• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.172-174
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• 2005

센서 네트워크에서 집계(aggregation) 연산은 센서 네트워크를 데이터베이스로 구현하는데 있어서 중요하게 제공되어져야 하는 서비스이다 현재 연구되고 있는 것으로 집계 연산을 센서 네트워크의 특징상으로 분류하여 근접한 결과값을 받는 것을 허용하고 집계 값을 자식노드가 부모노드로 보내는 기간을 부모노드가 자식노드에게 나누어 할당하여 센서 네트워크상에 적절히 구현하는 것에 대해 알아보고 집계 그루핑을 하는 과정에서 데이터웨어하우징 연구의 최신기술인 점진적인 덜 관리 기법을 통해 센서노드들의 평균값에 대해 새로운 값이 추가될 때 다시 모든 값을 계산하기 않고 변경된 값만 적용하여 계산함으로 좀더 에너지 효율적으로 확장하는 것을 제안하였다.

• Journal of the Korea Computer Industry Society
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• v.5 no.9
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• pp.973-986
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• 2004

Reports have a significant meaning In large transaction environments, such as advanced the information technology and online environment. This is due to the necessity of generating reports within a giventime limit without restraining the operation performance of large transaction environments. In order to generate reports in large transaction environments while sa!isfying time-constrained requirements, this paper proposes a model which combines the incremental operation mechanism and materialized view mechanism using triggers and stored procedures. Further, the implementation and evaluation of the proposed model provides the identification of the characteristics of the proposed model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8A
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• pp.1265-1272
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• 2000

A data warehouse is able to accommodate efficient data mining query processing and subsequent response by providing information needed for decision making. In such an environment, the data warehouse stores materialized view derived from various sources to enhance query processing. The compensating algorithm to maintain materialized view is well known for a single source site environment. In the compensating algorithm, several problems arise to get results in view maintenance. The problems are due to the overhead in query management within the data warehouse, increased complexity to manage queries in the warehouse as updates occur and increased volume of message traffic between the data source and the warehouse. In this paper, we propose a new algorithm that reduces the overhead in managing queries for new maintenance and that enhances the correctness. We also measured the performance of the new algorithm by evaluating the performance of the existing recomputing view and compensating algorithm and comparing the results with the proposed algorithm.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.175-187
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• 2006

The data cube is an aggregation operator that computes group-bys for all possible combination of dimension attributes. %on the number of the dimension attributes is n, a data cube computes $2^n$ group-bys. Each group-by in a data cube is called a cuboid. Data cubes are often precomputed and stored as materialized views in data warehouses. These data cubes need to be updated when source relation change. The incremental maintenance of a data cube is to compute and propagate only its changes. To compute the change of a data cube of $2^n$ cuboids, previous works compute a delta cube that has the same number of cuboids as the original data cube. Thus, as the number of dimension attributes increases, the cost of computing a delta cube increases significantly. Each cuboid in a delta cube is called a delta cuboid. In this paper. we propose an incremental cube maintenance method that can maintain a data cube by using only $_nC_{{\lceil}n/2{\rceil}}$ delta cuboids. As a result, the cost of computing a delta cube is substantially reduced. Through various experiments, we show the performance advantages of our method over previous methods.

• Journal of Digital Contents Society
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• v.13 no.1
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• pp.43-52
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• 2012

For accurate search on information resources, it is needed to manage gradual changes in ontology efficiently. Recently, because ontology is often written using OWL, techniques that can manage changes in OWL documents are required. To meet these needs, in this paper, we classify changeable elements to detect changes in OWL ontology and propose a storage schema that can manage the changes according to the characteristics of each element. And we suggest the possibility of improving performance of query processing using views that provide information about classes or properties in each ontology version. The proposed storage schema stores changes in metadata associated with each ontology version. In addition, it can manage metadata that must be added or deleted through reasoning when ontology changes. So, the proposed storage schema can support queries about history of changes in ontology and provide accurate and valid metadata that is suitable for user-selected ontology version.