• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.276-279
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• 2015

Along with the increase of the quantity of data in various industries, the construction industry has also developed various systems focusing on collecting data related to the construction performance such as productivity and costs achieved in construction job sites. Numerous researchers worldwide have been focusing on developing efficient methodologies to analyze such data. However, applications of such methodologies have shown serious limitations on practical applications due to lack of data and difficulty in finding appropriate analytic methodologies which were capable of implementing significant insights. With development of information technology, the new trend in analytic methodologies has been introduced and steeply developed with the new name of "big data analysis" in various fields in academia and industry. The new concept of big data can be applied for significant analysis on various formats of construction data such as structured, semi-structured, or non-structured formats. This study investigates preliminary application methods based on data collected from actual construction site. This preliminary investigation in this study expects to assess fundamental feasibility of big data analytic applications in construction.

• 한국정보통신학회논문지
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• 제21권11호
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• pp.2176-2183
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• 2017

4차 산업혁명의 주요 요소가 되는 빅데이터 가치는 민간부분에서 산업 생산성을 높이고, 공공부분에서 대국민 및 기업에 대한 행정 서비스를 제공해 줄 수 있는 부분이기도 하다. ICT 선진국들은 공공부분의 빅데이터 활용 방안을 빠르게 제시하고 있다. 특히 사회 위기관리 차원에 있어 재난의 사전 예측시스템을 잘 갖추고 있다. 우리나라 정부의 입장에서도 사회 위기관리 차원의 빅데이터 공공 활용 방안에 많은 관심을 기울이고 있다. 하지만 빅데이터의 전반적인 인프라 부분에 취약성을 드러내고 있는 현실은 앞으로 사회현안 문제해결 차원의 준비와 실천이 요구되는 사항이다. 따라서 우리는 빅데이터 활용 현상의 문제를 분석하고, 각국의 선도적 빅데이터 공공 활용이 선행되는 사례를 검토해 앞으로 나아가야 할 정책의 다양성을 제시하여야 한다. 이에 본 논문은 빅데이터 활용에 있어 나타나고 있는 문제점을 분석하여 전자정부의 역할과 정책을 제언하였다. 제시한 정책 사항은 정보개방과 법 제도 개선의 문제, 빅데이터 환경에서의 개인정보 침해 위협을 관리하는 빅데이터 서비스 고려 사항 문제, 기술적 측면에서 공공의 빅데이터 활용 관련 기술개발 및 빅데이터 운영 분석 기술개발 필요성 문제 등을 제시하였다.

• 식품보건융합연구
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• 제10권1호
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• pp.1-6
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• 2024

Purpose: The research aims to address the intricacies of AI and Big Data application within the food industry. This study explores the strategic implementation of AI and Big Data in the food industry. The study seeks to understand how these technologies can be employed to bolster consumer engagement and contribute to market expansion, while considering ethical implications. Research Method: This research employs a comprehensive approach, analyzing current trends, case studies, and existing academic literature. It focuses on the application of AI and Big Data in areas such as supply chain management, consumer behavior analysis, and personalized marketing strategies. Results: The study finds that AI and Big Data significantly enhance market analytics, consumer personalization, and market trend prediction. It highlights the potential of these technologies in creating more efficient supply chains, improving consumer satisfaction through personalization, and providing valuable market insights. Conclusion and Implications: The paper offers actionable insights and recommendations for the effective implementation of AI and Big Data strategies in the food industry. It emphasizes the need for ethical considerations, particularly in data privacy and the transparency of AI algorithms. The study also explores future trends, suggesting that AI and Big Data will continue to revolutionize the industry, emphasizing sustainability, efficiency, and consumer-centric practices.

• 산업경영시스템학회지
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• 제42권4호
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• pp.84-90
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• 2019

Lately, there have been tremendous shifts in the business technology landscape. Advances in cloud technology and mobile applications have enabled businesses and IT users to interact in entirely new ways. One of the most rapidly growing technologies in this sphere is business intelligence, and associated concepts such as big data and data mining. BI is the collection of systems and products that have been implemented in various business practices, but not the information derived from the systems and products. On the other hand, big data has come to mean various things to different people. When comparing big data vs business intelligence, some people use the term big data when referring to the size of data, while others use the term in reference to specific approaches to analytics. As the volume of data grows, businesses will also ask more questions to better understand the data analytics process. As a result, the analysis team will have to keep up with the rising demands on the infrastructure that supports analytics applications brought by these additional requirements. It's also a good way to ascertain if we have built a valuable analysis system. Thus, Business Intelligence and Big Data technology can be adapted to the business' changing requirements, if they prove to be highly valuable to business environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.2977-2997
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• 2018

In big data era, fresh data grows rapidly every day. More than 30,000 gigabytes of data are created every second and the rate is accelerating. Many organizations rely heavily on real time streaming, while big data stream computing helps them spot opportunities and risks from real time big data. Storm, one of the most common online stream computing platforms, has been used for big data stream computing, with response time ranging from milliseconds to sub-seconds. The performance of Storm plays a crucial role in different application scenarios, however, few studies were conducted to evaluate the performance of Storm. In this paper, we investigate the performance of Storm under different application scenarios. Our experimental results show that throughput and latency of Storm are greatly affected by the number of instances of each vertex in task topology, and the number of available resources in data center. The fault-tolerant mechanism of Storm works well in most big data stream computing environments. As a result, it is suggested that a dynamic topology, an elastic scheduling framework, and a memory based fault-tolerant mechanism are necessary for providing high throughput and low latency services on Storm platform.

