• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.2
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• pp.207-215
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• 2016

The demand for spatial data service technologies is increasing lately with the development of realistic 3D spatial information services and ICT (Information and Communication Technology). Research is being conducted on the real-time provision of spatial data services through a variety of mobile and Web-based contents. Big data or cloud computing can be presented as alternatives to the construction of spatial data for the effective use of large volumes of data. In this paper, the process of building HLM (Hyper Live Map) using multi-source data to acquire stereo CCTV and other various data is presented and a big data service architecture design is proposed for the use of flexible and scalable cloud computing to handle big data created by users through such media as social network services and black boxes. The provision of spatial data services in real time using big data and cloud computing will enable us to implement navigation systems, vehicle augmented reality, real-time 3D spatial information, and single picture based positioning above the single GPS level using low-cost image-based position recognition technology in the future. Furthermore, Big Data and Cloud Computing are also used for data collection and provision in U-City and Smart-City environment as well, and the big data service architecture will provide users with information in real time.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.406-412
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• 2022

Storing large amounts of data has always been a big problem from the beginning of computing history. Big Data has made huge advancements in improving business processes by finding the customers' needs using prediction models based on web and social media search. The main purpose of big data stream processing frameworks is to allow programmers to directly query the continuous stream without dealing with the lower-level mechanisms. In other words, programmers write the code to process streams using these runtime libraries (also called Stream Processing Engines). This is achieved by taking large volumes of data and analyzing them using Big Data frameworks. Streaming platforms are an emerging technology that deals with continuous streams of data. There are several streaming platforms of Big Data freely available on the Internet. However, selecting the most appropriate one is not easy for programmers. In this paper, we present a detailed description of two of the state-of-the-art and most popular streaming frameworks: Apache Ignite and Hazelcast. In addition, the performance of these frameworks is compared using selected attributes. Different types of databases are used in common to store the data. To process the data in real-time continuously, data streaming technologies are developed. With the development of today's large-scale distributed applications handling tons of data, these databases are not viable. Consequently, Big Data is introduced to store, process, and analyze data at a fast speed and also to deal with big users and data growth day by day.

• Journal of Engineering Education Research
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• v.25 no.3
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• pp.35-44
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• 2022

Artificial intelligence is a core technology in the era of digital transformation, and as the technology level is advanced and used in various industries, its influence is growing in various fields, including social, ethical and legal issues. Therefore, it is time to raise social awareness on ethics of artificial intelligence as a prevention measure as well as improvement of laws and institutional systems related to artificial intelligence development. In this study, we analyzed unstructured data, typically text, such as online news articles and comments to confirm the degree of social awareness on ethics of artificial intelligence development. The analysis showed that the public intended to concentrate on specific issues such as "Human," "Robot," and "President" in 2018 to 2019, while the public has been interested in the use of personal information and gender conflics in 2020 to 2021.

• The Journal of Society for e-Business Studies
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• v.22 no.1
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• pp.123-146
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• 2017

The information that consumers share in the SNS (Social Networking Service) has a great influence on the purchase of consumers. Therefore, it is necessary to pay attention to new research methodology and advertising strategy using Social Big Data. In this context, the purpose of this study is to quantitatively analyze customer value through Social Big Data. In this study, we analyzed the value structure of consumers for the three smartphone brands through text mining and positive/negative image analysis. Analysis result, it was possible to distinguish the emotional aspects (sensitivity) and rational aspects (rationality) for customer value per brand. In the case of the Galaxy S7 and iPhone 6S, emotional aspects were important before the launch, but the rational aspects was important after release date. On the other hand, in the case of the LG G5, emotional aspects were important before and after launch. We can propose two core advertising strategies based on analyzed consumer value. When developing advertising strategy in the case of the Galaxy S7, there is a need to emphasize the rational aspects of product attributes and differentiated functions. In the case of the LG G5, it is necessary to consider the emotional aspects of happiness, excitement, pleasure, and fun that are felt by using products in advertising strategy. As a result, this study will provide a good standard for actual advertising strategy through consumer value analysis. Advertising strategies are primarily driven by intuition or experience. Therefore, it is important to develop advertising strategies by analyzing consumer value through social big data analysis.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.593-600
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• 2018

