• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.247-249
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• 2013

Even though Relational Databases have been widely used in many enterprises, the relations among entities are not managed effectively and efficiently. In order to analyze Big Data, it is absolutely needed to express various relations among entities in a graphical form. In this paper, we define Graph Databases and its structure. And then, we check out their characteristics such as transaction, consistency, availability, retrieval function, and expandability. Also, we appropriate or inappropriate subjects for application of Graph Databases.

• East Asian Journal of Business Economics (EAJBE)
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• v.1 no.1
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• pp.17-21
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• 2013

Purpose: Forecasting of petroleum consumption is useful in planning and management of petroleum production and control of air pollution. Research Design, Data and Methodology: ARMA models, sometimes called Box-Jenkins models after the iterative Box-Jenkins methodology usually used to estimate them, are typically applied to auto correlated time series data. Results: Petroleum consumption modeling plays a role key in big urban air pollution planning and management. In this study three models as, MLFF, MLFF with GARCH (1,1) and ARMA(1,1), have been investigated to model the petroleum consumption forecasts. Certain standard statistical parameters were used to evaluate the performance of the models developed in this study. Based upon the results obtained in this study and the consequent comparative analysis, it has been found that the MLFF with GARCH (1,1) have better forecasting results.. Conclusions: Survey of data reveals that deposit of government policies in recent yeas, petroleum consumption rises in Tehran and unfortunately more petroleum use causes to air pollution and bad environmental problems.

• The Journal of Bigdata
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• v.9 no.1
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• pp.1-16
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• 2024

In urban design and planning, establishing neighborhood units and arranging urban planning facilities are important matters to be considered first. In particular, effective arrangement considering the influence area of each urban planning facility can solve traffic problems and improve the efficiency of urban structure according to the visitors to the facility, and can be used as basic data for more effective living areas. Therefore, this study proposed a methodology to analyze the number of users, the time required for access, and the destinations of users for major urban planning facilities such as schools and neighborhood parks based on mobile communication base station data. In addition, using this methodology, the users and influence areas of major urban planning facilities in Cheonan-si were analyzed.

• Journal of Cadastre & Land InformatiX
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• v.50 no.2
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• pp.101-115
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• 2020

In this paper, big data analysis method was used to find out global trends in autonomous driving and to derive activate spatial information services. The applied big data was used in conjunction with news articles and patent document in order to analysis trend in news article and patents document data in spatial information. In this paper, big data was created and key words were extracted by using LDA (Latent Dirichlet Allocation) based on the topic model in major news on autonomous driving. In addition, Analysis of spatial information and connectivity, global technology trend analysis, and trend analysis and prediction in the spatial information field were conducted by using WordNet applied based on key words of patent information. This paper was proposed a big data analysis method for predicting a trend and future through the analysis of the connection between the autonomous driving field and spatial information. In future, as a global trend of spatial information in autonomous driving, platform alliances, business partnerships, mergers and acquisitions, joint venture establishment, standardization and technology development were derived through big data analysis.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.3
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• pp.281-287
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• 2023

Establishing a ship's passage plan is an essential step before it starts to sail. The research related to the automatic generation of ship passage plans is attracting attention because of the development of maritime autonomous surface ships. In coastal water navigation, the land, islands, and navigation rules need to be considered. From the path planning algorithm's perspective, a ship's passage planning is a global path-planning problem. Because conventional global path-planning methods such as Dijkstra and A^* are time-consuming owing to the processes such as environmental modeling, it is difficult to modify a ship's passage plan during a voyage. Therefore, the D^* algorithm was used to address these problems. The starting point was near Busan New Port, and the destination was Ulsan Port. The navigable area was designated based on a combination of the ship trajectory data and grid in the target area. The initial path plan generated using the D^* algorithm was analyzed with 33 waypoints and a total distance of 113.946 km. The final path plan was simplified using the Douglas-Peucker algorithm. It was analyzed with a total distance of 110.156 km and 10 waypoints. This is approximately 3.05% less than the total distance of the initial passage plan of the ship. This study demonstrated the feasibility of automatically generating a path plan in coastal navigation for maritime autonomous surface ships using the D^* algorithm. Using the shortest distance-based path planning algorithm, the ship's fuel consumption and sailing time can be minimized.

