• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.71-78
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• 2020

Recently, as the issue of gentrification emerges, it becomes important to understand the dynamics of local commercial district, which plays the important role for facilitating the local economy and building the community in a city. This paper attempts to provide the framework for systemically analyzing and understanding the local commercial district. Then, this paper empirically analyzes the patterns of sales and flow of floating population by focusing on two representative local commercial districts in Seoul. In addition, the floating population data from telecommunication bases is further modeled with Markov chain for systemically understanding the local commercial districts. Finally, the transition patterns and consumption amounts of floating population are comprehensively analyzed for providing the implications on the evolutions of local commercial districts in a city. We expect that findings of our study could contribute to the economic growth of local commercial district, which could lead to the continuous development of city economy.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.133-143
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• 2017

Data is the most important asset in the financial sector. On average, 71 percent of financial institutions generate competitive advantage over data analysis. In particular, in the card industry, the card transaction data is widely used in the development of merchant information, economic fluctuations, and information services by analyzing patterns of consumer behavior and preference trends of all customers. However, creation of new value through fusion of data is insufficient. This study introduces the analysis and forecasting of consumption trends of credit card companies which convergently analyzed the social data and the sales data of the company's own. BC Card developed an algorithm for linking card and social data with trend profiling, and developed a visualization system for analysis contents. In order to verify the performance, BC card analyzed the trends related to 'Six Pocket' and conducted th pilot marketing campaign. As a result, they increased marketing multiplier by 40~100%. This study has implications for creating a methodology and case for analyzing the convergence of structured and unstructured data analysis that have been done separately in the past. This will provide useful implications for future trends not only in card industry but also in other industries.

• The Journal of Bigdata
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• v.5 no.2
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• pp.17-27
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• 2020

District heating system supplies heat from low-cost high-efficiency heat production facilities to heat demand areas through a heat pipe network. For efficient heat supply system operation, it is important to accurately predict the heat demand within the region and optimize the heat production plan accordingly. In this study, a heat demand forecasting model is proposed considering real-time calorimeter information from local heat demands. Previous models considered ambient temperature and heat demand history data to predict future heat demands. To improve forecast accuracy, the proposed heat demand forecast model added big data from real-time calorimeters installed in the heat demands within the target region. By employing calorimeter information directly in the model, it is expected that the proposed forecast model is to reflect heat use pattern of each demand. Computational experiemtns based on the actual heat demand data shows that the forecast accuracy of the proposed model improved when the calorimeter big data is reflected.

• Information Systems Review
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• v.21 no.2
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• pp.1-27
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• 2019

It is possible to provide Smart Tourism Service through the development of information technology. It is necessary for the tourism industry to understand and utilize Big Data that has tourists' consumption patterns and service usage patterns in order to continuously create a new business model by converging with other industries. This study suggests to activate Jeju Smart Tourism by analyzing Big Data based on credit card usage records and location of tourists in Jeju. The results of the study show that First, the percentage of Chinese tourists visiting Jeju has decreased because of the effect of THAAD. Second, Consumption pattern of Chinese tourists is mostly occurring in the northern areas where airports and duty-free shops are located, while one in other regions is very low. The regional economy of Jeju City and Seogwipo City shows a overall stagnation, without changes in policy, existing consumption trends and growth rates will continue in line with regional characteristics. Third, we need a policy that young people flow into by building Jeju Multi-complex Mall where they can eat, drink, and go shopping at once because the number of young tourists and the price they spend are increasing. Furthermore, it is necessary to provide services for life-support related to weather, shopping, traffic, and facilities etc. through analyzing Wi-Fi usage location. Based on the results, we suggests the marketing strategies and public policies for understanding Jeju tourists' patterns and stimulating Jeju tourism industry.

• Journal of the Korean Applied Science and Technology
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• v.39 no.6
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• pp.853-863
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• 2022

In order to revitalize start-ups in the beauty field, this study attempted to derive characteristic patterns of changes in demand and differences in emotions and meaning for 'beauty start-ups' by dividing the period by year from 2019 to 2021 based on exploratory data analysis (EDA). Most of the search terms related to the keyword "beauty start-up" showed more interest in institutions or certificates that can learn beauty skills than professional start-up education, which still does not recognize the importance of start-up education, and as an alternative, it is necessary to develop customized start-up education programs for each major. We establish hypotheses through exploratory data analysis and verify hypotheses by combining traditional corroborative data analysis (CDA). There has never been an exploratory data analysis method for beauty startups, and rather than mentioning the need for formal start-up education, analyzing changes in interest in beauty startups and the requirements of prospective start-ups with exploratory data will help develop customized start-up programs.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.173-187
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• 2023

In an era of population decline, depopulated regions facing challenges in attracting inbound population migration must enhance urban vitality through the attraction of living populations. This study focuses on Busan, a city experiencing population decline, comparing the spatiotemporal distribution characteristics of registered residents and living populations in various administrative districts (Eup-Myeon-Dong) using mobile communication big data. Administrative districts are typified based on population change patterns, and regional characteristics are analyzed using indicators related to urban decline and vitality. Spatiotemporal distribution analysis reveals generally similar density patterns between registered residents and living populations; however, a distinctive feature is observed in the city center areas where the density of registered residents is low, while the density of living populations is high. Divergent trends in spatial patterns of change between registered residents and living populations show clusters of registered population decline in low-density areas and clusters of living population decline in high-density areas. Areas adjacent to declining living populations exhibit large clusters of population changes, indicating a spillover effect from high-density to neighboring areas. Typification results reveal that, even in areas with a decline in registered residents, there is active population influx due to commuting or visiting. These areas sustain an increase in the number of businesses, confirming the presence of industrial and economic growth. However, approximately 47% of administrative districts in Busan are experiencing a decline in both registered residents and living populations, indicating ongoing regional decline. Urgent measures are needed for enhancing urban vitality. The study emphasizes the necessity of utilizing living population data as an urban planning indicator, considering the increasing limit distance of urban activities and growing interregional interaction due to advancements in transportation and communication.

