• Asia pacific journal of information systems
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• v.25 no.3
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• pp.548-562
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• 2015

The growing popularity of mobile application has led to researchers and practitioners needing to understand users' mobile application download behaviors. Using large-scale transaction data obtained from a leading Korean telecommunications company, we empirically explore how application download rank, which appears to users when they decide to download a new application, affects their extended mobile application download. This terminology refers to downloading an additional application in the same category as those that they have already downloaded. We also consider IT characteristics, user characteristics, and application type that might be associated with the extended application download. The analysis generates the following result. Overall, a higher rank of a new application encourages the extended application download, but the linear relationship between the rank and the extended application download disappears when critical rank points are incorporated into the model. Further, no quadratic effect of rank is found in the extended application download. Based on the results, we suggest theoretical and managerial implications.

• Journal of The Korea Institute of Healthcare Architecture
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• v.4 no.6
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• pp.29-36
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• 1998

Recently considerations of the location and sizes of hospitals and healthcare facilities in a region have increased in Korea. So, this study aims to explore the physical conditions of hospitals and healthcare facilities in a large scale as well as a middle scale medical service boundary. Through the analysis of existing data of the facilities, it was revealed that most of the facilities tend to concentrate in large cities. In sum, the useful data were collected, analyzed, and synthesized through this study and could be used in the relevant research in the future as reference informations.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.500-512
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• 2022

Post-Occupancy Evaluation (POE), which is one of the construction environment evaluation methods, is a series of processes that ask about the functional requirements and satisfaction of an object from a life cycle perspective such as design/construction/residence. However, there are no POE research activities targeting large-scale units. On the other hand, large-scale third-phase new towns are being developed. Therefore, this study conducted a post-residence evaluation (POE) research activity in a large-scale unit (Multifunctional Administrative City). The procedure of this study is to conduct a literature survey on the current status and implications of the multi-functional administrative city area, and based on the research data, derive the Happy City evaluation index for the major issues and special issues of the Happy City. Afterwards, 450 questionnaires were conducted for the residents of Happy City, and POE analysis was performed on the derived data for each module. And based on the analysis results, implications such as problems and improvement points for the current status of the Happy City were derived. This can be used as a basis for the expansion of a large-scale new city into a self-sufficient city, and it can be used as a basic data for the development and improvement of a happy city that meets social needs in the future.

• Journal of the Korean GEO-environmental Society
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• v.16 no.5
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• pp.43-53
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• 2015

In this study, detection methods of outlier in various monitoring data that fit into big data category were developed and outlier detections were conducted for both artificial data and real field monitoring data. Rule-based methods applied rate of change and probability of error for monitoring data are effective to detect a large-scale short faults and constant faults having no change within a certain period. There are however, problems with misjudgement that consider the normal data with a large scale variation as outlier caused by using independent single dataset. Rule-based methods for noise faults detection have a limit to application of real monitoring data due to the problem with a choice of proper window size of data and finding of threshold for outlier judgment. A correlation analysis among different two datasets were very effective to detect localized outlier and abnormal variation for short and long-term monitoring dataset if reasonable range of training data could be selected.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.4A
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• pp.399-414
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• 2004

In this paper, an evolution of software reliability engineering in a large-scale software project is summarized. The considered software consists of many components, called functional blocks in software of switching system. These functional blocks are served as the unit of coding and test, and the software is continuously updated by adding new functional blocks. We are mainly concerned with the analysis of the effects of these software components in software reliability and reliability evolution. We analyze the static characteristics of the software related to software reliability using collected failure data during system test. We also discussed a pattern which represents a local and global growth of the software reliability as version evolves. To find the pattern of system software, we apply the S-shaped model to a collection of failure data sets of each evolutionary version and the Goel-Okumoto(G-O) model to a grouped overall failure data set. We expect this pattern analysis will be helpful to plan and manage necessary human/resources fur a new similar software project which is developed under the same developing circumstances by estimating the total software failures with respect to its size and time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5287-5303
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• 2018

Handling vast amount of data found in large transactional datasets is an obvious challenge for the conventional data mining algorithms. Addressing this challenge, our paper proposes a parallel approach for proper decomposition of mining problem into sub-problems in order to find frequent patterns from these datasets. The proposed, Pattern Mining for Large Distributed Dataset (PMLDD) approach, ensures minimum dependencies as well as minimum communications among sub-problems. It establishes a linear aggregation of the intermediate results so that it can be adapted to large-scale programming models like MapReduce. In this context, an algorithmic structure for MapReduce programming model is presented. PMLDD guarantees an efficient load balancing among the sub-problems by a specific selection criterion. Further, it optimizes the number of required iterations over the dataset for mining frequent patterns as compared to the existing approaches. Finally, we believe that our approach is scalable enough to handle larger datasets in terms of performance evaluation, and the result analysis justifies all these mentioned concerns.

