• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1035-1045
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• 2015

Recent studies in Big Data Analysis are showing promising results, utilizing the main memory for rapid data processing. In-memory computing technology can be highly advantageous when used with high-performing servers having tens of gigabytes of RAM with multi-core processors. The constraint in network in these infrastructure can be lessen by combining in-memory technology with distributed parallel processing. This paper discusses the research in the aforementioned concept applying to a test taxi hailing application without disregard to its underlying RDBMS structure. The application of IMDG technology in the application's backend API without restructuring the database schema yields 6 to 9 times increase in performance in data processing and throughput. Specifically, the change in throughput is very small even with increase in data load processing.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.217-223
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• 2022

Subspace clustering for online data streams requires a large amount of memory resources as all subsets of data dimensions must be examined. In order to track the continuous change of clusters for a data stream in a finite memory space, in this paper, we propose a grid-based subspace clustering algorithm that effectively uses memory resources. Given an n-dimensional data stream, the distribution information of data items in data space is monitored by a grid-cell list. When the frequency of data items in the grid-cell list of the first level is high and it becomes a unit grid-cell, the grid-cell list of the next level is created as a child node in order to find clusters of all possible subspaces from the grid-cell. In this way, a maximum n-level grid-cell subspace tree is constructed, and a k-dimensional subspace cluster can be found at the k^th level of the subspace grid-cell tree. Through experiments, it was confirmed that the proposed method uses computing resources more efficiently by expanding only the dense space while maintaining the same accuracy as the existing method.

• 한국전산유체공학회:학술대회논문집
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• 2005.10a
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• pp.157-162
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• 2005

Parallel implementation and performance assessment of the grid assembly in a structured chimera grid approach is studied. The grid assembly process, involving hole cutting and searching donor, is parallelized on the PC cluster. A message passing programming model based on the MPI library is implemented using the single program multiple data(SPMD) paradigm. The coarse-grained communication is optimized with the minimized memory allocation because that the parallel grid assembly can access the decomposed geometry data in other processors by only message passing in the distributed memory system such as a PC cluster. The grid assembly workload is based on the static load balancing tied to flow solver. A goal of this work is a development of parallelized grid assembly that is suited for handling multiple moving body problems with large grid size.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.2
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• pp.275-282
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• 2015

Since a NAND-flash memory is able to keep data during electricity-off and has small cost to store data per bytes, it is widely used on hand-held devices. It is necessary to use an index in order to process mass data effectively on the flash memory. However, since the flash memory requires high cost for a write operation and does not support an overwrite operation, it is possible to reduce the performance of the index when the disk based index is exploited. In this paper, we implement the fixed grid file index and evaluate the performance of the index on various conditions. To do this, we measure the average processing time by the ratio of query operations and update operations. We also the compare the processing times of the flash memory with those of the magnetic disk.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.313-315
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• 2013

Hand-held devices such as smart phones exploit flash memory based storages to store data for processing jobs. Since the flash memory, non-volatile memory, is able to store mass data, it is required to use the index for processing queries. However, the flash memory has the shortcomings that it does not support the overwrite operation and its write operation is very slow. In this paper, we build the fixed grid file, one of the multi-dimensional spatial index, on a flash memory and evaluate the performance test.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.5
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• pp.125-133
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• 2016

Data processing capacity and speed are rapidly increasing due to the development of hardware and software in recent time. As a result, data usage is geometrically increasing and the amount of data which computers have to process has already exceeded five-thousand transaction per second. That is, the importance of Big Data is due to its 'real-time' and this makes it possible to analyze all the data in order to obtain accurate data at right time under any circumstances. Moreover, there are many researches about this as construction of smart factory with the application of Big Data is expected to have reduction in development, production, and quality management cost. In this paper, system using In-Memory Data Grid for high speed processing is implemented in semiconductor process which numerous data occur and improved performance is proven with experiments. Implemented system is expected to be possible to apply on not only the semiconductor but also any fields using Big Data and further researches will be made for possible application on other fields.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.75-78
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• 2017

This paper compares operational and network analysis of centralized and distributed repository for big data solutions in the IoT enabled Smart Grid environment. The comparative analysis clearly depicts that centralize repository consumes less memory consumption while distributed locality-based repository reduce network complexity issues than centralize repository in state-of-the-art Big Data Solution.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.248-252
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• 2002

Many sensors such as a structured grid pattern generator, a laser, and CCD camera to information have been used, but most of algorithms for a calibration are inefficient memory and experiment data are required. In this paper, the calibration algorithm of a structured grid pattern based on triang is introduced to calculate 3D information in the real world. The beams generated from str pattern generator established horizontally with the CCD camera are projected on the calibn CCD camera observes the intersection plane of a light and an object plane. The 3D infon calculated using observed and calibration data. This proposed method in this paper has advantages such as a memory saving and an experimental data since the 3D information are obtained simply triangulation method.

• Journal of Industrial Technology
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• v.15
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• pp.41-50
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• 1995

This paper presents a technique for estimating distribution of data stored in a database. This technique is very useful for accurate selectivity estimation, which is essential in query optimization and physical database design. To maintain data distribution, we employ the directory of the multilevel grid file, a multidimensional dynamic file organization. The major advantage of our technique is that data distribution information is maintained dynamically in the multilevel grid file. In contrast, other static methods such as the histogram method use static date structures, which requires periodic restructuring. Furthermore, we propose a method for keeping the abstract information of data distribution in main memory. This is advantageous in the situation where the size of main memory is not sufficient. Finally, We also suggest formulas for calculating selectivies of various queries based on our data distribution information.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.26 no.11
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• pp.1521-1530
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• 2002

This article proposes a pressure based method for predicting flows at all speeds. The compressible SIMPLE algorithm is extended to unstructured grid framework. Convection terms are discretized using second-order scheme with deferred correction approach. Diffusion term discretization is based on structured grid analogy that can be easily adopted to hybrid unstructured grid solver. This method also uses node centered scheme with edge based data structure for memory and computing time efficiency of arbitrary grid types. Both incompressible and compressible benchmark problems are solved using the above methodology. The demonstration of this method is extended to slip flow problem that has low Reynolds number but compressibility effect. It is shown that the proposed method can improve efficiency in memory usage and computing time without losing any accuracy.