• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.218-223
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• 2021

The five elements of big data are said to be Volume, Variety, Velocity, Veracity, and Value. Among them, data lacking the Veracity of the data or fake data not only makes an error in decision making, but also hinders the creation of value. This study analyzed YouTube's revenue structure to focus the effect of data integrity on data valuation among these five factors. YouTube is one of the OTT service platforms, and due to COVID-19 in 2020, YouTube creators have emerged as a new profession. Among the revenue-generating models provided by YouTube, the process of generating advertising revenue based on click-based playback was analyzed. And, analyzed the process of subtracting the profits generated from invalid activities that not the clicks due to viewers' pure interests, then paying the final revenue. The invalid activity in YouTube's revenue structure is Raw Data, not pure viewing activity of viewers, and it was confirmed a direct impact on revenue generation. Through the analysis of this process, the new Data Value Chain was proposed.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.265-274
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• 2017

This paper examines the problems of big data analysis education and suggests ways to solve them. Big data is a trend that the characteristic of big data is evolving from V3 to V5. For this reason, big data analysis education must take V5 into account. Because increased uncertainty can increase the risk of data analysis, internal and external structured/semi-structured data as well as disturbance factors should be analyzed to improve the reliability of the data. And when using opinion mining, error that is easy to perceive is variability and veracity. The veracity of the data can be increased when data analysis is performed against uncertain situations created by various variables and options. It is the node analysis of the textom(텍스톰) and NodeXL that students and researchers mainly use in the analysis of the association network. Social network analysis should be able to get meaningful results and predict future by analyzing the current situation based on dark data gained.

• Journal of Wetlands Research
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• v.18 no.1
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• pp.100-112
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• 2016

In recent, the direction of water resources policy is changing from the typical plan for water use and flood control to the sustainable water resources management to improve the quality of life. This change makes the information related to water resources such as data collection, management, and supply is becoming an important concern for decision making of water resources policy. We had analyzed the structured data according to the purpose of providing information on water resources. However, the recent trend is big data and cloud computing which can create new values by linking unstructured data with structured data. Therefore, the trend for the management of water resources information is also changing. According to the paradigm change of information management, this study tried to suggest an application of big data and cloud computing in water resources field for efficient management and use of water. We examined the current state and direction of policy related to water resources information in Korea and an other country. Then we connected volume, velocity and variety which are the three basic components of big data with veracity and value which are additionally mentioned recently. And we discussed the rapid and flexible countermeasures about changes of consumer and increasing big data related to water resources via cloud computing. In the future, the management of water resources information should go to the direction which can enhance the value(Value) of water resources information by big data and cloud computing based on the amount of data(Volume), the speed of data processing(Velocity), the number of types of data(Variety). Also it should enhance the value(Value) of water resources information by the fusion of water and other areas and by the production of accurate information(Veracity) required for water management and prevention of disaster and for protection of life and property.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3868-3888
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• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.

• Knowledge Management Research
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• v.24 no.1
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• pp.51-72
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• 2023

Digital literacy is vital knowledge and ability of an individual in the information society. As the level of digital literacy increases, the interest in privacy protection increases. This change may hinder the use of digital technologies and services. This research examines (1) the mediating effect of online social participation and information veracity on smart device literacy and privacy concerns, and (2) the moderating effect of privacy literacy. Using Korean media panel survey data reported in 2020 and in 2021, this study analyzes the responses of 7,737 people who use smart devices and participate in online activities. SPSS and PROCESS Macro are used to test the research model and hypotheses. In the analysis of 2020 and 2021 survey, this research shows that smart device literacy has major effects on privacy concerns; confirms that the mediating effect of online social participation; moderated meditating effect of privacy literacy. Although information veracity is not significant in 2020, mediating and moderated mediating effects are found in 2021.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.413-414
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• 2019

빅데이터는 초기에는 개념적인 접근으로 대용량의 데이터로 정의하기도 하였으나 지금은 데이터를 수집, 저장, 처리, 분석하여 가치 창출까지의 개념으로 확산되고, 최근에는 정확성(Veracity), 가변성(Variability), 시각화(Visualization) 개념까지 새롭게 추가되어 7V로 제시되기도 한다.

