• Journal of Advanced Navigation Technology
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• v.18 no.5
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• pp.468-475
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• 2014

The recommend system is getting more difficult real time recommend by lager preference data set, computing power and recommend algorithm. For this reason, recommend system is proceeding actively one's studies toward distribute processing method of large preference data set. This paper studied distribute processing method of large preference data set using hadoop distribute processing platform and mahout machine learning library. The recommend algorithm is used Co-occurrence Matrix similar to item Collaborative Filtering. The Co-occurrence Matrix can do distribute processing by many node of hadoop cluster, and it needs many computation scale but can reduce computation scale by distribute processing. This paper has simplified distribute processing of co-occurrence matrix by changes over from four stage to three stage. As a result, this paper can reduce mapreduce job and can generate recommend file. And it has a fast processing speed, and reduce map output data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.11
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• pp.2827-2848
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• 2012

Previously, we described a social media cloud computing service environment (SMCCSE). This SMCCSE supports the development of social networking services (SNSs) that include audio, image, and video formats. A social media cloud computing PaaS platform, a core component in a SMCCSE, processes large amounts of social media in a parallel and distributed manner for supporting a reliable SNS. Here, we propose a Hadoop-based multimedia system for image and video transcoding processing, necessary functions of our PaaS platform. Our system consists of two modules, including an image transcoding module and a video transcoding module. We also design and implement the system by using a MapReduce framework running on a Hadoop Distributed File System (HDFS) and the media processing libraries Xuggler and JAI. In this way, our system exponentially reduces the encoding time for transcoding large amounts of image and video files into specific formats depending on user-requested options (such as resolution, bit rate, and frame rate). In order to evaluate system performance, we measure the total image and video transcoding time for image and video data sets, respectively, under various experimental conditions. In addition, we compare the video transcoding performance of our cloud-based approach with that of the traditional frame-level parallel processing-based approach. Based on experiments performed on a 28-node cluster, the proposed Hadoop-based multimedia transcoding system delivers excellent speed and quality.

• Journal of the Korean Society for information Management
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• v.19 no.3
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• pp.5-30
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• 2002

In this paper, classification feature is selected with focus of musical content, note sequences pattern, and measures similarity between note sequences followed by constructing clusters by similar note sequences, which is easier for users to search by showing the similar note sequences with the search result in the CBMR system. Experimental document was $\ulcorner$A Dictionary of Musical Themes$\lrcorner$, the index of theme bar focused on classical music and obtained kern-type file. Humdrum Toolkit version 1.0 was used as note sequences treat tool. The hierarchical clustering method is by stages focused on four-type similarity matrices by whether the note sequences segmentation or not and where the starting point is. For the measurement of the result, WACS standard is used in the case of being manual classification and in the case of the note sequences starling from any point in the note sequences, there is used common feature pattern distribution in the cluster obtained from the clustering result. According to the result, clustering with segmented feature unconnected with the starting point Is higher with distinct difference compared with clustering with non-segmented feature.

• Journal of the Korea Convergence Society
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• v.7 no.5
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• pp.1-6
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• 2016

Many studies have been carried out for the development of big data utilization and analysis technology recently. There is a tendency that government agencies and companies to introduce a Hadoop of a processing platform for analyzing big data is increasing gradually. Increased interest with respect to the processing and analysis of these big data collection technology of data has become a major issue in parallel to it. However, study of the collection technology as compared to the study of data analysis techniques, it is insignificant situation. Therefore, in this paper, to build on the Hadoop cluster is a big data analysis platform, through the Apache sqoop, stylized from relational databases, to collect the data. In addition, to provide a sensor through the Apache flume, a system to collect on the basis of the data file of the Web application, the non-structured data such as log files to stream. The collection of data through these convergence would be able to utilize as a basic material of big data analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.204-226
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• 2018

Big data processing applications have been migrated into cloud gradually, due to the advantages of cloud computing. Hadoop Distributed File System (HDFS) is one of the fundamental support systems for big data processing on MapReduce-like frameworks, such as Hadoop and Spark. Since HDFS is not aware of the co-location of virtual machines in the cloud, the default scheme of block allocation in HDFS does not fit well in the cloud environments behaving in two aspects: data reliability loss and performance degradation. In this paper, we present a novel location-aware data block allocation strategy (LDBAS). LDBAS jointly optimizes data reliability and performance for upper-layer applications by allocating data blocks according to the locations and different processing capacities of virtual nodes in the cloud. We apply LDBAS to two stages of data allocation of HDFS in the cloud (the initial data allocation and data recovery), and design the corresponding algorithms. Finally, we implement LDBAS into an actual Hadoop cluster and evaluate the performance with the benchmark suite BigDataBench. The experimental results show that LDBAS can guarantee the designed data reliability while reducing the job execution time of the I/O-intensive applications in Hadoop by 8.9% on average and up to 11.2% compared with the original Hadoop in the cloud.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.189-198
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• 2009

