensor nodes to the base station and filters out unnecessary transmissions using it. The experimental results show that our algorithm reduces false positives and improves the network lifetime over the existing method.

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

• 한국노년학
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• v.36 no.4
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• pp.1003-1024
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• 2016

The human rights of older people have gotten more attention recently in South Korea, a country that is in transition to a super-aged society. This study aimed to systematically review studies on elder abuse and related factors among community-dwelling older adults in South Korea over twenty years (1994-2016). We searched major databases (Riss, DBpia, KISS, KMbase, and PubMed) and identified published studies relevant to the topic. Based on inclusion and exclusion criteria related to study quality, a total of 31 studies were selected for this review. We examined types, measurements, and risk factors of elder abuse as well as study designs in the selected studies, guided by Johannesen's theoretical framework on elder abuse. All of the reviewed studies on elder abuse in Korea were cross-sectional studies, most of which focused on older people living in urban areas, using a non-random sampling method. All of the studies focused on certain types of elder abuse only. Some adopted elder-abuse instruments that were not validated, and others used self-developed instruments without psychometric tests. As for the risk factors of elder abuse in South Korea, the physical and mental health of the victims and aggressors impacted the risk of elder abuse, but general sociodemographic factors such as age, sex, and education were less likely to be related to the risk. In addition, decreasing caregiver burden and building elder-friendly communities are important for the prevention of elder abuse. Needed are further empirical studies on elder abuse with a theoretical framework that gives consideration to the unique sociocultural contexts of Korea. It is also recommended to develop instruments to measure elder abuse reflecting the sociocultural contexts of Korea, and to examine the multi-dimensional risk factors of elder abuse.