• Journal of Digital Convergence
• /
• v.12 no.11
• /
• pp.233-239
• /
• 2014

Ultra high-resolution content, the file size is very large, therefore existing encoding techniques, it is not possible to transmit via the network. Efficient use of the network encoder HEVC corporation can be transferred. Compression requires a lot of time because it requires a distributed transcoding system. Distributed transcoding system is a distributed data store, and then encoded using a large number of nodes. The disadvantage of distributed transcoding system for distributed information is exposed or vulnerable to attack by internal managers. In this paper, when the super high definition content transcoding, distributed transcoding system does not guarantee the confidentiality of the problem to solve. We are using SNA, HEVC encoded content data encrypted using the secret distributing scheme was. Consequently, secure shared transcoding is possible, the internal administrator could prevent the attack.

• Journal of Digital Convergence
• /
• v.13 no.9
• /
• pp.129-136
• /
• 2015

We investigate the underlying structure of big data research domain, which is diversified and complicated using bottom-up approach. For that purpose, we derive a set of articles by searching "big data" through the Korea Citation Index System provided by National Research Foundation of Korea. With some preprocessing on the author-provided keywords, we analyze bibliometric data such as author-provided keywords, publication year, author, and journal characteristics. From the analysis, we both identify major sub-domains of big data research area and discover the hidden issues which made big data complex. Major keywords identified include SOCIAL NETWORK ANALYSIS, HADOOP, MAPREDUCE, PERSONAL INFORMATION POLICY/PROTECTION/PRIVATE INFORMATION, CLOUD COMPUTING, VISUALIZATION, and DATA MINING. We finally suggest missing research themes to make big data a sustainable management innovation and convergence medium.

• Journal of Convergence for Information Technology
• /
• v.7 no.5
• /
• pp.177-182
• /
• 2017

As major ICT technologies such as IoT, cloud computing, and Big Data are being applied to manufacturing, smart factories are beginning to be built. The key of smart factory implementation is the ability to acquire and analyze data of the factory. Therefore, the need for a big data analysis platform is increasing. The purpose of this study is to construct a platform for big data analysis of manufacturing process and propose integrated method for analysis. The proposed platform is a RHadoop-based structure that integrates analysis tool R and Hadoop to distribute a large amount of datasets. It can store and analyze big data collected in the unit process and factory in the automation system directly in HBase, and it has overcome the limitations of RDB - based analysis. Such a platform should be developed in consideration of the unit process suitability for smart factories, and it is expected to be a guide to building IoT platforms for SMEs that intend to introduce smart factories into the manufacturing process.

• The Journal of the Korea Contents Association
• /
• v.13 no.9
• /
• pp.20-27
• /
• 2013

In recently years, with the growth of social media and the development of mobile devices, the data have been significantly increased. MapReduce is an emerging programming model that processes large amount of data. However, since MapReduce evenly places the data in the dedicated distributed computing environment, it is not suitable to the non-dedicated distributed computing environment. The data replacement algorithms were proposed for performance optimization of MapReduce in the non-dedicated distributed computing environments. However, they spend much time for date replacement and cause the network load for unnecessary data transmission. In this paper, we propose an efficient data replacement algorithm for the performance optimization of MapReduce in the non-dedicated distributed computing environments. The proposed scheme computes the ratio of data blocks in the nodes based on the node availability model and reduces the network load by transmitting the data blocks considering the data placement. Our experimental results show that the proposed scheme outperforms the existing scheme.

• Journal of KIISE
• /
• v.43 no.1
• /
• pp.61-70
• /
• 2016

In recent years, there has been a need for techniques for large-scale ontology inference in order to infer new knowledge from existing knowledge at a high speed, and for a diversity of semantic services. With the recent advances in distributed computing, developments of ontology inference engines have mostly been studied based on Hadoop or Spark frameworks on large clusters. Parallel programming techniques using GPGPU, which utilizes many cores when compared with CPU, is also used for ontology inference. In this paper, by combining the advantages of both techniques, we propose a new method for reasoning large RDFS ontology data using a Spark in-memory framework and inferencing distributed data at a high speed using GPGPU. Using GPGPU, ontology reasoning over high-capacity data can be performed as a low cost with higher efficiency over conventional inference methods. In addition, we show that GPGPU can reduce the data workload on each node through the Spark cluster. In order to evaluate our approach, we used LUBM ranging from 10 to 120. Our experimental results showed that our proposed reasoning engine performs 7 times faster than a conventional approach which uses a Spark in-memory inference engine.

• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.10
• /
• pp.487-492
• /
• 2017

Clustering is a technique which is used to measure similarities between data in big data analysis and data mining field. Among various clustering methods, k-Modes algorithm is representatively used for categorical data. To increase the performance of iterative-centric tasks such as k-Modes, a distributed and concurrent framework Spark has been received great attention recently because it overcomes the limitation of Hadoop. Spark provides an environment that can process large amount of data in main memory using the concept of abstract objects called RDD. Spark provides Mllib, a dedicated library for machine learning, but Mllib only includes k-means that can process only continuous data, so there is a limitation that categorical data processing is impossible. In this paper, we design RDD for k-Modes algorithm for categorical data clustering in spark environment and implement an algorithm that can operate effectively. Experiments show that the proposed algorithm increases linearly in the spark environment.

• Smart Media Journal
• /
• v.8 no.3
• /
• pp.88-94
• /
• 2019

Livestock disease is a very important issue in the livestock industry because if livestock disease is not responded quickly enough, its damage can be devastating. To solve the issues involving the occurrence of livestock disease, it is necessary to diagnose in advance the status of livestock disease and develop systematic and scientific livestock feeding technologies. However, there is a lack of domestic studies on such technologies in Korea. This paper, therefore, proposes Livestock Disease Forecasting and Livestock Farm Integrated Control System using Cloud Computing to quickly manage livestock disease. The proposed system collects a variety of livestock data from wireless sensor networks and application. Moreover, it saves and manages the data with the use of the column-oriented database Hadoop HBase, a column-oriented database management system. This provides livestock disease forecasting and livestock farm integrated controlling service through MapReduce Model-based parallel data processing. Lastly, it also provides REST-based web service so that users can receive the service on various platforms, such as PCs or mobile devices.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.14 no.4
• /
• pp.300-306
• /
• 2021

Due to the development of IT technology, hardware technologies applied to IoT equipment have recently been developed, so smart systems using low-cost, high-performance RF and computing devices are being developed. However, in the infrastructure environment where a large amount of IoT devices are installed, big data collection causes a load on the collection server due to a bottleneck between the transmitted data. As a result, data transmitted to the data collection server causes packet loss and reduced data throughput. Therefore, there is a need for an efficient big data collection technique in an infrastructure environment where a large amount of IoT devices are installed. Therefore, in this paper, we propose an efficient big data collection technique in an infrastructure environment where a vast amount of IoT devices are installed. As a result of the performance evaluation, the packet loss and data throughput of the proposed technique are completed without loss of the transmitted file. In the future, the system needs to be implemented based on this design.

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

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.12 no.1
• /
• pp.440-454
• /
• 2020

To prevent pollution from ships, the Energy Efficiency Design Index (EEDI) is a mandatory guideline for all new ships. The Ship Energy Efficiency Management Plan (SEEMP) has also been applied by MARPOL to all existing ships. SEEMP provides the Energy Efficiency Operational Indicator (EEOI) for monitoring the operational efficiency of a ship. By monitoring the EEOI, the shipowner or operator can establish strategic plans, such as routing, hull cleaning, decommissioning, new building, etc. The key parameter in calculating EEOI is Fuel Oil Consumption (FOC). It can be measured on board while a ship is operating. This means that only the shipowner or operator can calculate the EEOI of their own ships. If the EEOI can be calculated without the actual FOC, however, then the other stakeholders, such as the shipbuilding company and Class, or others who don't have the measured FOC, can check how efficiently their ships are operating compared to other ships. In this study, we propose a method to estimate the EEOI without requiring the actual FOC. The Automatic Identification System (AIS) data, ship static data, and environment data that can be publicly obtained are used to calculate the EEOI. Since the public data are of large capacity, big data technologies, specifically Hadoop and Spark, are used. We verify the proposed method using actual data, and the result shows that the proposed method can estimate EEOI from public data without actual FOC.