• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.207-216
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• 2021

Recently, there is increasing interest in attracting local tourist in the city to revitalize the local economy. For this purpose, customized tourism strategies based on the analysis of travel routes and consumption patterns are becoming important. However, existing studies either focused on limited mainstream tourist analysis or lacked analysis of tourists' behavior-based data perspectives. Therefore, this study aims to present a big data-based tourism strategy that provides customized information by analyzing the demand of individual travelers in details based on mobile service data and card expenditure data generated by the travelers in Yongin city. By tracing those data, this study visualized the tourists' itinerary and their expenditure patterns. The analysis of data from July 2017 to June 2018 shows that men tend to consume in various areas compared to women. It also shows consumption areas for people in their 30s and 40s are similar, whereas those in their 20s do not vary. Using the big data based on Geographic Information system, this study provides strategic insights to administrative personnel who are in charge of tour policy.

• The Journal of Bigdata
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• v.5 no.2
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• pp.85-95
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• 2020

In this study, a mobility analysis method is suggested to estimate an O/D trip demand estimation using Mobile Phone Signaling Data. Using mobile data based on mobile base station location information, a trip chain database was established for each person and daily traffic patterns were analyzed. In addition, a new algorithm was developed to determine the traffic characteristics of their mobilities. To correct the ping pong handover problem of communication data itself, the methodology was developed and the criteria for stay time was set to distinguish pass by between stay within the influence area. The big-data based method is applied to analyze the mobility pattern in inter-regional trip and intra-regional trip in both of an urban area and a rural city. When comparing it with the results with traditional methods, it seems that the new methodology has a possibility to be applied to the national survey projects in the future.

• The Journal of the Korea Contents Association
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• v.13 no.2
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• pp.211-219
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• 2013

The analysis method using Big Data has evolved based on the Big data Management Technology. There are quite a few researching institutions anticipating new era in data analysis using Big Data and IT vendors has been sided with them launching standardized technologies for Big Data management technologies. Big Data is also affected by improvements of IT gadgets IT environment. Foreran by social media, analyzing method of unstructured data is being developed focusing on diversity of analyzing method, anticipation and optimization. In the past, data analyzing methods were confined to the optimization of structured data through data mining, OLAP, statics analysis. This data analysis was solely used for decision making for Chief Officers. In the new era of data analysis, however, are evolutions in various aspects of technologies; the diversity in analyzing method using new paradigm and the new data analysis experts and so forth. In addition, new patterns of data analysis will be found with the development of high performance computing environment and Big Data management techniques. Accordingly, this paper is dedicated to define the possible analyzing method of social media using Big Data. this paper is proposed practical use analysis for social media analysis through data mining analysis methodology.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.13-21
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• 2021

This study aims to analyze tourist inflow trends and consumption patterns using a geographic information system and big data analysis system. Songdo Central Park and Chinatown were selected among the major tourist destinations in Incheon, and floating population analysis and card sales analysis were conducted for one month in June 2017. The number of tourists visiting Songdo Central Park from metropolitan cities across the country was highest in the order of Incheon Metropolitan City, Gyeonggi-do, and Seoul Metropolitan City, and the proportion of foreign tourists was the highest in China. The number of card consumption used by Chinatown tourists was 12.4% higher for men than for women, and the amount of card consumption was also higher for men by 18%. This study has implications for proposing a strategic plan for tourism policy by analyzing the inflow trend and consumption pattern of tourists and deriving major issues in the establishment of tourism policy. Based on this study, it is expected that it can be helpful in improving the construction of tourism infrastructure in the future.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2014.06a
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• pp.319-322
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• 2014

VTS(Vessel Traffic Center)는 관할해역의 해상교통데이터를 수집하여 해상교통관제를 수행하고 있다. 이러한 해상교통데이터는 가공되지 않는 정보이므로, 관제사 및 선박 등 사용자가 유용하게 활용할 수 있는 형태로의 분석이 필요하다. 이는 객관적인 데이터로 관제사 및 선박에서 해상교통 안전정책을 수립하는데 중요하다. 이를 위해 본 연구에서는 수년간 VTS에 축적되고 있는 BIG DATA를 활용하여 해상교통패턴을 분석하고자 한다. 분석하는 해상교통패턴은 통항분포, 선종별 항적 비교, 예부선의 강 조류 주의구역 판별, 항로상 어선 조업 현황분석 등을 통해 빅데이터를 활용한 관제구역설정, 집중관제구역 검토가 가능하다.

