• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.291-292
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• 2022

최근 4차 산업혁명이 대두됨에 따라 빅데이터 시장의 지속적인 성장과 다양한 데이터 시각화 플랫폼이 개발되고 있다. 해양 산업에서도 선박, 다이버, 기상 API 등 다양한 해양 데이터를 통해 꾸준한 연구가 이루어지고 있으며 본 연구에서는 해양 데이터를 기반으로 데이터 분석 및 시각화를 통해 사용자에게 정보를 제공하는 플랫폼을 제시하고, 기하급수적으로 늘어날 빅데이터를 효과적으로 분석하기 위해 데이터 분석 및 시각화 기법 연구의 필요성을 제시하였음.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.100-112
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• 2021

With the recent growth of the big data technology market, interest in visualization technology has steadily increased over the past few years. Data visualization is currently used in a wide range of disciplines such as information science, computer science, human-computer interaction, statistics, data mining, cartography, and journalism, each with a slightly different meaning. Big data visualization in smart cities that require multidisciplinary research enables an objective and scientific approach to developing user-centered smart city services and related policies. In particular, spatial-based data visualization enables efficient collaboration of various stakeholders through visualization data in the process of establishing city policy. In this paper, a user-centered spatial big data visualization expression request method was derived by examining the spatial-based big data visualization expression process and principle from the viewpoint of effective information delivery, not just a visualization tool.

• Spatial Information Research
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• v.23 no.6
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• pp.109-116
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• 2015

In this study, define the concept of spatial big data and special feature of spatial big data, examine information visualization methodology for increase the insight into the data. Also presented problems and solutions in the visualization process. Spatial big data is defined as a result of quantitative expansion from spatial information and qualitative expansion from big data. Characteristics of spatial big data id defined as 6V (Volume, Variety, Velocity, Value, Veracity, Visualization), As the utilization and service aspects of spatial big data at issue, visualization of spatial big data has received attention for provide insight into the spatial big data to improve the data value. Methods of information visualization is organized in a variety of ways through Matthias, Ben, information design textbook, etc, but visualization of the spatial big data will go through the process of organizing data in the target because of the vast amounts of raw data, need to extract information from data for want delivered to user. The extracted information is used efficient visual representation of the characteristic, The large amounts of data representing visually can not provide accurate information to user, need to data reduction methods such as filtering, sampling, data binning, clustering.

• The Journal of Bigdata
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• v.5 no.1
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• pp.147-154
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• 2020

Today, research is actively being conducted to derive meaningful results from big data. In this paper, we propose a partition-based big data analysis algorithm that can analyze the correlation between variables by setting the data areas of big data as partitions and calculating the representative values of each partition. In this paper, the analyzed visualization results are compared according to the partition size of a proposed partition-based big data analysis (PBDA) algorithm that can control the size of the partition. In order to verify the proposed PBDA algorithm, the big data of 'A' is analyzed, and meaningful results are obtained through the analysis of changes in sales volume of products according to changes in temperature and sales price.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.290-293
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• 2017

최근 IoT 기술발달로 인한 스마트폰 및 대용량 미디어기기 사용증가로 인터넷 네트워크 사용량이 폭발적으로 증가되고 있고, 이러한 데이터 사용량 급증으로 대량의 데이터를 지칭하는 빅데이터 수집 및 분석에 많은 기업과 정부가 주목하고 있다. 빅데이터는 기존에 없던 새로운 데이터의 구축이 아니며, 그동안 축적된 다방면의 방대한 데이터의 집합이라 할 수 있다. 빅데이터의 이용 및 분석에 대한 기업 정부 학계의 수요는 증가하고 있지만, 고난도의 빅데이터 분석을 위한 인프라 구축이 선결과제이어서, 이러한 인프라구축 비용 때문에 빅데이터 분석이 일선 산업분야에 바로 적용하는데 많은 장애요인이 되어 데이터 분석가들의 빅데이터 분석에 애로사항으로 존재하고 있다. 이러한 어려움을 해소하기 위한 방안으로 새로운 인프라 구축 없이 Google Analytics API를 연동한 R 프로그래밍의 데이터 시각화를 활용한 데이터 분석 방안을 제시하고자 한다. 본 연구에서는 구글 애널리틱스 API를 연동하여 사용자 웹사이트의 사용자접속, 사이트운영, 이벤트 발생 등의 데이터를 R 프로그램을 활용하여 사이트 현황을 데이터 시각화로 분석하고 운영중인 웹사이트에 적용 가능한 콘텐츠 개발 방안에 대해 연구하였다.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.763-770
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• 2024