• Journal of Korean Society of Rural Planning
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• v.27 no.4
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• pp.13-24
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• 2021

This study used big data to analyze visitors' experiences in Fishing Experience Recreation Village. Through the portal site posting data for the past six years, the experience of visiting Fishing Experience Villages in Baekmi and Susan was analyzed. The analysis method used Text mining and Social Network Analysis which are Big data analysis techniques. Data was collected using Textom, and experience keywords were extracted by analyzing the frequency and importance of experience texts. Afterwards, the characteristics of the experience of visiting the Fishing Experience Village were identified through the analysis of the interaction between the experience keywords using 'U cinet 6.0' and 'NetDraw'. First, through TF and TF-IDF values, keywords such as "Gungpyeong Port", "Susan Port", and "Yacht Marina" that refer to the name of the port and the port facilities appeared at the top. This is interpreted as the name of the port has the greatest impact on the recognition of the Fishing Experience Villages, and visitors showed a lot of interest in the port facilities. Second, focusing on the unique elements of port facilities and fishing villages such as "mud flat experience", "fishing village experience", "Gungpyeong port", "Susan port", "yacht marina", and "beach" through the values of degree, closeness, and betweenness centrality interpreted as having an interaction with various experiences. Third, through the CONCOR analysis, it was confirmed that the visitor's experience was focused on the dynamic behavior, the experience program had the greatest influence on the experience of the visitor, and that the experience of the static and the dynamic behavior was relatively balanced. In conclusion, the experience of visitors in the Fishing Experience Villages is most affected by the environment of the fishing village such as the tidal flats and the coast and the fishing village experience program conducted at the fishing port facilities. In particular, it was found that fishing port facilities such as ports and marinas had a high influence on the awareness of the Fishing Experience Villages. Therefore, it is important to actively utilize the scenery and environment unique to fishing villages in order to revitalize the Fishing Experience Villages experience and improve the quality of the visitor experience. This study is significant in that it studied visitors' experiences in fishing village recreation villages using big data and derived the connection between fishing village and fishing village infrastructure in fishing village experience tourism.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.11a
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• pp.50-51
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• 2019

Globally, South Korea is a country that has a lot of $CO_2$ emissions and has steadily increased its total greenhouse gas emissions since the 1990s. With the recent implementation of the carbon emission trading system in Korea, the importance of calculating $CO_2$ emissions of construction equipment is increasing, hence the need for accurate calculation of environmental penalties through allocating carbon emission rights. This study presents a methodology to predict the price of carbon credits using big data analysis method. This methodology is based on correlating and regression analysis of trends in carbon emission prices and search volumes. This study aims to support faster and more accurate budget calculations in the planning of the construction process based on the predicted price of carbon emission rights.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.367-376
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• 2020

According to recent statistics on new consumer market trends, 'alone consumption' is at the center. This study focuses on the social big data that attracts the public's opinions in that it is important for a certain social trend to comprehensively understand the various fields such as society, locality, culture, marketing, economics, and psychology that form the background for it. Therefore, we set up the linkage of 'solo consumption' and conducted research on new consumer market trends using Opinion Analisys. As a result of this trend analysis, representative keywords such as 'honbab', 'honsul' and 'honyoeng' were derived and analyzed the trend of new consumer market using this data. Alone consumption is an inevitable new consumption trend caused by demographic change after the global economic crisis. The importance as a trend reflecting this will be further strengthened. Trend analysis by social big data will help scientific and systematic business distribution strategies and planning to help make new and valuable decisions and decisions about new consumer markets.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.1-10
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• 2018

Current evaluation practices for IT projects suffer from several problems, which include the difficulty of self-explanation for the evaluation results and the improperly scaled scoring system. This study aims to develop a methodology of opinion mining to extract key factors for the causal relationship analysis and to assess the feasibility of quantifying evaluation scores from text comments using opinion mining based on big data analysis. The research has been performed on the domain of publicly procured IT proposal evaluations, which are managed by the National Procurement Service. Around 10,000 sets of comments and evaluation scores have been gathered, most of which are in the form of digital data but some in paper documents. Thus, more refined form of text has been prepared using various tools. From them, keywords for factors and polarity indicators have been extracted, and experts on this domain have selected some of them as the key factors and indicators. Also, those keywords have been grouped into into dimensions. Causal relationship between keyword or dimension factors and evaluation scores were analyzed based on the two research models-a keyword-based model and a dimension-based model, using the correlation analysis and the regression analysis. The results show that keyword factors such as planning, strategy, technology and PM mostly affects the evaluation result and that the keywords are more appropriate forms of factors for causal relationship analysis than the dimensions. Also, it can be asserted from the analysis that evaluation scores can be composed or calculated from the unstructured text comments using opinion mining, when a comprehensive dictionary of polarity for Korean language can be provided. This study may contribute to the area of big data-based evaluation methodology and opinion mining for IT proposal evaluation, leading to a more reliable and effective IT proposal evaluation method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.171-178
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• 2019

Korea's shipbuilding industry is in a critical condition due to changes in the domestic and international environment. To overcome this crisis, preemptive development of products and technologies through prediction of new demand for ships is necessary. The goal of this research is to develop an artificial intelligence algorithm based on ship big data in order to predict new demand for ships. We intend to develop a big data analytics platform specialized in predicting ship demand and to utilize the forecast results of new ship demand through data analysis for planning/development of new products. By doing so, the development of sustainable new business models for equipment and equipment manufacturers will create new growth engines for shipyard and shipbuilders. Furthermore, it is expected that shipbuilders will be able to create business cases based on measurable performance, plan market-oriented products and services, and continuously achieve innovation that has high market destructive power.