• KIISE Transactions on Computing Practices
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• v.21 no.8
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• pp.561-566
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• 2015

Hadoop is becoming widely adopted in scientific and commercial areas as an open-source distributed data processing framework. Recently, for real-time processing and analysis of data, an attempt to apply high-performance computing technologies to Hadoop is being made. In this paper, we have expanded the Hadoop Filesystem library to support Lustre, which is a popular high-performance parallel distributed filesystem, and implemented the Hadoop MapReduce execution environment over the Lustre filesystem. We analysed Hadoop MapReduce over Lustre by using Hadoop standard benchmark tools. We found that Hadoop MapReduce over Lustre execution has a performance 2-13 times better than a typical Hadoop MapReduce execution.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.2
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• pp.127-135
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• 2017

The computing environment has changed rapidly to enable large-scale finite element models to be analyzed at the PC or workstation level, such as multi-core CPU, optimal math kernel library implementing BLAS and LAPACK, and popularization of direct sparse solvers. In this paper, the design considerations on a parallel finite element code for shared memory based multi-core CPU system are proposed; (1) the use of optimized numerical libraries, (2) the use of latest direct sparse solvers, (3) parallelism using OpenMP for computing element stiffness matrices, and (4) assembly techniques using triplets, which is a type of sparse matrix storage. In addition, the parallelization effect is examined on the time-consuming works through a large scale finite element model.

• Journal of Internet Computing and Services
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• v.18 no.4
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• pp.99-108
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• 2017

Open Science is evolving not only to share research results, but also to open the research process. We are developing the EDISON platform for the spread of open science in computational science and engineering. The EDISON platform provides online simulation services developed by computational science and engineering researchers. It also provides an environment for sharing source code, data, and related research publications. An effective simulation software registration management system is required for successful service on the EDISON platform. In this paper, we proposes a simulation software management system to provide online simulation service through EDISON platform. The proposed system allows the developer to register the simulation software on the EDISON platform without administrator intervention and effectively build a web-based simulation environment.

• Korean Journal of Agricultural and Forest Meteorology
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• v.13 no.2
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• pp.101-108
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• 2011

Climate change could have a pronounced impact on natural and agricultural ecosystems. To assess the impact of climate change, projected climate data have been used as inputs to models. Because such studies are conducted occasionally, it would be useful to employ Cloud computing, which provides multiple instances of operating systems in a virtual environment to do processing on demand without building or maintaining physical computing resources. Furthermore, it would be advantageous to use open source geospatial applications in order to avoid the limitations of proprietary software when Cloud computing is used. As a pilot study, Amazon Web Service ? Elastic Compute Cloud (EC2) was used to calculate the number of days with rain in a given month. Daily sets of climate projection data, which were about 70 gigabytes in total, were processed using virtual machines with a customized database transaction application. The application was linked against open source libraries for the climate data and database access. In this approach, it took about 32 hours to process 17 billion rows of record in order to calculate the rain day on a global scale over the next 100 years using ten clients and one server instances. Here I demonstrate that Cloud computing could provide the high level of performance for impact assessment studies of climate change that require considerable amount of data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.4
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• pp.2749-2756
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• 2015

Recently, Depending on the quality of data increases, the problem of time-consuming to process the image is raised by being required to accelerate the image processing algorithms, in a traditional CPU and CUDA(Compute Unified Device Architecture) based recognition system for computing speed and performance gains compared to OpenMP When character recognition has been learned by the system to measure the input by the character data matching is implemented in an environment that recognizes the region of the well, so that the font of the characters image learning English alphabet are each constant and standardized in size and character an image matching method for calculating the matching has also been implemented. GPGPU (General Purpose GPU) programming platform technology when using the CUDA computing techniques to recognize and use the four cores of Intel i5 2500 with OpenMP to deal quickly and efficiently an algorithm, than the performance of existing CPU does not produce the rate of four times due to the delay of the data of the partition and merge operation proposed a method of improving the rate of speed of about 3.2 times, and the parallel processing of the video card that processes a result, the sequential operation of the process compared to CPU-based who performed the performance gain is about 21 tiems improvement in was confirmed.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.69-77
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• 2021

