• 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.

• Journal of KIBIM
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• v.6 no.3
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• pp.24-33
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• 2016

Planning occurs from day-to-day, small-scale decisions to large-scale infrastructure investment decisions. For that reason, various attempts have been made to appropriately assist decision-making process and its optimization. Lately, initiation of a large amount of data, also known as big data has received great attention from diverse disciplines because of versatility and adoptability in its use and possibility to generate new information. Accordingly, implementation of big data and other information management systems, such as geographic information systems (GIS) and building information modeling (BIM) have received enough attention to establish each of its own profession and other associated activities. In this extent, this study illustrates a series of big data implementation cases that can provide a lesson to urban planning domain. In specific, case studies analyze how data was used to extract the most optimized solution and what aspects could be helpful in relation to planning decisions. Also, important notions about GIS and its application in various urban cases are examined.

• Journal of KIBIM
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• v.13 no.4
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• pp.106-114
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• 2023

In this study, we developed a method to quantify urban social costs using mobile sensing data, providing a novel approach to urban planning. By collecting and analyzing extensive mobile data over time, we transformed travel patterns into measurable social costs. Our findings highlight the effectiveness of big data in urban planning, revealing key correlations between transportation modes and their associated social costs. This research not only advances the use of mobile data in urban planning but also suggests new directions for future studies to enhance data collection and analysis methods.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.1
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• pp.19-28
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• 2019

The purpose of this study is to evaluate the walking behavior analysis methodology used in the previous studies, paying attention to the demand for empirical data collecting for urban and neighborhood planning. The preceding researches are divided into (1)Recording, (2) Surveys, (3)Statistical data, (4)Global positioning system (GPS) devices, and (5)Mobile Big Data analysis. Next, we analyze the precedent research and identify the changes of the walkability research. (1)being required empirical data on the actual walking and moving patterns of people, (2)beginning to be measured micro-walking behaviors such as actual route, walking facilities, detour, walking area. In addition, according to the trend of research, it is analyzed that the use of GPS device and the mobile big data are newly emerged. Finally, we analyze pedestrian data based on mobile big data in terms of 'application' and distinguishing it from existing survey methodology. We present the possibility of mobile big data. (1)Improvement of human, temporal and spatial constraints of data collection, (2)Improvement of inaccuracy of collected data, (3)Improvement of subjective intervention in data collection and preprocessing, (4)Expandability of walking environment research.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.91-100
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• 2022

Major developed countries are seriously considering smart factories to increase their manufacturing competitiveness. Smart factory is a customized factory that incorporates ICT in the entire process from product planning to design, distribution and sales. This can reduce production costs and respond flexibly to the consumer market. The smart factory converts physical signals into digital signals, connects machines, parts, factories, manufacturing processes, people, and supply chain partners in the factory to each other, and uses the collected data to enable the smart factory platform to operate intelligently. Enhancing personalized value is the key. Therefore, it can be said that the success or failure of a smart factory depends on whether big data is secured and utilized. Standardized communication and collaboration are required to smoothly acquire big data inside and outside the factory in the smart factory, and the use of big data can be maximized through big data analysis. This study examines big data analysis and standardization in smart factory. Manufacturing innovation by country, smart factory construction framework, smart factory implementation key elements, big data analysis and visualization, etc. will be reviewed first. Through this, we propose services such as big data infrastructure construction process, big data platform components, big data modeling, big data quality management components, big data standardization, and big data implementation consulting that can be suggested when building big data infrastructure in smart factories. It is expected that this proposal can be a guide for building big data infrastructure for companies that want to introduce a smart factory.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1259-1273
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• 2012

As a society of knowledge and information has been developed rapidly, because of changing from web environment to ubiquitous environment, a lot of countries across the world as well as S. Korea for national information with electronic Government have a variety of changes with big data. So this study is about development for national information and e-government of S. Korea with public data as big data analysed by the application of scenario planning. And then this research focused on the strategy consulting of national information with e-Government of S. Korea for utilization of public data as big data analysed by the application of 'scenario planning' as a foresight method. As a result, the future policy for utilization of public data as big data for national information with electronic government of S. Korea is to further spur the development of technology for linked data with semantic web for 'understanding of machine' than 'understanding of man'.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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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.

• The Journal of Information Systems
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• v.30 no.1
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• pp.151-177
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• 2021

Purpose This paper aims to improve management performance by effectively responding to production needs and reducing inventory through synchronizing production planning and procurement in the aviation industry. In this study, the differences in production planning and execution were first analyzed in terms of demand, supply, inventory, and process using the big data collected from a domestic aircraft manufacturers. This paper analyzed the problems in procurement and inventory management using legacy big data from ERP system in the company. Based on the analysis, we performed a simulation to derive an efficient procurement and inventory management plan. Through analysis and simulation of operational data, we were able to discover procurement and inventory policies to effectively respond to production needs. Design/methodology/approach This is an empirical study to analyze the cause of decrease in inventory turnover and increase in inventory cost due to dis-synchronize between production requirements and procurement. The actual operation data, a total of 21,306,611 transaction data which are 18 months data from January 2019 to June 2020, were extracted from the ERP system. All them are such as basic information on materials, material consumption and movement history, inventory/receipt/shipment status, and production orders. To perform data analysis, it went through three steps. At first, we identified the current states and problems of production process to grasp the situation of what happened, and secondly, analyzed the data to identify expected problems through cross-link analysis between transactions, and finally, defined what to do. Many analysis techniques such as correlation analysis, moving average analysis, and linear regression analysis were applied to predict the status of inventory. A simulation was performed to analyze the appropriate inventory level according to the control of fluctuations in the production planing. In the simulation, we tested four alternatives how to coordinate the synchronization between the procurement plan and the production plan. All the alternatives give us more plausible results than actual operation in the past. Findings Based on the big data extracted from the ERP system, the relationship between the level of delivery and the distribution of fluctuations was analyzed in terms of demand, supply, inventory, and process. As a result of analyzing the inventory turnover rate, the root cause of the inventory increase were identified. In addition, based on the data on delivery and receipt performance, it was possible to accurately analyze how much gap occurs between supply and demand, and to figure out how much this affects the inventory level. Moreover, we were able to obtain the more predictable and insightful results through simulation that organizational performance such as inventory cost and lead time can be improved by synchronizing the production planning and purchase procurement with supply and demand information. The results of big data analysis and simulation gave us more insights in production planning, procurement, and inventory management for smart manufacturing and performance improvement.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.4
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• pp.296-303
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• 2016

In public transportation, smart cards have been introduced for the purpose of convenient payment systems. The smart card transaction data can be utilized not only for the exact and convenient payment but also for civil planning based on travel tracking of citizens. This paper focuses on the analysis of the transportation convenience using the smart card big data. To this end, a new index is developed to measure the transit convenience of each region by considering how passengers actually experience the transportation network in their travels. The movement data such as movement distance, time and amount between regions are utilized to access the public transportation convenience of each region. A smart card data of five working days in March is used to evaluate the transit convenience of each region in Seoul city. The contribution of this study is that a new transit convenience measure was developed based on the reality data. It is expected that this measure can be used as a means of quantitative analysis in civil planning such as a traffic policy or local policy.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.87-92
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• 2014

We have noted a possibility of big data as a solution of social problem and pending issue. At the same time big data has a problem of privacy. Big data and privacy were in conflict. In this paper we pointed out that issue and propose a planning of big data based on privacy using case study of advanced country.