• Spatial Information Research
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• v.22 no.6
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• pp.13-21
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• 2014

This study defines concept and service framework of Geo-Spatial Big Data(GSBD). The major concept of the GSBD is formulated based on the 7V characteristics: the general characteristics of big data with 3V(Volume, Variety, Velocity); Geo-spatial oriented characteristics with 4V(Veracity, Visualization, Versatile, Value). GSBD is the technology to extract meaningful information from Geo-spatial fusion data and support decision making responding with rapidly changing activities by analysing with almost realtime solutions while efficiently collecting, storing and managing structured, semi-structured or unstructured big data. The application area of the GSBD is segmented in terms of technical aspect(store, manage, analyze and service) and public/private area. The service framework for the GSBD composed of modules to manage, contain and monitor GSBD services is suggested. Such additional studies as building specific application service models and formulating service delivery strategies for the GSBD are required based on the services framework.

• Spatial Information Research
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• v.23 no.6
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• pp.109-116
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• 2015

In this study, define the concept of spatial big data and special feature of spatial big data, examine information visualization methodology for increase the insight into the data. Also presented problems and solutions in the visualization process. Spatial big data is defined as a result of quantitative expansion from spatial information and qualitative expansion from big data. Characteristics of spatial big data id defined as 6V (Volume, Variety, Velocity, Value, Veracity, Visualization), As the utilization and service aspects of spatial big data at issue, visualization of spatial big data has received attention for provide insight into the spatial big data to improve the data value. Methods of information visualization is organized in a variety of ways through Matthias, Ben, information design textbook, etc, but visualization of the spatial big data will go through the process of organizing data in the target because of the vast amounts of raw data, need to extract information from data for want delivered to user. The extracted information is used efficient visual representation of the characteristic, The large amounts of data representing visually can not provide accurate information to user, need to data reduction methods such as filtering, sampling, data binning, clustering.

• Spatial Information Research
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• v.21 no.5
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• pp.43-51
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• 2013

In this study, the concept of spatial big data and effective ways to build a spatial big data system are presented. Big Data is defined as 3V(volume, variety, velocity). Spatial big data is the basis for evolution from 3V's big data to 6V's big data(volume, variety, velocity, value, veracity, visualization). In order to build an effective spatial big data, spatial big data system building should be promoted. In addition, spatial big data system should be performed a national spatial information base, convergence platform, service providers, and providers as a factor of production. The spatial big data system is made up of infrastructure(hardware), technology (software), spatial big data(data), human resources, law etc. The goals for the spatial big data system build are spatial-based policy support, spatial big data platform based industries enable, spatial big data fusion-based composition, spatial active in social issues. Strategies for achieving the objectives are build the government-wide cooperation, new industry creation and activation, and spatial big data platform built, technologies competitiveness of spatial big data.

• Journal of KIISE
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• v.44 no.1
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• pp.84-94
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• 2017

Recently, the importance of velocity, one of the characteristics of big data (5V: Volume, Variety, Velocity, Veracity, and Value), has been emphasized in the data processing, which has led to several studies on the real-time stream processing, a technology for quick and accurate processing and analyses of big data. In this paper, we propose a Squall framework using Time-triggered Message-triggered Object (TMO) technology, a model that is widely used for processing real-time big data. Moreover, we provide a description of Squall framework and its operations under a single node. TMO is an object model that supports the non-regular real-time processing method for certain conditions as well as regular periodic processing for certain amount of time. A Squall framework can support the real-time event stream of big data and micro-batch processing with outstanding performances, as compared to Apache storm and Spark Streaming. However, additional development for processing real-time stream under multiple nodes that is common under most frameworks is needed. In conclusion, the advantages of a TMO model can overcome the drawbacks of Apache storm or Spark Streaming in the processing of real-time big data. The TMO model has potential as a useful model in real-time big data processing.