• 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.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.117-125
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• 2022

Recently, as users' interest for travel increases, research on a travel route recommendation service that replaces the cumbersome task of planning a travel itinerary with automatic scheduling has been actively conducted. The most important and common goal of the itinerary recommendations is to provide the shortest route including popular tour spots near the travel destination. A number of existing studies focused on providing personalized travel schedules, where there was a problem that a survey was required when there were no travel route histories or SNS reviews of users. In addition, implementation issues that need to be considered when calculating the shortest path were not clearly pointed out. Regarding this, this paper presents a quantified method to find out popular tourist destinations using social big data, and discusses problems that may occur when applying the shortest path algorithm and a heuristic algorithm to solve it. To verify the proposed method, 63,000 places information was collected from the Gyeongnam province and big data analysis was performed for the places, and it was confirmed through experiments that the proposed heuristic scheduling algorithm can provide a timely response over the real data.

• 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 Internet Computing and Services
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• v.24 no.4
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• pp.65-76
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• 2023

This paper identifies the aspects of living environment elements (PM^2.5, PM¹⁰, Noise) throughout Seoul and the urban characteristics that affect them by utilizing the big data of the S-Dot sensors in Seoul, which has recently become a hot topic. In other words, it proposes a big data based urban computing research methodology and research direction to confirm the relationship between urban characteristics and living environments that directly affect citizens. The temporal range is from 2020 to 2021, which is the available range of time series data for S-Dot sensors, and the spatial range is throughout Seoul by 500mX500m GRID. First of all, as part of analyzing specific living environment patterns, simple trends through EDA are identified, and cluster analysis is conducted based on the trends. After that, in order to derive specific urban planning factors of each cluster, basic statistical analysis such as ANOVA, OLS and MNL analysis were conducted to confirm more specific characteristics. As a result of this study, cluster patterns of environment elements(PM^2.5, PM¹⁰, Noise) and urban factors that affect them are identified, and there are areas with relatively high or low long-term living environment values compared to other regions. The results of this study are believed to be a reference for urban planning management measures for vulnerable areas of living environment, and it is expected to be an exploratory study that can provide directions to urban computing field, especially related to environmental data in the future.

• Journal of the Korean Institute of Rural Architecture
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• v.22 no.4
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• pp.43-50
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• 2020

The purpose of this study is to extract deficiency areas as basic data of policies and projects in the future Living SOC introduction and planning. In order to extract living SOC deficient areas, accessibility data for living SOC and density data for main users by facility were overlapped, focusing on the living SOC indicators presented in the National Urban Regeneration Basic Policy. According to the analysis of accessibility of the Iksan-si Living SOC, the gap between deficiency in urban and township areas was large in common with the accessibility of the village and local base units. As a result of overlapping life SOC accessibility data and density data analysis of the main users by facility, areas where accessibility is weak but not inhabited by the main users of each facility were extracted. It is meaningful that more accurate deficient areas can be extracted by simultaneously utilizing the density distribution of the main users, rather than simply accessing the facilities.