• The Journal of the Convergence on Culture Technology
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• v.6 no.1
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• pp.413-418
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• 2020

The travel pattern of tourists for the development of the tourist course is designed to collect and analyze tourist information based on the big data of the carrier to improve the quality of the tourist course. In particular, the analyzed data is used to derive empirical data that can estimate the effect of tourists' inflow into tourism, and to utilize the information as basic data for the development of the tourist course. In addition, the travel pattern of tourists for the development of regional tourism courses is to collect and analyze information on the route and duration of tourists' travel based on big data collected by telecom operators, credit card companies and other data to improve the quality of tourist course development, and to derive empirical data to estimate the effect of tourist inflow through the analyzed data, based on the characteristics of the tourism course and the data needed for the development of new tourist courses in the future.

• Journal of Digital Convergence
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• v.17 no.6
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• pp.365-371
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• 2019

In this study, VR contents were produced by using the rose which is the material of the tomb of the surrealistic work wrestler of Rene Magritte, an artistic genre, as a motive. In conclusion, the distortion (spatial modulation) of the image scale is connected to the dynamic-curve and texture-soft areas, and the superposition (combination of contradictory images) is called the big-size, irregular-depth area, Are connected to the positions of big-size and irregular-space regions. The theme of the work was Dream, and the plants and roses patterns were produced in each timeline, and overlap, scale, distortion, overlap, distortion, and scale were used.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.684-690
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• 2018

In society of the big data is being recognized as one of the core technologies witch is analysis of the collected information, the intelligent evolution of society seems to be more oriented society through an optimized value creation based on a prediction technique. If we take advantage of technologies based on big data about various data and a large amount of data generated during system operation, it will be possible to support stable operation and prevention of faults and failures. In this paper, we suggested an environment using the collection and analysis of big data, and proposed an derive time series prediction model for predicting failure through server performance monitoring for data collected and analyzed. It can be capable of supporting stable operation of the IT systems through failure prediction model for the server operator.

• Journal of Environmental Health Sciences
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• v.44 no.6
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• pp.563-571
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• 2018

Objectives: The purpose of this study was to investigate the environmental pollution caused by exposure to air pollution in Korea. Therefore, it is necessary to investigate environmental and health factors through big data. Methods: Among the environmental diseases, the data centered on "percentage per day in 2015 to 2018". Data of environmental diseases and concentrations of air pollution monitoring network were analyzed. Results: Lung cancer and bronchiolitis obliterans were correlated with 0.027 and 0.0158, respectively, in the contamination concentration of fine dust ($PM_{10}$). Ozone, COPD, allergic rhinitis, and bronchiolitis obliterans were correlated with 0.0022, 0.0028 and 0.0093, respectively. At the concentration of $SO_2$ and the diseases of asthma, atopic dermatitis, lung cancer and bronchiolitis obliterans were 0.0008, 0.0523, 0.0016 and 0.0126, respectively. Conclusions: We surveyed the trends of air pollution according to the characteristics of Seoul area in Korea and evaluated the perception of Korea and the world. As a result, respiratory lung disease is thought to be a major factor in exposure to environmental pollution.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.1-9
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• 2018

In the fourth industrial revolution, which has become an issue now, we have been able to receive instant analysis results faster than the existing slow speed through various Big Data technologies, and to conduct real-time monitoring on mobile and web. First, various irregular sensor Data is generated using IoT device, Raspberry Pi. Sensor Data is collected in real time, and the collected data is distributed and stored using several nodes. Then, the stored Sensor Data is processed and refined. Visualize and output the analysis result after analysis. By using these methods, we can train the human resources required for Big Data and mobile related fields using IoT, and process data efficiently and quickly. We also provide information that can confirm the reliability of research results through real time monitoring.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.173-178
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• 2019

Recently, the types and amount of data generated, collected, and measured in IoT such as smart home, security, and factory are increasing. The technologies for IoT service include sensor devices to measure desired data, embedded software to control the devices such as signal processing, wireless network protocol to transmit and receive the measured data, and big data and AI-based analysis. In this paper, we focused on developing a gateway for interfacing heterogeneous sensor network protocols that are used in various IoT devices and propose a heterogeneous network interface IoT gateway. We utilized a OpenWrt-based wireless routers and used 6LoWAN stack for IP-based communication via BLE and IEEE 802.15.4 adapters. We developed a software to convert Z-Wave and LoRa packets into IP packet using our Python-based middleware. We expect the IoT gateway to be used as an effective device for collecting IoT big data.

• Journal of Industrial Convergence
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• v.18 no.6
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• pp.19-25
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• 2020

Big data analytics are indispensable today in various industries and public sectors. Therefore, in this study, we will utilize big data analysis to search for improvement plans for domestic tourism services using the LDA analysis method. In particular, we have tried an exploratory approach that can improve tourist satisfaction, which can improve revisit and service, especially in Seoul, which has the largest number of foreign tourists. In this study, we collected and analyzed statistical data of Seoul City and Korea Tourism Organization and Internet information such as SNS via R. And we utilized text mining methods including LDA. As a result of the analysis, one of the purposes of visiting South Korea by foreigners was gastronomic tourism. We will try to derive measures to improve the quality of services centered on gastronomic tourism.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1137-1144
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• 2020

This paper aims to provide strategic suggestions by analyzing technology trends related to big data, IoT, and artificial intelligence. To this end, analysis was performed using the 2018 national R&D information, and major basic analysis and language network analysis were performed. As a result of the analysis, research and development related to big data, IoT, and artificial intelligence are being conducted by focusing on the basic and development stages, and it was found that universities and SMEs have a high proportion. In addition, as a result of the language network analysis, it is judged that the related fields are mainly research for use in the smart farm and healthcare fields. Based on these research results, first, big data is essential to use artificial intelligence, and personal identification research should be conducted more actively. Second, they argued that full-cycle support is needed for technology commercialization, not simple R&D activities, and the need to expand application fields.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.107-117
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• 2020

With the recent development of autonomous vehicles, various sensors installed in vehicles have become common, and big data generated from those sensors is increasingly being used in the transportation field. In this study, we proposed a grid index method to efficiently process real-time vehicle sensing big data and public data such as road weather. The applicability and effect of the proposed grid space division method and grid ID generation method were analyzed. We created virtual data based on DTG data and mapped to the road link based on coordinates. As a result of analyzing the data processing speed in grid index method, the data processing performance improved by more than 2,400 times compared to the existing link unit processing method. In addition, in order to analyze the efficiency of the proposed technology, the virtually generated data was mapped and visualized.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.39-46
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• 2021

When conducting social big data analysis for franchise stores, reviews of multiple branches of a franchise can be collected together, from which analysis results can be distorted significantly. To improve its accuracy, it should be possible to filter reviews of other branches properly which are not subject to the analysis. This paper presents a method for social big data analysis which reflects characteristics of franchise stores. The proposed method consists of search key configuration and review filtering. For the former, the open data provided by Small Business Promotion Agency is used to extract region names for collecting reviews more accurately. For the latter, open search APIs provided by Naver or Kakao are used to obtain franchise branch information for filtering reviews of other branches that are not subject to analysis. To verify performance of the proposed method, experiments were conducted based on real social reviews collected from online, where the results showed that the accuracy of the proposed review filtering was 93.6% on the average.