The main channels of the financial industry are rapidly changing to mobile. In this environment, banks are focusing on information and communication technology to secure their competitiveness, and rapid innovation is being pursued especially in the payment settlement sector. In October 2019, Korea also introduced open banking services to accelerate the financial innovation, such as the open conversion of financial settlement networks and the expansion of the use of simple payments. In this paper, an empirical study was conducted on the effect of domestic open banking quality factors on usage intention. The service quality factors for open banking were classified into interface design, innovation, security, and data sharing, and the technology acceptance model (TAM) was used to verify whether it has a significant effect on perceived convenience, usefulness and intention to use. According to the analysis results, while innovation and security did not have a significant effect on convenience and usefulness, interface design and data sharing were found to have an effect on perceived convenience. The results would provide implications for some quality issues for companies seeking to introduce open banking services as well as for the related academic arena.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.569-584
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• 2016

In this paper, we present a spatial knowledge extractor implemented in Hadoop MapReduce parallel, distributed computing environment. From a large spatial dataset, this knowledge extractor automatically derives a qualitative spatial knowledge base, which consists of both topological and directional relations on pairs of two spatial objects. By using R-tree index and range queries over a distributed spatial data file on HDFS, the MapReduce-enabled spatial knowledge extractor, MRSPAKE, can produce a web-scale spatial knowledge base in highly efficient way. In experiments with the well-known open spatial dataset, Open Street Map (OSM), the proposed web-scale spatial knowledge extractor, MRSPAKE, showed high performance and scalability.

• Journal of Internet Computing and Services
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• v.10 no.3
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• pp.71-83
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• 2009

In this paper, we propose a paging scheme considering local environment and moving patterns of mobile hosts on HMIPv6. The proposed paging scheme can adaptively construct a paging domain according to handoff probability of mobile hosts and local environment, and process a paging based on handoff probability stage by stage. For analysis of the proposed paging scheme, we simulate our scheme in two different experiment environments: an open environment like a square and a restricted environment like downtown. In experiment results, the proposed scheme shows superior performance to that of compared scheme all over. Particularly, in case of high call request rate and a restricted environment, the proposed scheme shows better performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.554-557
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• 2016

Recently, supercomputer has been increasingly adopted as a computing environment for scientific simulation as well as education, healthcare and national defence. Especially, supercomputing system with heterogeneous computing resources is gaining resurgence of interest as a next-generation problem solving environment, allowing theoretical and/or experimental research in various fields to be free of time and spatial limits. However, traditional supercomputing services have only been handled through a simple form of command-line based console, which leads to the critical limit of accessibility and usability of heterogeneous computing resources. To address this problem, in this paper, we provide the design and implementation of web-based HPC (High Performance Computing) job management framework for computational scientific simulation. The proposed framework has highly extensible design principles, providing the abstraction interfaces of job scheduler (as well as bundle scheduler plug-ins for LoadLeveler, Sun Grid Engine, OpenPBS scheduler) in order to easily incorporate the broad spectrum of heterogeneous computing resources such as cluster, computing cloud and grid. We also present the detailed specification of HTTP standard based RESTful endpoints, which manage simulation job's life-cycles such as job creation, submission, control and status monitoring, etc., enabling various 3rd-party applications to be newly created on top of the proposed framework.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.129-139
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• 2018

The crowdsourcing software development (CSD) is growing rapidly in the open call format in a competitive environment. In CSD, tasks are posted on a web-based CSD platform for CSD workers to compete for the task and win rewards. Task searching and assigning are very important aspects of the CSD environment because tasks posted on different platforms are in hundreds. To search and evaluate a thousand submissions on the platform are very difficult and time-consuming process for both the developer and platform. However, there are many other problems that are affecting CSD quality and reliability of CSD workers to assign the task which include the required knowledge, large participation, time complexity and incentive motivations. In order to attract the right person for the right task, the execution of action plans will help the CSD platform as well the CSD worker for the best matching with their tasks. This study formalized the task assignment method by utilizing different situations in a CSD competition-based environment in artificial intelligence (AI) planning. The results from this study suggested that assigning the task has many challenges whenever there are undefined conditions, especially in a competitive environment. Our main focus is to evaluate the AI automated planning to provide the best possible solution to matching the CSD worker with their personality type.