• 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.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.705-711
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• 2000

This paper presents a fast 3D mesh generation method using a surface based method with a stitching algorithm. This method uses the surface based method since the volume based method that uses 3D Delaunay triangulation can hardly deal with a large scale of scanned points. To reduce the processing time, this method also uses a stitching algorithm: after dividing the whole point data into several sections and performing mesh generation on individual sections, the meshes from several sections are stitched into one mesh. Stitching method prevents the surface based method from increasing the processing time exponentially as the number of the points increases. This method works well with different types of scanned points: a scattered type points from a conventional 3D scanner and a cross-sectional type from CT or MRI.

• 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.

• Journal of Internet of Things and Convergence
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• v.1 no.1
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• pp.11-19
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• 2015

The mobile and ubiquitous environment is experiencing a rapid development of information and communications technology as it provides an ever increasing flow of information. Particularly, GIS is now widely applied in daily life due to its high accuracy and functionality. GIS information is utilized through the tiling method, which divides and manages large-scale map information. The tiling method manages map information and additional information to allow overlay, so as to facilitate quick access to tiled data. Unlike past studies, this paper proposes a new architecture and algorithms for tile searching and indexing management to optimize map information and additional information for GIS mobile applications. Since this involves the processing of large-scale information and continuous information changes, information is clustered for rapid processing. In addition, data size is minimized to overcome the constrained performance associated with mobile devices. Our system has been implemented in actual services, leading to a twofold increase in performance in terms of processing speed and mobile bandwidth.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.942-948
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• 2009

Today's construction is a large-scale and long life span program, so called Mega-scale project, that every moment constructor faces much of hardships, It is because of a large amount of stakeholders, data and complicated relationship among workers. In order to overcome these problems, IETM(Interactive Electronic Technical Manual) has been introduced to construction industry recently, It is regarded as a useful tool for handling the data, procedures of the construction, but it is similar to existing IT-based information systems, the PMIS(Project Management Information System) and the KMS(Knowledge Management System), without characterizing. This research is intended to find out IETM's property and to present the Ontology scheme discriminating Construction IETM from existing systems..

• Journal of KIISE
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• v.43 no.6
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• pp.613-620
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• 2016

This paper presents a MOHA(Many-Task Computing on Hadoop) framework which aims to effectively apply the Many-Task Computing(MTC) technologies originally developed for high-performance processing of many tasks, to the existing Big Data processing platform Hadoop. We present basic concepts, motivation, preliminary results of PoC based on distributed message queue, and future research directions of MOHA. MTC applications may have relatively low I/O requirements per task. However, a very large number of tasks should be efficiently processed with potentially heavy inter-communications based on files. Therefore, MTC applications can show another pattern of data-intensive workloads compared to existing Hadoop applications, typically based on relatively large data block sizes. Through an effective convergence of MTC and Big Data technologies, we can introduce a new MOHA framework which can support the large-scale scientific applications along with the Hadoop ecosystem, which is evolving into a multi-application platform.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1113-1119
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• 2000

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.

• Atmosphere
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• v.32 no.1
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• pp.1-15
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• 2022

Recently, Japan's Meteorological Research Institute presented the d4PDF database (Database for Policy Decision-Making for Future Climate Change, d4PDF) through large-scale climate ensemble simulations to overcome uncertainty arising from variability when the general circulation model represents extreme-scale precipitation. In this study, the change of precipitation characteristics between the historical and future climate conditions in the Yongdam-dam basin was analyzed using the d4PDF data. The result shows that annual mean precipitation and seasonal mean precipitation increased by more than 10% in future climate conditions. This study also performed an analysis on the change of the return period rainfall. The annual maximum daily rainfall was extracted for each climatic condition, and the rainfall with each return period was estimated. In this process, we represent the extreme-scale rainfall corresponding to a very long return period without any statistical model and method as the d4PDF provides rainfall data during 3,000 years for historical climate conditions and during 5,400 years for future climate conditions. The rainfall with a 50-year return period under future climate conditions exceeded the rainfall with a 100-year return period under historical climate conditions. Consequently, in future climate conditions, the magnitude of rainfall increased at the same return period and, the return period decreased at the same magnitude of rainfall. In this study, by using the d4PDF data, it was possible to analyze the change in extreme magnitude of rainfall.

• 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.

• The KIPS Transactions:PartB
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• v.12B no.2 s.98
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• pp.209-214
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• 2005

The use of protein interaction data is highly reliable for predicting functions to proteins without function in proteomics study. The computational studies on protein function prediction are mostly based on the concept of guilt-by-association and utilize large-scale interaction map from revealed protein-protein interaction data. This study compares graph-based approaches such as neighbor-counting and $\chi^2-statistics$ methods using protein-protein interaction data and proposes an approach that is effective in analyzing large-scale protein interaction data. The proposed approach is also based protein interaction map but sequence similarity and heuristic knowledge to make prediction results more reliable. The test result of the proposed approach is given for KDD Cup 2001 competition data along with those of neighbor-counting and $\chi^2-statistics$ methods.