• Journal of the Korean Data and Information Science Society
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• 제24권5호
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• pp.959-974
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• 2013

빅데이터 시대를 맞이하여 통계학과 통계학자의 역할에 대하여 살펴본다. 빅데이터에 대한 정의 및 응용분야를 살펴보고, 빅데이터 자료의 통계학적 특징들 및 이와 관련한 통계학적 의의에 대해서 설명한다. 빅데이터 자료 분석에 유용하게 사용되는 통계적 방법론들에 대해서 살펴보고, 국외와 국내의 빅데이터 관련 프로젝트를 소개한다.

• 대한공간정보학회지
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• 제24권4호
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• pp.89-96
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• 2016

본 연구에서는 빅데이터와 공간 빅데이터 선행연구들을 기반으로 공간 빅데이터를 빅데이터를 구성하는 하나의 구성요소로 인식하고, 위치정보를 이용하여 공간화 할 수 있으며, 시계열 변화에 따라 계속적으로 누적되는 모든 데이터들과 이를 이용할 수 있는 활용체계를 공간 빅데이터라 정의하였다. 따라서 공간 빅데이터는 기존 빅데이터와 분리하여 구분할 것이 아니라, 기존 빅데이터를 구성하는 하나의 구성요소로서 이해하고, 이러한 활용체계 안에서 공간 빅데이터의 활용방안을 검토해야 한다. 본 연구에서는 공간 빅데이터가 제공해야 하는 서비스 요구사항들을 제시하였다. 공간정보를 포함한 공간 빅데이터는 기본적으로 다양한 공간분석이 가능해야 하고, 기존에 구축된 공간정보와 향후 구축될 공간정보까지 고려할 수 있는 서비스 고려가 필요하다. 시간의 흐름에 따른 위치별 시계열 변화의 탐지는 물론 공간정보의 속성정보들을 이용하여 다양한 빅데이터 관련 분석이 가능해야 한다. 공간정보가 아닌 빅데이터 또한 공간정보와 연계하여 공간 분석이 가능해야 한다. 이러한 공간 빅데이터 요구사항들을 만족시키기 위해 다양한 형태의 빅데이터들과 공간 빅데이터의 연계가 가능한 분석 서비스 제공을 위한 샘플링 포인트 생성 및 속성정보 추출 방안을 제시하였다. 이러한 빅데이터와 연계된 공간정보의 활용 증대는 공간정보 산업 및 기술발전에 크게 기여할 수 있을 것이라 판단된다.

• International Journal of Contents
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• 제17권3호
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• pp.74-83
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• 2021

A location-aware algorithm is proposed in this study to optimize the system performance of distributed systems for processing big data with low data reliability and application performance. Compared with previous algorithms, the location-aware data block placement algorithm uses data block placement and node data recovery strategies to improve data application performance and reliability. Simulation and actual cluster tests showed that the location-aware placement algorithm proposed in this study could greatly improve data reliability and shorten the application processing time of I/O interfaces in real-time.

• 한국멀티미디어학회논문지
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• 제22권2호
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• pp.194-202
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• 2019

In the information society represented by the Fourth Industrial Revolution, various types of data and information that are difficult to see are produced, processed, and processed and circulated to enhance the value of existing goods. The IoT(Internet of Things) paradigm will change the appearance of individual life, industry, disaster, safety and public service fields. In order to implement the IoT paradigm, several elements of technology are required. It is necessary that these various elements are efficiently connected to constitute one system as a whole. It is also necessary to collect, provide, transmit, store and analyze IoT data for implementation of IoT platform. We designed and implemented a big data processing IoT platform for IoT service implementation. Proposed platform system is consist of IoT sensing/control device, IoT message protocol, unstructured data server and big data analysis components. For platform testing, fixed IoT devices were implemented as solar power generation modules and mobile IoT devices as modules for table tennis stroke data measurement. The transmission part uses the HTTP and the CoAP, which are based on the Internet. The data server is composed of Hadoop and the big data is analyzed using R. Through the emprical test using fixed and mobile IoT devices we confirmed that proposed IoT platform system normally process and operate big data.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2014년도 제49차 동계학술대회논문집 22권1호
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• pp.433-434
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• 2014

Big Data services is composed of Big Data user, Big Data service provider, and Big Data application provider. And it is possible to extend the service to interplay-reciprocal actions among three subjects such as providing, being provided, connecting, being connected, and so on. In this paper, we propose an ecosystems of Big Data and a framework of its service.