Currently, land-based fish farms utilizing seawater have introduced and are utilizing various equipment such as real-time water quality monitoring systems, facility automation systems, and automated dissolved oxygen supply devices. Furthermore, data collected from various equipment in these fish farms produce structured and unstructured big data related to water quality environment, facility operations, and workplace visual information. The big data generated in the operational environment of fish farms aims to improve operational and production efficiency through the development and application of various methods. This study aims to develop a system for effectively analyzing and visualizing big data produced from land-based fish farms. It proposes a data visualization process suitable for use in a fish farm big data analysis system, develops big data visualization tools, and compares the results. Additionally, it presents intuitive visualization models for exploring and comparing big data with time-series characteristics.

• The Journal of the Korea Contents Association
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• v.20 no.1
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• pp.142-151
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• 2020

The reason why today's public actively uses social communication is that the necessary information is collected and classified under the name of social big data through the web space to create the big data era, an ecosystem of information. In order for big data information to be used by the public, it is necessary to visualize it easily. This study categorized the types of visualization according to the information of social big data, and targeted the experienced students including the related majors and the general public who need to directly utilize and study the actual big data visualization as an experience evaluation target. As a result of analyzing the experiences of the experienced people, important implications for the visualization method for managing, analyzing, and utilizing the data were derived. The big data visualization strategy is to be expressed in a way that fits the data environment and user's eye level on SNS. In the future, if big data visualization is applied to product service or social trend, it will be an important data in terms of broadening its role, scope of application, and application.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1301-1311
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• 2017

Manufacturing big data systems have supported decision making that can improve preemptive manufacturing activities through collection, storage, management, and predictive analysis of related 4M data in pre-manufacturing processes. Effective visualization of data is crucial for efficient management and operation of data in these systems. This paper presents visualization techniques that can be used to effectively show data collection, analysis, and prediction results in the manufacturing big data systems. Through the visualization technique presented in this paper, we have confirmed that it was not only easy to identify the problems that occurred at the manufacturing site, but also it was very useful to reply to these problems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.12
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• pp.2305-2311
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• 2017

In recent years, there have been tries to open public data from various government agencies along with publicization of public information for the public interest. In other words, various kinds of electronic data generated and collected by the public institutions as a result of their work are opened in the public portal sites. However, users who use it are limited in their use of big data due to lack of understanding of data format, lack of data processing knowledge, difficulty in accessing and managing data, and lack of visualization data to understand collected and stored data. Therefore, in this study, we propose a big data collection, storing and visualization platform that can collect big data provided by various public sites using data set URL and API regardless of data format, re-process collected data using XML structure.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.57-70
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• 2018

The advancement and the spread of information and communication technology (ICT) changes the way we live and act. Computer and ICT devices become smaller and invisible, and they are now virtually everywhere in the world. Many socio-economic activities are now subject to the use of computer and ICT devices although we don't really recognize it. Various socio economic activities supported by digital devices leave digital records, and a myriad of these records becomes what we call'big data'. Big data differ from conventional data we have collected and managed in that it holds more detailed information of socio-economic activities. Thus, they offer not only new insight for our society and but also new opportunity for policy analysis. However, the use of big data requires development of new methods and tools as well as consideration of institutional issues such as privacy. The goals of this research are twofold. Firstly, it aims to understand the opportunities and challenges of using big data for planning support. Big data indeed is a big sum of microscopic and dynamic data, and this challenges conventional analytical methods and planning support tools. Secondly, it seeks to suggest ways of visualizing such spatial big data for planning support. In this regards, this study attempts to develop a dynamic visualization model and conducts an experimental case study with mobile phone big data for the Jeju island. Since the off-the-shelf commercial software for the analysis of spatial big data is not yet commonly available, the roles of open source software and computer programming are important. This research presents a pilot model of dynamic visualization for spatial big data, as well as results from them. Then, the study concludes with future studies and implications to promote the use of spatial big data in urban planning field.