• Journal of KIISE:Computing Practices and Letters
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• v.16 no.7
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• pp.819-823
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• 2010

The skyline query is one of the effective methods to deal with the large amounts and multi-dimensional data set. By utilizing the concept of 'dominate' the skyline query can pinpoint the target data so that the dominated ones, about 95% of them, can efficiently be excluded as an unnecessary data. Most of the skyline query algorithms, however, have been developed in terms of the numerical data set. This paper pioneers an entirely new domain, the categorical data, on which the corresponding ranking measures for the skyline queries are suggested. In the experiment, the ACM Computing Classification System has been exploited to which our methods are significantly represented with respect to performance thresholds such as the processing time and precision ratio, etc.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.938-942
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• 2009

In this paper, we propose a novel filtering method for processing continuous skyline queries on wireless sensor network environments. The existing filtering methods use the filter based on router paths. However, because these filters are applied not to a whole area but to a partial area, these methods send almost data of sensor nodes to transmit to the base station and have no sufficient effect in terms of energy efficiency. Therefore, we propose an efficient method to dramatically reduce the transmission data of sensors through applying a low-cost and effective filter to all sensor nodes. The proposed effective filter is generated by using characteristics such as the data locality and the clustering of sensors. An extensive performance study verifies the merits of our new method.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.139-148
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• 2008

The sensor network is applied from the field which is various. The sensor network nodes are exchanged with mobile environment and they construct they select cluster and cluster headers. In this paper, we propose the Dynamic Prediction Clustering Algorithm use to Skyline queries attributes in direction, angel and hop. This algorithm constructs cluster in base mobile sensor node after select cluster header. Propose algorithm is based made cluster header for mobile sensor node. It "Adv" reduced the waste of energy which mobile sensor node is unnecessary. Respects clustering where is efficient according to hop count of sensor node made dynamic cluster. To extend a network life time of 2.4 times to decrease average energy consuming of sensor node. Also maintains dynamic cluster to optimize the within hop count cluster, the average energy specific consumption of node decreased 14%.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.1-8
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• 2017

Given a multi-dimensional dataset of tuples, a skyline query returns a subset of tuples which are not 'dominated' by any other tuples. Skyline query is very useful in Big data analysis since it filters out uninteresting items. Much interest was devoted to the MapReduce-based parallel processing of skyline queries in large-scale distributed environment. There are three requirements to improve parallelism in MapReduced-based algorithms: (1) workload should be well balanced (2) avoid redundant computations (3) Optimize network communication cost. In this paper, we introduce MR-SEAP (MapReduce sample Skyline object Equality Angular Partitioning), an efficient angular space partitioning based skyline query processing using sampling-based pruning, which satisfies requirements above. We conduct an extensive experiment to evaluate MR-SEAP.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.73-75
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• 2005

최근에 이동 객체의 위치 정보를 활용한 위치기반서비스(Location-based Services : LBS)에 대한 관심이 급증하고 있고, 다양한 서비스들이 연구되고 있다. 기존의 이동 객체에 대한 위치 의존 질의(Location-dependent Query)들은 단순히 대상 객체와의 거리만을 고려하였고, 스카이라인 질의(Skyline Query)는 질의의 위치와 무관한 대상 객체의 정적인 속성만을 고려하였다. 이동 객체에 대한 스카이라인 질의는 스카이라인 질의의 다중 속성과 이동 객체의 동적인 속성인 대상 객체와의 거리를 고려해야 하기 때문에 이동 객체의 위치 변경에 따른 연속적인 질의가 발생한다. 이 논문에서는 이동 객체의 연속적인 스카이라인 질의를 효율적으로 처리하기 위한 Voronoi Diagram(VD)기반의 스카이라인 영역(Skyline Region)정의와 효율적인 영역 결정 기법을 제안한다.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.1-2
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• 2010

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.23-24
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• 2010

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.4
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• pp.289-293
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• 2009

In sensor networks, many methods have been proposed to process in-network aggregation effectively. Contrary to normal aggregation queries, skyline query processing that compare multi-dimension data for producing result is very hard. 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 filters transmission. In this paper, we propose a lazy filtering-based skyline query processing algorithm of in-network for reducing energy consumption by filters transmission. 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 the proposed algorithm reduces false positive by 53% and improves network lifetime by 44% on average over MFTAC.

• Proceedings of the Korean Information Science Society Conference
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• 2010.11a
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• pp.11-12
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• 2010