• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.1
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• pp.76-81
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• 2018

The Internet of Things (IoT), which is emerging as a future economic growth engine, has been actively introduced in areas close to our daily lives. However, there are still IoT security threats that need to be resolved. In particular, with the spread of smart homes and smart cities, an explosive amount of closed-circuit televisions (CCTVs) have been installed. The Internet protocol (IP) information and even port numbers assigned to CCTVs are open to the public via search engines of web portals or on social media platforms, such as Facebook and Twitter; even with simple tools these pieces of information can be easily hacked. For this reason, a big-data analytics system is needed, capable of supporting quick responses against data, that can potentially contain risk factors to security or illegal websites that may cause social problems, by assisting in analyzing data collected by search engines and social media platforms, frequently utilized by Internet users, as well as data on illegal websites.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.265-272
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• 2019

In this study, we conducted a prediction study to qualitatively identify the continuous growth rate that causes problems every year for deposit bank mortgage loans, identify the characteristic factors that could once again stabilize, and come up with measures for future quantitative analysis of mortgage loans and growth trends. Based on data analysis using the R program, which is widely used for big data analysis, the parameters of ARIMA model (0.1,1)(0.1,1)[12] were found to be most suitable. In these indicators, estimates over the next five years (60 months) increased 4.5% on average. However, this has limitations that do not reflect socio-environmental factors, which require further study of these limitations.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.2
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• pp.33-40
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• 2016

Due to the growth of social network systems (SNS), big data are realized and Hadoop was developed as a distributed platform for analyzing big data. Enterprises analyze data containing users' sensitive information by using Hadoop and utilize them for marketing. Therefore, researches on data encryption have been done to protect the leakage of sensitive data stored in Hadoop. However, the existing researches support only the AES encryption algorithm, the international standard of data encryption. Meanwhile, Korean government choose ARIA algorithm as a standard data encryption one. In this paper, we propose a HDFS data encryption scheme using ARIA algorithms on Hadoop. First, the proposed scheme provide a HDFS block splitting component which performs ARIA encryption and decryption under the distributed computing environment of Hadoop. Second, the proposed scheme also provide a variable-length data processing component which performs encryption and decryption by adding dummy data, in case when the last block of data does not contains 128 bit data. Finally, we show from performance analysis that our proposed scheme can be effectively used for both text string processing applications and science data analysis applications.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.1-23
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• 2019

Big data refers to data that is difficult to store, manage, and analyze by existing software. As the lifestyle changes of consumers increase the size and types of needs that consumers desire, they are investing a lot of time and money to understand the needs of consumers. Companies in various industries utilize Big Data to improve their products and services to meet their needs, analyze unstructured data, and respond to real-time responses to products and services. The financial industry operates a decision support system that uses financial data to develop financial products and manage customer risks. The use of big data by financial institutions can effectively create added value of the value chain, and it is possible to develop a more advanced customer relationship management strategy. Financial institutions can utilize the purchase data and unstructured data generated by the credit card, and it becomes possible to confirm and satisfy the customer's desire. CRM has a granular process that can be measured in real time as it grows with information knowledge systems. With the development of information service and CRM, the platform has change and it has become possible to meet consumer needs in various environments. Recently, as the needs of consumers have diversified, more companies are providing systematic marketing services using data mining and advanced CRM (Customer Relationship Management) techniques. KB Kookmin Card, which started as a credit card business in 1980, introduced early stabilization of processes and computer systems, and actively participated in introducing new technologies and systems. In 2011, the bank and credit card companies separated, leading the 'Hye-dam Card' and 'One Card' markets, which were deviated from the existing concept. In 2017, the total use of domestic credit cards and check cards grew by 5.6% year-on-year to 886 trillion won. In 2018, we received a long-term rating of AA + as a result of our credit card evaluation. We confirmed that our credit rating was at the top of the list through effective marketing strategies and services. At present, Kookmin Card emphasizes strategies to meet the individual needs of customers and to maximize the lifetime value of consumers by utilizing payment data of customers. KB Kookmin Card combines internal and external big data and conducts marketing in real time or builds a system for monitoring. KB Kookmin Card has built a marketing system that detects realtime behavior using big data such as visiting the homepage and purchasing history by using the customer card information. It is designed to enable customers to capture action events in real time and execute marketing by utilizing the stores, locations, amounts, usage pattern, etc. of the card transactions. We have created more than 280 different scenarios based on the customer's life cycle and are conducting marketing plans to accommodate various customer groups in real time. We operate a smart offering system, which is a highly efficient marketing management system that detects customers' card usage, customer behavior, and location information in real time, and provides further refinement services by combining with various apps. This study aims to identify the traditional CRM to the current CRM strategy through the process of changing the CRM strategy. Finally, I will confirm the current CRM strategy through KB Kookmin card's big data utilization strategy and marketing activities and propose a marketing plan for KB Kookmin card's future CRM strategy. KB Kookmin Card should invest in securing ICT technology and human resources, which are becoming more sophisticated for the success and continuous growth of smart offering system. It is necessary to establish a strategy for securing profit from a long-term perspective and systematically proceed. Especially, in the current situation where privacy violation and personal information leakage issues are being addressed, efforts should be made to induce customers' recognition of marketing using customer information and to form corporate image emphasizing security.