Owing to the advancement of Internet and smart devices, access to various media such as social media became easy; thus, a large amount of big data is being produced. Particularly, the companies that provide various Internet services are analyzing the big data by using the MapReduce-based big data analysis techniques to investigate the customer preferences and patterns and strengthen the security. However, with MapReduce, when the big data is analyzed by defining the number of reducer objects generated in the reduce stage as one, the processing rate of big data analysis decreases. Therefore, in this paper, a MapReduce-based split big data analysis method is proposed to improve the log analysis processing rate. The proposed method separates the reducer partitioning stage and the analysis result combining stage and improves the big data processing rate by decreasing the bottleneck phenomenon by generating the number of reducer objects dynamically.

• Journal of the Korea Convergence Society
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• v.6 no.1
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• pp.85-91
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• 2015

Recently government has distributed precautionary measure and response procedures for smishing(SMS phishing), pharming, phishing, memory hacking and intensified Electronic Financial Transaction Act because of the sharp increase of electronic bank frauds. However, the methods of electronic bank frauds also developed and changed accordingly so much it becomes hard to cope with them. In contrast to earlier voice phishing targeted randomizing object, these new methods find out the personal information of targets and analyze them in detail making a big data base. And they are progressed into new kind of electronic bank frauds using those analyzed informations for voice phishing. This study analyze the attack method of voice phishing blended with the Big Data of personal informations and suggests response procedures for electronic bank frauds increasingly developed. Using the method to save meaningless data in a memory, attackers cannot deduct accurate information and try voice phishing properly even though they obtain personal information based on the Big Data. This study analyze newly developed social technologic attacks and suggests response procedures for them.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.6
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• pp.457-464
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• 2020

This study aims to recognize customers' real sentiment and then discover the data-driven insights for strategic decision-making in the financial sector of Saudi Arabia. The data was collected from the social media (Facebook and Twitter) from start till October 2018 in financial companies (NCB, Al Rajhi, and Bupa) selected in the Kingdom of Saudi Arabia according to criteria. Then, it was analyzed using a sentiment analysis, one of data mining techniques. All three companies have similar likes and followers as they serve customers as B2B and B2C companies. In addition, for Al Rajhi no negative sentiment was detected in English posts, while it can be seen that Internet penetration of both banks are higher than BUPA, rarely mentioned in few hours. This study helps to predict the overall popularity as well as the perception or real mood of people by identifying the positive and negative feelings or emotions behind customers' social media posts or messages. This research presents meaningful insights in data-driven approaches using a specific data mining technique as a tool for corporate decision-making and forecasting. Understanding what the key issues are from customers' perspective, it becomes possible to develop a better data-based global strategies to create a sustainable competitive advantage.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.01a
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• pp.283-284
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• 2015

We can picture a workforce that extends beyond your employees: one that consists of any user connected to the Internet. Cloud, social, and collaboration technologies now allow organizations to tap into vast pools of resources across the world, many of whom are motivated to help. Channeling these efforts to drive business goals is a challenge, but the opportunity is enormous: it can give every business access to an immense, agile workforce that is not only better suited to solving some of the problems that organizations struggle with today but in many cases will do it for free. In this paper, we research on factors to design an organizational crowd source for Big Data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.117-119
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• 2016

Various sites which provide big data analysis service do not show the interpretation of analysis results such as social trends and events but simple numeric results. In this point, this study attempts to suggest a way of interpretation on big data analysis results focusing on brand awareness and preference.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.143-153
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• 2013

Big data is expected to bring forth enormous public good as well as economic opportunity. However there is ongoing concern about privacy not only from public authorities but also from private enterprises. Big data is suspected to aggravate the existing privacy battle ground by introducing new types of privacy risks such as privacy risk of behavioral pattern. On the other hand, big data is asserted to become a new way to by-pass tradition behavioral tracking such as cookies, DPIs, finger printing${\cdots}$ and etc. For it is not based on a targeted person. This paper is to find out if big data could contribute to catching out behavioral patterns of consumers without threatening or damaging their privacy. The difference between traditional behavioral tracking and big data analysis from the perspective of privacy will be discerned.