• Journal of KIISE:Databases
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• v.33 no.4
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• pp.353-362
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• 2006

A data warehouse is a data repository that enables users to store large volume of data and to analyze it effectively. In this research, we investigate an algorithm to establish a multidimensional data cube which is a powerful analysis tool for the contents of data warehouses and databases. There exists an inevitable retrieval overhead in a multidimensional data cube due to the sparsity of the cube. In this paper, we propose a dense sub-cube extraction algorithm that identifies dense regions from a large sparse data cube and constructs the sub-cubes based on the dense regions found. It reduces the retrieval overhead remarkably by retrieving those small dense sub-cubes instead of scanning a large sparse cube. The algorithm utilizes the bitmap and histogram based techniques to extract dense sub-cubes from the data cube, and its effectiveness is demonstrated via an experiment.

• 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 the Korean Data and Information Science Society
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• v.14 no.1
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• pp.37-44
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• 2003

One of the characteristics of both on-line analytical processing(OLAP) applications and decision support systems is to provide aggregated source data. The purpose of this study is to discuss on the aggregation of multi-dimensional data. In this paper, we (1) examine the SQL aggregate functions and the GROUP BY operator, (2) introduce the Data Cube and MDX, (3) present an example for the practical usage of the Data Cube and MDX using sample data.

• 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 Aerospace System Engineering
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• v.6 no.4
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• pp.34-39
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• 2012

CubeSats classified as pico-class satellite require a ground station to track the satellite, transmit a command, and receive an on-orbit data such as SOH (State-of-Health) and mission data according to the operation plan. For this, ground station system has to be properly designed to perform a communication to with the satellite with enough up- and down-link budgets. In this study, a conceptual design of the ground station has been performed for the CubeSat named as STEP Cube Lab. (Cube Laboratory for Space Technology Experimental Project). The paper includes a ground station hardware interface design, link budget analysis and a ground station software realization. In addition, the operation plan of the ground station has been established considering the STEP Cube Lab. mission requirements.

• Journal of Korea Multimedia Society
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• v.9 no.11
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• pp.1395-1409
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• 2006

A spatial data warehouse has spatial data cube composed of multi-dimensional data for efficient OLAP(On-Line Analytical Processing) operations. A spatial data cube supporting concept hierarchies holds huge amount of data so that many researches have studied a incremental update method for minimum modification of a spatial data cube. The Cube, however, compressed by eliminating prefix and suffix redundancy has coalescing paths that cause update inconsistencies for some updates can affect the aggregate value of coalesced cell that has no relationship with the update. In this paper, we propose incremental batch update method of a spatial data cube. The proposed method uses duplicated nodes and extended node structure to avoid update inconsistencies. If any collision is detected during update procedure, the shared node is duplicated and the duplicate is updated. As a result, compressed spatial data cube that includes concept hierarchies can be updated incrementally with no inconsistency. In performance evaluation, we show the proposed method is more efficient than other naive update methods.

• Journal of KIISE:Databases
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• v.32 no.1
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• pp.1-11
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• 2005

An aggregation query computes aggregate information over a data cube in the query range specified by a user Existing methods based on the prefix-sum approach use an additional cube called the prefix-sum cube(PC), to store the cumulative sums of data, causing a high space overhead. This space overhead not only leads to extra costs for storage devices, but also causes additional propagations of updates and longer access time on physical devices. In this paper, we propose a new prefix-sum cube called 'SPEC' which drastically reduces the space of the PC in a large data warehouse. The SPEC decreases the update propagation caused by the dependency between values in cells of the PC. We develop an effective algorithm which finds dense sub-cubes from a large data cube. We perform an extensive experiment with respect to various dimensions of the data cube and query sizes, and examine the effectiveness and performance ot our proposed method. Experimental results show that the SPEC significantly reduces the space of the PC while maintaining a reasonable query performance.

• Journal of Information Technology and Architecture
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• v.9 no.4
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• pp.455-464
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• 2012

Due to the recent data explosion, methods which can meet the requirement of large data analysis has been studying. This paper proposes MRIterativeBUC algorithm which enables efficient computation of large data cube by distributed parallel processing with MapReduce framework. MRIterativeBUC algorithm is developed for efficient iterative operation of the BUC method with MapReduce, and overcomes the limitations about the storage size and processing ability caused by large data cube computation. It employs the idea from the iceberg cube which computes only the interesting aspect of analysts and the distributed parallel process of cube computation by partitioning and sorting. Thus, it reduces data emission so that it can reduce network overload, processing amount on each node, and eventually the cube computation cost. The bottom-up cube computation and iterative algorithm using MapReduce, proposed in this paper, can be expanded in various way, and will make full use of many applications.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.44 no.3
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• pp.308-317
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• 2011

A MBES (multibeam echo sounder) survey around Yokji Island, Korea, was conducted to find an effective method for removing error data. Two post-processing software programs, PDS2000 (RESON) and HIPS (CARIS), were used to remove the error data using an interactive editing method and the CUBE algorithm filter. The post-processing with the PDS2000 and HIPS programs, using the interactive editing method, took 120 and 168 hours, respectively, and there was little difference in the seafloor images. The processing time of the PDS2000 and HIPS programs using the CUBE algorithm filter was 36 and 60 hours, respectively. Nevertheless, there was little difference in the seafloor images because of differences in the factor parameters in each of the post-processing programs. Therefore, post-processing using CUBE filtering can save time in data processing and provide consistent results, excluding the subjective decisions of the operator. This method is more effective than other methods for rejecting erroneous multibeam echo sounder data.

• Journal of Aerospace System Engineering
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• v.9 no.4
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• pp.37-42
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• 2015

The CubeSats is classified as a pico-class satellite which requires a ground station to track the satellite, transmit commands, and receive an on-orbit data such as SOH (State-of-Health) and mission data according to the operation plan. In order to this, the ground station system has to be properly designed to perform a communication to with the satellite with enough up- and down-link budgets. In this study, a conceptual design of the ground station has been performed for the CubeSat named as STEP Cube Lab. (Cube Laboratory for Space Technology Experimental Project). The paper includes a ground station hardware interface design, a link budget analysis and a ground station software realization. In addition, the operation plan of the ground station has been established considering the STEP Cube Lab. mission requirements.