• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4B
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• pp.303-310
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• 2006

In sensor network environments, a sensor node has a limited power because of their resource constraints. Therefore it is important to efficiently use its power in sensor networks. Power consumption of sensor node is closely related to its amount of transmission data. So, we need to reduce the transmission data in order to minimize the power consumption. And sensor networks are inherently unreliable because radio transmission can fail, node can move, and so on. In this paper, we propose the reliable data aggregation protocol in order to these problems. This protocol performs the routing and the query inserting process at the same time to minimize the packet loss caused by network changes. And, this protocol removes the unnecessary routing caused by the periodic routing without query. Additionally, we suggest the countermeasure algorithm against the frequent errors in sensor networks.

• The Journal of the Korea Contents Association
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• v.11 no.7
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• pp.70-79
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• 2011

The multi-path routing schemes that assigns labels to sensor nodes for the reliability of data transmission and the accuracy of an aggregation query over the sensor networks where data transfer is prone to defect have been proposed. However, the existing schemes have high costs for reassigning labels to nodes when the network topology is changed. In this paper, we propose a novel routing method that avoids duplicated data and reduces the update cost of a sensor node. In order to show the superiority of the proposed scheme, we compare it with the existing scheme through the various experiments. Our experimental results show that our proposed method reduces about 95% the amount of the transmitted data for restoration to node failure and about 220% the amount of the transmitted data for query processing over the existing method on average.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.8
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• pp.3689-3696
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• 2011

In decision support system, knowledge workers require aggregation operations of the large data and are more interested in the trend analysis rather than in the punctual analysis. Therefore, it is necessary to provide fast approximate answers rather than exact answers, and to research approximate query answering techniques. In this paper, we propose a new approximation query answering method which is based on Fuzzy C-means clustering (FCM) method and Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed method using FCM-ANFIS can compute aggregate queries without accessing massive multidimensional data cube by producing the KR model of multidimensional data cube. In our experiments, we show that our method using the KR model outperforms the NMF method.

• Journal of KIISE:Databases
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• v.30 no.5
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• pp.451-463
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• 2003

Recently, there have been various research efforts to develop strategies for accelerating OLAP operations on huge amounts of spatio-temporal data. Most of the work is based on multi-tree structures which consist of a single R-tree variant for spatial dimension and numerous B-trees for temporal dimension. The multi~tree based frameworks, however, are hardly applicable to spatio-temporal OLAP in practice, due mainly to high management cost and low query efficiency. To overcome the limitations of such multi-tree based frameworks, we propose a new approach called Hilbert Cube(H-Cube), which employs fractals in order to impose a total-order on cells. In addition, the H-Cube takes advantage of the traditional Prefix-sum approach to improve Query efficiency significantly. The H-Cube partitions an embedding space into a set of cells which are clustered on disk by Hilbert ordering, and then composes a cube by arranging the grid cells in a chronological order. The H-Cube refines cells adaptively to handle regional data skew, which may change its locations over time. The H-Cube is an adaptive, total-ordered and prefix-summed cube for spatio-temporal data warehouses. Our approach focuses on indexing dynamic point objects in static spatial dimensions. Through the extensive performance studies, we observed that The H-Cube consumed at most 20% of the space required by multi-tree based frameworks, and achieved higher query performance compared with multi-tree structures.

• 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.

• The KIPS Transactions:PartD
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• v.13D no.1 s.104
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• pp.23-28
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• 2006

In this paper, a power-aware query processing using optimized distributed R-tree in a sensor network is proposed. The proposed technique is a new approach for processing range queries that uses spatial indexing. Range queries are most often encountered under sensor networks for computing aggregation values. The previous work just addressed the importance but didn't provide any efficient technique for processing range queries. A query processing scheme is thus designed for efficiently processing them. Each node in the sensor network has the MBR of the region where its children nodes and the node itself are located. The range query is evaluated over the region which intersects the geographic location of sensors. It ensures the maximum power savings by avoiding the communication of nodes not participating over the evaluation of the query.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.132-135
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• 2015

맵리듀스(MapReduce)는 분산 환경에서의 빅데이터(Big Data), 즉 대용량 데이터를 처리하는 프로그래밍 모델이다. 대용량의 데이터를 분석하기 위해서 집계 함수(Aggregation function)로 데이터를 처리할 수 있다. 본 논문에서는 맵리듀스 환경을 기반으로 SQL 쿼리에서 집계 함수를 더 적은 비용으로 수행하며 효율적으로 처리할 수 있는 두 가지 전략을 제안한다. 두 가지 전략 중 더 높은 성능을 보이는 전략을 더 효율적인 처리 방법으로 판단한다. 첫 번째 전략은 두 테이블을 Join하여 집계 함수를 처리하는 방법이다. 두 번째 전략은 집계 함수를 처리하여 Join에 참여할 튜플의 수를 최소로 줄인 후 Join을 수행하고 다시 집계 함수를 처리하는 방법이다. 두 제안 방법을 비교하기 위하여 실험을 한 결과 두 번째 전략이 더 적은 비용이 드므로 더 효율적인 처리 방법인 것으로 보인다.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06d
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• pp.189-194
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• 2007

자원 제약적인 무선 센서네트워크상에서 전송비용을 최대한 줄이기 위하여 데이터의 수집 및 처리를 분산된 형태로 처리하는 방법이 필수적이다. 이에 따라 Declarative Query Language를 이용하여 다양한 질의를 표현하고, 이와 같은 질의를 효율적으로 처리하기 위한 에너지 분산 질의처리 방법이 중요한 이슈로 부각되고 있다. 본 논문은 [3]의 확장된 논문으로써 유한 상태 머신 기반 운영체제인 SenOS상에서 질의를 처리할 수 있는 시스템의 구조 중 SenOS의 동적 재구성 기능적 특성을 적용한 SenDB의 동적 Aggregation Function 추가 기능에 대하여 살펴보았다. 아울러 [3]에서 제안한 이기종 센서노드 연동기능의 개선점 및 구현 방법에 대하여 살펴보겠다.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.501-512
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• 2006

XML is the most popular platform-independent data expression which is used to communicate between loosely coupled heterogeneous systems such as B2B Applications or Workflow systems. The powerful query language XQuery has been developed to support diverse needs for querying XML documents. XQuery is designed to configure results from diverse data sources into a uniquely structured query result. Therefore, it became the standard for the XML query language. Although the latest XQuery supports heavy search functions including iterations, the grouping mechanism for data is too primitive and makes the query expression difficult and complex. Therefore, this work is focused on supporting the groupby clause in the query expression to process XQuery grouping. We suggest it to be a more efficient way to process grouping for restructuring and aggregation functions on XML data. We propose an XQuery EBNF that includes the groupby clause and implemented an XQuery processing system with grouping functions based on the eXist Native XML Database.

• Journal of KIISE:Databases
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• v.36 no.3
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• pp.226-234
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• 2009

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Due to the characteristics of a data stream, it is impossible to save all the data elements of a data stream. Therefore it is necessary to define a new synopsis structure to store the summary information of a data stream. For this purpose, this paper proposes a cuboid prefix tree that can be effectively employed in evaluating an iceberg query over data streams. A cuboid prefix tree only stores those itemsets that consist of grouping attributes used in GROUP BY query. In addition, a cuboid prefix tree can compute multiple iceberg queries simultaneously by sharing their common sub-expressions. A cuboid prefix tree evaluates an iceberg query over an infinitely generated data stream while efficiently reducing memory usage and processing time, which is verified by a series of experiments.