• International journal of advanced smart convergence
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• v.8 no.1
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• pp.82-86
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• 2019

We aim to use public data different from the remote BEMS energy diagnostics technology and already established and then switch the conventional operation environment to a big-data-based integrated management environment to operate and build a building energy management environment of maximized efficiency. In Step 1, various network management environments of the system integrated with a big data platform and the BEMS management system are used to collect logs created in various types of data by means of the big data platform. In Step 2, the collected data are stored in the HDFS (Hadoop Distributed File System) to manage the data in real time about internal and external changes on the basis of integration analysis, for example, relations and interrelation for automatic efficient management.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.637-638
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• 2014

This paper proposes a new visualization method based on Vitualization technique which uses internal relationship of user correlation and external information of social network to visualize user relationship hierarchy. The proposed method use hadoop on virtual machine of OpenStack for distribution and parallel processing which the result of calculation visualizes hierarchy graph to analyze link nodes of Social Network Services for users.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.326-328
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• 2018

최근 4K/8K 급 초고품질 콘텐츠의 서비스에 관심이 집중되는 만큼 스트리밍 서비스에 대한 연구도 활발히 이루어지고 있다. 하지만 단일 PC 성능의 한계로 인해 SW 기반 영상 처리에 어려움을 겪고 있다. 본 논문에서는 분산 처리를 통해 실시간 영상 처리가 가능하도록 시스템을 제안한다. 제안한 시스템은 영상 패킷 분석 및 분할, 분산 트랜스코딩, 패킷 통합 단계로 이루어지며 Hadoop 과 Spark 를 이용하여 실시간 분산 처리를 지원한다. 실험 결과는 초고품질 입력 영상($3840{\times}2160@60Hz$, YCbCr 4:2:2, 10-bit)에 대해 평균 74.47fps 의 트랜스코딩 속도를 보인다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.56-58
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• 2013

최근 페이스북이나 트위터와 같은 소셜네트워크 서비스를 포함하여 대용량의 빅데이터에 대한 처리와 분석이 중요한 이슈로 다뤄지고 있으며, 사용자들이 끊임없이 쏟아내는 데이터로 인해서 이러한 데이터들을 어떻게 다룰 것인지, 혹은 어떻게 분석하여 의미 있고, 가치 있는 것으로 가공할 것인지가 중요한 사안으로 여겨지고 있다. 이러한 빅데이터 관리 도구로써 하둡은 빅데이터의 처리와 분석에 있어서 가장 해결에 근접한 도구로 평가받고 있다. 이 논문은 하둡의 주요 구성요소인 HDFS(Hadoop Distributed File System)와 JAVA에 기반하여 제작되는 온라인 대용량 저장소 시스템의 가장 기본적인 요소인 온라인 데이터 저장소를 직접 설계하고 제작하고, 구현하여 봄으로써 대용량 저장소의 구현 방식에 대한 이슈를 다뤄보도록 한다.

• Journal of Platform Technology
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• v.8 no.2
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• pp.3-9
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• 2020

Big Data is a new concept in the global and local area. This field has gained tremendous momentum in the recent years and has attracted attention of several researchers. Big Data is a data analysis methodology enabled by recent advances in information and communications technology. However, big data analysis requires a huge amount of computing resources making adoption costs of big data technology. Therefore, it is not affordable for many small and medium enterprises. We survey the concepts and characteristics of Big Data along with a number of tools like HADOOP, HPCC for managing Big Data. It also presents an overview of big data like Characteristics of Big data, big data technology, big data management tools etc. We have also highlighted on some challenges and opportunities related to the fields of big data.

• KIISE Transactions on Computing Practices
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• v.24 no.1
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• pp.24-32
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• 2018

This paper proposes a Big Data system for energy Big Data which is aggregated in real-time from industrial and public sources. The constructed Big Data system is based on Hadoop and the Spark framework is simultaneously applied on Big Data processing, which supports in-memory distributed computing. In the paper, we focus on Big Data, in the form of heat energy for district heating, and deal with methodologies for storing, managing, processing and analyzing aggregated Big Data in real-time while considering properties of energy input and output. At present, the Big Data influx is stored and managed in accordance with the designed relational database schema inside the system and the stored Big Data is processed and analyzed as to set objectives. The paper exemplifies a number of heat demand plants, concerned with district heating, as industrial sources of heat energy Big Data gathered in real-time as well as the proposed system.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.7
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• pp.791-797
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• 2014

In order to create an environment for Apache Hadoop for parallel distributed processing system of Bigdata, by connecting a plurality of computers, or to configure the node, using the configuration of the virtual nodes on a single computer it is necessary to build a cloud fading environment. However, be constructed in practice for education in these systems, there are many constraints in terms of cost and complex system configuration. Therefore, it is possible to be used as training for educational institutions and beginners in the field of Bigdata processing, development of learning systems and inexpensive practical is urgent. Based on the Raspberry Pi board, training and analysis of Big data processing, such as Hadoop and NoSQL is now the design and implementation of a learning system of parallel distributed processing of possible Bigdata in this study. It is expected that Bigdata parallel distributed processing system that has been implemented, and be a useful system for beginners who want to start a Bigdata and education.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.552-557
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• 2015

IT technology development and smart appliances due to the increased use of a lot of data on production and consumption has become in the internet. Because this is why importance of information retrieval technology although the growing becoming aware of the difficult techniques to access the required of lot a background knowledge on information retrieval technology. However, the Lucene due to emerge provide to background can implement on search engine by using the Lucene of lack background knowledge for search technology. In this paper, suggest to implement on search engine by using the developed a framework on Lucene-based. Suggest a frameworks are use in the search engines on have guarantee in server environment support on distributed processing and distributed storage, and high availability by using the Hadoop and Nutch, Solr, Zookeeper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2067-2075
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• 2017

Lately big data processing is considered as an emerging issue. As a huge amount of data is generated, data processing capability is getting important. In processing big data, both Hadoop distributed file system and unstructured date processing-based NoSQL data store are getting a lot of attention. However, there still exists problems and inconvenience to use NoSQL. In case of low volume data, MapReduce of NoSQL normally consumes unnecessary processing time and requires relatively much more data retrieval time than RDBMS. In order to address the NoSQL problem, in this paper, an interworking scheme between NoSQL and the conventional RDBMS is proposed. The developed auto-mapping scheme enables to choose an appropriate database (NoSQL or RDBMS) depending on the amount of data, which results in fast search time. The experimental results for a specific data set shows that the database interworking scheme reduces data searching time by 35% at the maximum.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.203-211
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• 2013

Due to the rapid growth of mobile devices and applications, mobile cloud computing is becoming an important platform in the development of cloud services. However, the mobile cloud computing is facing many challenges in terms of the computing resources and communications. One of them is the performance issue between mobile devices and cloud server. In the paper, we implemented a hadoop-based android mobile application, called mobile TPC-C, and used it for evaluating the performance aspect between mobile devices and cloud server. The mobile TPC-C was implemented based on the existing TPC-C, to make it possible to execute on top of android mobile devices. The performance measurement using mobile TPC-C was executed on various transactions while changing the number of mobile clients. By comparing it to the evaluation on the personal PC, we tried to point out the important aspects affecting the performance improvement between mobile clients and cloud server.