• Journal of Korean Society of Archives and Records Management
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• v.21 no.4
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• pp.1-18
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• 2021

Today, record management has become more important in management as records generated from administrative work and data production have increased significantly, and the development of information and communication technology, the working environment, and the size and various functions of the government have expanded. It is explained as an example in connection with the concept of public records with the characteristics of big data and big data characteristics. Social, Technological, Economical, Environmental and Political (STEEP) analysis was conducted to examine such areas according to the big data generation environment. The appropriateness and necessity of applying big data technology in the field of public record management were identified, and the top priority applicable framework for public record management work was schematized, and business implications were presented. First, a new organization, additional research, and attempts are needed to apply big data analysis technology to public record management procedures and standards and to record management experts. Second, it is necessary to train record management specialists with "big data analysis qualifications" related to integrated thinking so that unstructured and hidden patterns can be found in a large amount of data. Third, after self-learning by combining big data technology and artificial intelligence in the field of public records, the context should be analyzed, and the social phenomena and environment of public institutions should be analyzed and predicted.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1061-1069
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• 2015

Recently, transportation big data generated in the transportation sector has been widely used in the transportation policies making and efficient system management. Bus passengers' mobility patterns are useful insight for transportation policy maker to optimize bus lines and time intervals in a city. We propose a new methodology to discover mobility patterns by using transportation card data. We first estimate the bus stations where the passengers get-off because the transportation card data don't have the get-off information in most cities. We then applies LDA (Latent Dirichlet Allocation), the most representative topic modeling technique, to discover mobility patterns of bus passengers in Cheong-Ju city. To understand discovered patterns, we construct a data warehouse and perform multi-dimensional analysis by bus-route, region, time-period, and the mobility patterns (get-on/get-off station). In the case of Cheong Ju, we discovered mobility pattern 1 from suburban area to Cheong-Ju terminal, mobility pattern 2 from residential area to commercial area, mobility pattern 3 from school areas to commercial area.

• Information Systems Review
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• v.21 no.4
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• pp.175-195
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• 2019

This study conducted both big data and netnography analysis to analyze consumer needs and behaviors of online consumer community. Big data analysis is easy to identify correlations, but causality is difficult to identify. To overcome this limitation, we used netnography analysis together. The netnography methodology is excellent for context grasping. However, there is a limit in that it is time and costly to analyze a large amount of data accumulated for a long time. Therefore, in this study, we searched for patterns of overall data through big data analysis and discovered outliers that require netnography analysis, and then performed netnography analysis only before and after outliers. As a result of analysis, the cause of the phenomenon shown through big data analysis could be explained through netnography analysis. In addition, it was able to identify the internal structural changes of the community, which are not easily revealed by big data analysis. Therefore, this study was able to effectively explain much of online consumer behavior that was difficult to understand as well as contextual semantics from the unstructured data missed by big data. The big data-netnography integrated model proposed in this study can be used as a good tool to discover new consumer needs in the online environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.741-743
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• 2015

오늘날 BI(Business Intelligence)시스템 다차원 데이터를 다루는 많은 방법들이 제안되어 TB 이상의 데이터를 다룰 수 있다. 하지만 IT 전문가 및 IT에 대한 투자여력이 충분하지 않은 중소 제조 기업들은 발 맞춰가기 힘들다. 또한 생산관리시스템(MES)을 미 도입한 기업이 대다수이고, 존재하는 현장데이터의 대부분도 수기데이터 또는 Excel 데이터로 보관 되어 있어, 수작업에 의한 데이터 분석과 의사결정을 수행한다. 이로 인해, 불량 요인 파악이나 이상 현상 파악이 불분명하기 때문에 데이터 분석에 어려움을 겪는다. 이에 본 연구에서는 중소제조기업의 경쟁력 강화를 위하여 제조 기업현장에서 사용되는 데이터를 자동으로 수집하여 정제 및 처리하여 저장이 가능하도록 하는 빅 데이터 분석 시스템 모델을 개발하였다. 이 분석 시스템 모델은 ERP, MIS 등에 존재하는 데이터들이 각 시스템의 DB 기능을 활용하여 데이터를 추출하고 정제하여 수집하는 ETL(Extract Transform Loading)과정을 통한다. 현장에서 비정형으로 기록되고 있는 정보들(ex. Excel)은 ODE(Office Data Excavation)모듈을 통해 문서의 패턴을 자동으로 인식하고 정형화된 정보로서 추출, 정제되어 수집된다. 저장된 데이터는 오픈소스 데이터 시각화 라이브러리인 D3.js를 이용하여 다양한 chart들을 통한 강력한 시각효과를 제공함으로써, 정보간의 연관 관계 및 다차원 분석의 기반을 마련하여 의사결정체계를 효과적으로 지원한다. 또한, 높은 가격에 형성되어 있는 빅데이터 솔루션을 대신해 오픈소스 Spago BI를 이용하여 경제적인 빅 데이터 솔루션을 제공한다. 본 연구의 기대효과로는 첫째, 현장 데이터 중심의 효과적인 의사결정 기반을 마련할 수 있다. 둘째, 통합 데이터 기반의 연관/다차원 분석으로 경영 효율성이 향상된다. 마지막으로, 중소 제조기업 환경에 적합한 분석 시스템을 구축함으로써 경쟁력과 생산력을 강화한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.182-183
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• 2018

Big data analysis is the process of discovering a meaningful correlation, pattern, and trends in large data set stored in existing data warehouse management tools and creating new values. In addition, by extracts new value from structured and unstructured data set in big volume means a technology to analyze the results. Most of the methods of Big data analysis technology are data mining, machine learning, natural language processing, pattern recognition, etc. used in existing statistical computer science. Global research institutes have identified Big data as the most notable new technology since 2011.