• Smart Media Journal
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• v.8 no.1
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• pp.59-66
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• 2019

The importance of data is increasing at a high rate as data is massively generated and taken into account in various policy supports and contents. However, because of their speed of growth, it is difficult to find the data that is needed. Both the methodological elements that summarize the data and the technical elements of the visualization that help to see at a glance are important. This paper summarizes data visualization methods to improve the currently used design - oriented infographics and propose data - centric infographics. In addition, we will present examples of data analysis and infographics production using Tableau Public. The Gwangju metropolitan city bus user data was used for infographics production, and the results show that the total number of passengers using the stopping point is similar to that of the general passengers, while it is different from the numbers of transit passengers and teen riding-and-transit passengers. Data-centric infographics visualization, unlike existing infographics that is pronounced only as a visual role, is expected to be used as a tool for scientific research as well as efficiently delivering data.

• Korean Journal of Human Ecology
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• v.15 no.4
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• pp.651-658
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• 2006

This study was performed to provide the analysis on their size and the structural factors required in the process of design and manufacture of school girls' shoes. 371 elementary school girls in Kyungin and Youngnam area were participated in the size measurement. 25 foot items and 6 main body items were measured directly or indirectly using a digital photography. The results of the study are as follows: first, by most of measured items, the range of their foot size was very wide from the size of toddlers to adults'. That shows that the change of school girls' foot size occurred with their growth is pretty big. Second, from the structural factor analysis on 25 foot items, five factors were extracted such as 'the size of the foot', 'the volume of the foot,' 'the height and inclination of the foot,' 'the shape of the foot,' and 'the inside and outside inclination of the foot'. Third, from the cluster analysis, three clusters were classified: Cluster 1 was the group of 10 to 11 year old girls who had big-sized feet. The elementary school girls in the fourth to sixth grade belonged to this group. Cluster 2 consisted of girls who had small-sized and big-volumed feet. Cluster 3 had medium-sized and slim-shaped feet. Most of 6 to 7 year old elementary school girls belonged to this group. The above-mentioned results imply that many continual researches are required on children's shoe production reflecting the change of elementary school girls' feet size owing to their growth. The quantitative data on elementary school girls' feet size in this study could be used as basic information for the development of children's shoe design and its production.

• The Journal of the Korea Contents Association
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• v.17 no.7
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• pp.443-454
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• 2017

A positive destination image has an impact on the tourist arrivals and economic growth of the tourist destination. Recently, the content generated by sharing tourist experiences and destination information on the internet has been increasing. The online content has the potential to become a major tourist decision source and provide more in-depth materials and richer content to extract destination image, insight and tourist's perceptions of the destination. This study was designed to explore the destination image of Daegu online and draw lessons for successful image management in an era of big data. Text mining approach and social network analysis were conducted to extract destination image determining elements and assess the influence of the elements. The result showed that destination image elements related to tourist infra-structures and culture, history and art affected the overall destination image of Daegu. Destination marketers should make an effort to grasp these precise destination image and seek ways to boost competitiveness as a tourist destination.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.175-181
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• 2014

With rapid growth of the wireless mobile computing network technologies, various mobile ad hoc network applications converged with other related technologies are rapidly disseminated nowadays. Vehicular Ad Hoc Networks are self-organizing mobile ad hoc networks that typically have moving vehicle nodes with high speeds and maintaining its topology very short with unstable communication links. Therefore, VANETs are very vulnerable for the malicious noise of sensors and anomalies of the nodes in the network system. In this paper, we propose an anomaly detection method by using big data techniques that efficiently identify malicious behaviors or noises of sensors and anomalies of vehicle node activities in these VANETs, and the performance of the proposed scheme is evaluated by a simulation study in terms of anomaly detection rate and false alarm rate for the threshold ${\epsilon}$.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.289-295
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• 2016

Subway is a future-oriented means of transportation that can be safely and quickly mass transport many passengers than buses and taxis. Congestion growth due to the increase of the metro users is one of the factors that hinder citizens' rights to comfortably use the subway. Accordingly, congestion prediction in the subway is one of the ways to maximize the use of passenger convenience and comfort. In this paper, we monitor the level of congestion in real time via the existing congestion on the metro using multiple regression analysis and big data processing, as well as their departure station and arrival station information More information about the transfer stations offer a personalized congestion prediction system. The accuracy of the predicted congestion shows about 81% accuracy, which is compared to the real congestion. In this paper, the proposed prediction and recommendation application will be a help to prediction of subway congestion and user convenience.

• Journal of the Korean Regional Science Association
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• v.39 no.1
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• pp.21-36
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• 2023

Entering the era of low growth due to changes in social and economic conditions, most cities across the country are actively promoting urban regeneration. Although urban regeneration is a project with huge national finances, a clear evaluation system has not yet been established. In order to ensure the sustainability of urban regeneration, it is necessary to secure the validity of urban regeneration policies and establish a reflux system to supplement the policies. The purpose of this study is to derive the limitations of the existing comprehensive performance evaluation and to develop an improved urban regeneration policy comprehensive performance evaluation technique based on spatial big data. The urban regeneration comprehensive performance evaluation technique differentiated the areas affected by the urban regeneration project and the surrounding areas based on the type of urban regeneration project and the presence or absence of large cities and middle cities. The effects of urban regeneration were quantitatively verified through relative comparison between the areas affected by urban regeneration projects and the surrounding areas of population, society, economy, industry, physical and environmental evaluation indicators.