• Knowledge Management Research
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• v.22 no.4
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• pp.119-133
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• 2021

As a large amount of data is produced in each industry, a number of time series pattern prediction studies are being conducted to make quick business decisions. However, there is a limit to predicting specific patterns in nonlinear time series data due to the uncertainty inherent in the data, and there are difficulties in making strategic decisions in corporate management. In addition, in recent decades, various studies have been conducted on data such as demand/supply and financial markets that are suitable for industrial purposes to predict time series data of irregular random walk models, but predict specific rules and achieve sustainable corporate objectives There are difficulties. In this study, the prediction results were compared and analyzed using the Chaos analysis method for roulette data and financial market data, and meaningful results were derived. And, this study confirmed that chaos analysis is useful for finding a new method in analyzing time series data. By comparing and analyzing the characteristics of roulette games with the time series of Korean stock index future, it was derived that predictive power can be improved if the trend is confirmed, and it is meaningful in determining whether nonlinear time series data with high uncertainty have a specific pattern.

• Electronics and Telecommunications Trends
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• v.31 no.5
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• pp.120-130
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• 2016

21세기 디지털 사회에서는 각 개인의 연결과 상호작용이 사회 현상에 막대한 영향을 미치고 있으며, 이로 인해 사회는 더욱 복잡해지고, 순간적으로 급격한 변화가 일어나는 현상이 빈번히 발생한다. 사회 복잡성(Social Complexity)은 다수의 사회 구성원들 간의 복잡한 연결로 인해 발생하는 상호작용 패턴을 의미한다. 2011년 발생한 '아랍의 봄' 혁명, 글로벌 금융위기, 조류독감의 발생과 확산, 글로벌 기후 변화, 테러리스트들의 네트워크와 사이버 범죄의 증가 등은 복잡하게 연결된 글로벌 사회 현상으로 발생하는 복잡성의 사례들이다. 우리 사회의 안전성과 지속성을 확보하기 위해, 이러한 사회 복잡성을 그려내어, 그 속에 숨겨진 패턴을 분석하고 예측하는 기술에 대한 연구 개발이 국가적으로 필요하다. 다양한 분야의 사회적 모형을 개발하고 서로 연결하여, 복잡한 사회적 상호작용을 대규모 시뮬레이션을 통해 현상 분석과 사회경제 정책적 대응방향에 대한 통찰력을 가질 수 있는 대규모 '모사현실(Simulated Reality)' 기술 개발을 서둘러야 한다.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.3
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• pp.155-164
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• 2016

For frequency pattern analysis of large databases, the new tree-based frequency pattern analysis algorithm which can compensate for the disadvantages of the Apriori method has been variously studied. In frequency pattern tree, the number of nodes is associated with memory allocation, but also affects memory resource consumption and processing speed of the growth. Therefore, reducing the number of nodes in the tree is very important in the frequency pattern mining. However, the absolute criteria which need to order the transaction items for construction frequency pattern tree has lowered the compression ratio of the tree nodes. But most of the frequency based tree construction methods adapted the absolute criteria. FP-tree is typically frequency pattern tree structure which is an extended prefix-tree structure for storing compressed frequent crucial information about frequent patterns. For construction the tree, all the frequent items in different transactions are sorted according to the absolute criteria, frequency descending order. CanTree also need to absolute criteria, canonical order, to construct the tree. In this paper, we proposed a novel frequency pattern tree construction method that does not use the absolute criteria, IRFP-tree algorithm. IRFP-tree(Intersection Rule based FP-tree). IRFP-tree is constituted with the new paradigm of the intersection rule without the use of the absolute criteria. It increased the compression ratio of the tree nodes, and reduced the tree construction time. Our method has the additional advantage that it provides incremental mining. The reported test result demonstrate the applicability and effectiveness of the proposed approach.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1195-1202
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• 2016

Recently, a lot of studies that applying the big data technology to various fields, are progressing actively. In the maritime domain, the big data is the meaningful information which makes and gathers by the navigation and communication equipment from the many ships on the ocean. Also, importance of the maritime safety is emphasized, because maritime accidents are rising with increasing of maritime traffic. To support prevention of maritime accidents, in this paper, we developed a vessel traffic display and statistic system based on AIS messages from the many vessels of maritime. Also, to verify the developed system, we conducted tests for vessel track display function and vessel traffic statistic function based on two test scenarios. Therefore, we verified the effectiveness of the developed system for vessel tracks display, abnormal navigation patterns, checking failure of AIS equipments and maritime traffic statistic analyses.