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A Study on Big Data-based GraphX Model for Social Network Service
조이상,김진홍 한국지식정보기술학회 2020 한국지식정보기술학회 논문지 Vol.15 No.6
Nowaday, towards adopting big data processing system has increased and it is commonly seen in every aspect of life. For this, the problem of finding connected components in undirected graphs has been well studied, and it is an essential pre-processing step to many graph computations, and a fundamental task in graph analytics applications. Recently, it has been a main area of interest in the large graph processing. However, much of the research has focused on solving the problem using High Performance Computers. In large distributed systems, the MapReduce framework dominates the processing of big data, and has been used for finding connected components in big graphs although iterative processing is not directly supported in MapReduce. Current big data processing systems have developed into supporting iterative processing and providing additional features other than MapReduce. This research investigates how to enhance the performance of finding connected components algorithm for large graph in distributed processing system. It uses the approach to considering the graph degree property in choosing the component identifier, reviewing how this can affect the efficiency of the algorithm. In the design of our proposed algorithm features provided by current new processing systems such as moving the computation more toward the data partition in Spark framework model are integrated.
조이상,조진수,Cho, Leesang,Cho, Jinsoo 한국교통대학교 융복합기술연구소 2015 융ㆍ복합기술연구소 논문집 Vol.5 No.1
Numerical analyses on the aerodynamic characteristics of a counter rotating axial flow fan is carried out using the frequency domain panel method. Front rotor and rear rotor blades of a counter rotating axial fan are designed by using the simplified meridional flow analysis method with the radial equilibrium equation and the free vortex design condition, according to design requirements. Performance characteristics of a counter rotating axial flow fan are estimated for the variation of design parameters such as the hub to tip ratio, the taper ratio and the solidity. Pressure losses were higher at leading edge and hub region of rotor blades. Characteristic curve of the counter rotating fan was overpredicted without consideration of viscous effect.
A Study on Big Data Processing-based Data Concentrated Computation
조이상,김진홍 한국지식정보기술학회 2021 한국지식정보기술학회 논문지 Vol.16 No.6
Over the last decades one could observe a drastic increase in the generation and storage of data in both, industry and science. While the field of data analysis is not new, it is now facing the challenge of coping with an increasing size, bandwidth and complexity of data. This renders traditional analysis methods and algorithms ineffective. This problem has been coined as the Big Data challenge. Concretely in science the major data producers are large-scale monolithic experiments and the outputs of domain simulations. Up until now, most of this data has not yet been completely analyzed, but rather stored in data repositories for later consideration due to the lack of efficient means of processing. We proposes a design and prototypical realization of such a framework based on the experience collected from empirical applications, so we called BDP(Big Data Processing). For this, selected scientific use cases, with an emphasis on earth sciences, were studied. In particular, these are object segmentation in point cloud data and biological imagery, outlier detection in oceanographic time-series data as well as land cover type classification in remote sensing images. In order to deal with the data amounts, two analysis algorithms have been parallelized for shared- and distributed-memory systems. The presented parallelization strategies have been abstracted into a generalized paradigm, enabling the formulation of scalable algorithms for other similar analysis methods. Moreover, it permits a large-scale data analysis framework and algorithm library for heterogeneous, distributed high-performance computing systems.