Unstructured peer-to-peer systems are most commonly used in today's internet. But file placement is random in these systems and no correlation exists between peers and their contents. There is no guarantee that flooding queries will find the desired data. In this paper, we propose to cluster nodes in unstructured P2P systems using the agglomerative hierarchical clustering algorithm to improve the search method. We compared the delay time of clustering the nodes between our proposed algorithm and the k-means clustering algorithm. We also simulated the delay time of locating data in a network topology and recorded the overhead of the system using our proposed algorithm, k-means clustering, and without clustering. Simulation results show that the delay time of our proposed algorithm is shorter compared to other methods and resource overhead is also reduced.

• Korea Journal of Hospital Management
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• v.4 no.2
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• pp.85-105
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• 1999

The main purpose of this article is to analyze the managerial performance of small and medium-size hospitals that are specialized in certain areas of medical services. Data of 189 hospitals were obtained from the data file of Korea Institute of Health Services Management The items include general characteristics of the hospitals, fianancial reports, and utilization records. Degree of specialization is measured by concentration(Herfindahl) index, and the sample hospitals are accordingly classified into specialized and unspecialized groups, by means of cluster analysis. These groups are compared in terms of various measures of managerial performance, which include several profitability indices such as operating margin, return on assets(ROA), and return on equity(ROE). To examine the relationship between specialization and managerial performance, we estimate the regression model, where the profitability indices are used as the dependent variables and the concentration index as the independent variable, controlling for the hospital characteristics such as size, type and location. Also, we perform 'Du Pont' analysis, to investigate the basic elements that can explain the differences in profitability between specialized and unspecialized hospitals. Major findings are as follows: 1. Managerial performance is better for the specilized hospitals than the unspecilized, in all aspects of profitability(operating margin, ROA, ROE). 2. Regression analysis suggests that there is a positive, statistically significant relationship beween the degree of specialization(i.e. concentration) and hospital profitability. 3. Main reason for the higher profitability of specialized hospitals lies in lower expenses rather than higher revenue. 4. In particular, personnel and material expenses are significantly smaller for the specialized hospitals, and this result seems to stem from the efficiency of operating fewer lines of business.(some kind of 'economies of scale') 5. Specialized hospitals also have fewer employees compared with the unspecialized, especially in administrative departments, which implies their efficient personnel management.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.989-1009
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• 2018

Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed 'big data'. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.

• KIISE Transactions on Computing Practices
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• v.21 no.10
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• pp.651-657
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• 2015

The amount of web traffic has increased as a result of the rapid growth of the use of web-based applications. In order to obtain valuable information from web logs, we need to develop systems that can support interactive, flexible, and efficient ways to analyze and handle large amounts of data. In this paper, we present CERES, a log-based, interactive web analytics system for backbone networks. Since CERES focuses on analyzing web log records generated from backbone networks, it is possible to perform a web analysis from the perspective of a network. CERES is designed for deployment in a server cluster using the Hadoop Distributed File System (HDFS) as the underlying storage. We transform and store web log records from backbone networks into relations and then allow users to use a SQL-like language to analyze web log records in a flexible and interactive manner. In particular, we use the data cube technique to enable the efficient statistical analysis of web log. The system provides users a web-based, multi-modal user interface.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.10
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• pp.231-238
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• 2019

Analyzing next-generation genome sequencing data in a conventional way using single server may take several tens of hours depending on the data size. However, in order to cope with emergency situations where the results need to be known within a few hours, it is required to improve the performance of a single genome analysis. In this paper, we propose a parallelized method for pre-processing genome sequence data which can reduce the analysis time by utilizing the big data technology and the highperformance computing cluster which is connected to the high-speed network and shares the parallel file system. For the reliability of analytical data, we have chosen a strategy to parallelize the existing analytical tools and algorithms to the new environment. Parallelized processing, data distribution, and parallel merging techniques have been developed and performance improvements have been confirmed through experiments.