• Wind and Structures
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• v.20 no.6
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• pp.793-810
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• 2015

Tests were conducted at the Florida International University (FIU) Wall of Wind (WOW) to investigate the susceptibility of Variable Message Signs (VMS) to wind induced vibrations due to vortex shedding and galloping instability. Large scale VMS models were tested in turbulence representative of the high frequency end of the spectrum in a simulated suburban atmospheric boundary layer. Data was measured for the $0^{\circ}$ and $45^{\circ}$ horizontal wind approach directions and vertical attack angles ranging from $-4.5^{\circ}$ to $+4.5^{\circ}$. Analysis of the power spectrum of the fluctuating lift indicated that vertical vortex oscillations could be significant for VMS with a large depth ratio attached to a structure with a low natural frequency. Analysis of the galloping test data indicated that VMS with large depth ratios, greater than about 0.5, and low natural frequency could also be subject to galloping instability.

• International Journal of Knowledge Content Development & Technology
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• v.3 no.1
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• pp.65-80
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• 2013

Today, citation index information is used as an outcome scale of spreading technology and encouraging research. Article citation information is an important factor to determine the authority of the relevant author. Google Scholar uses the article citation information to organize academic article search results with a rank algorithm. For an accurate analysis of such important citation index information, large amounts of bibliographic data are required. Therefore, this study aims to build a fast and efficient system for large amounts of bibliographic data, and to design and develop a system for quickly analyzing cited information for that data. This study also aims to use and analyze citation data to be a basic element for providing various advanced services to the academic article search system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.56-63
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• 2015

Several fields of science have demanded large-scale workflow support, which requires thousands of CPU cores or more. In order to support such large-scale scientific workflows, high capacity parallel systems such as supercomputers are widely used. In order to increase the utilization of these systems, most schedulers use backfilling policy: Small jobs are moved ahead to fill in holes in the schedule when large jobs do not delay. Since an estimate of the runtime is necessary for backfilling, most parallel systems use user's estimated runtime. However, it is found to be extremely inaccurate because users overestimate their jobs. Therefore, in this paper, we propose a novel system for the runtime prediction based on workload-aware clustering with the goal of improving prediction performance. The proposed method for runtime prediction of parallel applications consists of three main phases. First, a feature selection based on factor analysis is performed to identify important input features. Then, it performs a clustering analysis of history data based on self-organizing map which is followed by hierarchical clustering for finding the clustering boundaries from the weight vectors. Finally, prediction models are constructed using support vector regression with the clustered workload data. Multiple prediction models for each clustered data pattern can reduce the error rate compared with a single model for the whole data pattern. In the experiments, we use workload logs on parallel systems (i.e., iPSC, LANL-CM5, SDSC-Par95, SDSC-Par96, and CTC-SP2) to evaluate the effectiveness of our approach. Comparing with other techniques, experimental results show that the proposed method improves the accuracy up to 69.08%.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3874-3897
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• 2023

A high-fidelity computational fluid dynamics (CFD) analysis was performed using the Large Eddy Simulation (LES) model for the lower plenum of the High-Temperature Test Facility (HTTF), a ¼ scale test facility of the modular high temperature gas-cooled reactor (MHTGR) managed by Oregon State University. In most next-generation nuclear reactors, thermal stress due to thermal striping is one of the risks to be curiously considered. This is also true for HTGRs, especially since the exhaust helium gas temperature is high. In order to evaluate these risks and performance, organizations in the United States led by the OECD NEA are conducting a thermal hydraulic code benchmark for HTGR, and the test facility used for this benchmark is HTTF. HTTF can perform experiments in both normal and accident situations and provide high-quality experimental data. However, it is difficult to provide sufficient data for benchmarking through experiments, and there is a problem with the reliability of CFD analysis results based on Reynolds-averaged Navier-Stokes to analyze thermal hydraulic behavior without verification. To solve this problem, high-fidelity 3-D CFD analysis was performed using the LES model for HTTF. It was also verified that the LES model can properly simulate this jet mixing phenomenon via a unit cell test that provides experimental information. As a result of CFD analysis, the lower the dependency of the sub-grid scale model, the closer to the actual analysis result. In the case of unit cell test CFD analysis and HTTF CFD analysis, the volume-averaged sub-grid scale model dependency was calculated to be 13.0% and 9.16%, respectively. As a result of HTTF analysis, quantitative data of the fluid inside the HTTF lower plenum was provided in this paper. As a result of qualitative analysis, the temperature was highest at the center of the lower plenum, while the temperature fluctuation was highest near the edge of the lower plenum wall. The power spectral density of temperature was analyzed via fast Fourier transform (FFT) for specific points on the center and side of the lower plenum. FFT results did not reveal specific frequency-dominant temperature fluctuations in the center part. It was confirmed that the temperature power spectral density (PSD) at the top increased from the center to the wake. The vortex was visualized using the well-known scalar Q-criterion, and as a result, the closer to the outlet duct, the greater the influence of the mainstream, so that the inflow jet vortex was dissipated and mixed at the top of the lower plenum. Additionally, FFT analysis was performed on the support structure near the corner of the lower plenum with large temperature fluctuations, and as a result, it was confirmed that the temperature fluctuation of the flow did not have a significant effect near the corner wall. In addition, the vortices generated from the lower plenum to the outlet duct were identified in this paper. It is considered that the quantitative and qualitative results presented in this paper will serve as reference data for the benchmark.