• Journal of Communications and Networks
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• v.17 no.6
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• pp.549-557
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• 2015

Mobile cellular networks are becoming increasingly complex to manage while classical deployment/optimization techniques and current solutions (i.e., cell densification, acquiring more spectrum, etc.) are cost-ineffective and thus seen as stopgaps. This calls for development of novel approaches that leverage recent advances in storage/memory, context-awareness, edge/cloud computing, and falls into framework of big data. However, the big data by itself is yet another complex phenomena to handle and comes with its notorious 4V: Velocity, voracity, volume, and variety. In this work, we address these issues in optimization of 5G wireless networks via the notion of proactive caching at the base stations. In particular, we investigate the gains of proactive caching in terms of backhaul offloadings and request satisfactions, while tackling the large-amount of available data for content popularity estimation. In order to estimate the content popularity, we first collect users' mobile traffic data from a Turkish telecom operator from several base stations in hours of time interval. Then, an analysis is carried out locally on a big data platformand the gains of proactive caching at the base stations are investigated via numerical simulations. It turns out that several gains are possible depending on the level of available information and storage size. For instance, with 10% of content ratings and 15.4Gbyte of storage size (87%of total catalog size), proactive caching achieves 100% of request satisfaction and offloads 98% of the backhaul when considering 16 base stations.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.201-208
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• 2015

With services such as SNS and facebook, Big Data popularize the use of small size such as micro blogs are increasing. However, the problem of accuracy and computational cost of the search result of big data of a small size is unresolved. In this paper, we propose a subnet selection techniques based probability to improve the browsing speed of the small size of the text information from big data environments, such as micro-blogs. The proposed method is to configure the subnets to give to the attribute information of the data increased the probability data search speed. In addition, the proposed method improves the accessibility of the data by processing a pair of the connection information between the probability of the data constituting the subnet to easily access the distributed data. Experimental results showed the proposed method is 6.8% higher detection rates than CELF algorithm, the average processing time was reduced by 8.2%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.6
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• pp.1567-1575
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• 1996

This paper presents a new power control method for compensating the short term fading of the reverse link channel in the CDMA mobile telephone system. The fixed step closed loop power control which is now adopted in IS-95, is very simple in structure. However, the step size in the closed loop power control is too big for the channel with a small variation or too big for the channel with a small variation or too small for the channel with a large variation. The method presented in this paper has a simple structure and shows a new model employing the combination of the fixed step size method and variable step size method which results in compensatingthe disadvantages mentioned above. This paper also evaluates the performance inthe fundamental channel model.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.253-253
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• 2015

This research evaluated the effect of two main sources on the results of the ocean regional circulation model (ORCMs) during downscaling and nesting the results from the coarse data. The two sources should be the domain size, and temporal and spatial resolution different between driving and driven data. The Big-Brother Experiment is applied to examine the impact of them on the results of the ORCMs separately. Within resolution of 3km grid point ORCMs applying in the Big-Brother Experiment framework, it showed that the simulation results of the ORCMs depend on the domain size and specially the spatial and temporal resolution of lateral boundary conditions (LBCs). The domain size can be selected at 9.5 times larger than the interest area, and the spatial resolution between driving data and driven model can be up to 3 of ratio resolution and updating frequency of the LBCs can be up to every 6 hours per day.

• Polymer(Korea)
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• v.28 no.2
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• pp.170-176
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• 2004

The effect of reaction conditions on the size and size distribution of superparamagnetic iron oxide coated with siloxane was big investigated by using dynamic light scattering. The hydrogen bond between the hydroxyl groups on tile surface of the magnetite and silanol was confirmed by FT-IR. The size of nanoparticles increased with the reaction temperature, but decreased with monomer contents and agitation speeds. There was not a big difference in size of nanoparticles, prepared by different reaction conditions, but its distribution was in the range of 14∼41nm. All samples exhibited the superparamagnetic nature. The magnetic susceptibility of the nanoparticles increased with the reaction temperature while it decreased with the monomer content and agitation speed.

• Journal of Digital Convergence
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• v.17 no.6
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• pp.365-371
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• 2019

In this study, VR contents were produced by using the rose which is the material of the tomb of the surrealistic work wrestler of Rene Magritte, an artistic genre, as a motive. In conclusion, the distortion (spatial modulation) of the image scale is connected to the dynamic-curve and texture-soft areas, and the superposition (combination of contradictory images) is called the big-size, irregular-depth area, Are connected to the positions of big-size and irregular-space regions. The theme of the work was Dream, and the plants and roses patterns were produced in each timeline, and overlap, scale, distortion, overlap, distortion, and scale were used.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.4
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• pp.197-212
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• 2019

The purpose of this study is to identify the maturity stages of venture firms through classification analysis, which is widely used as a big data technique. Venture companies should develop a competitive advantage in the market. And the maturity stage of a company can be classified into five stages. I will analyze a difference in the growth stage of venture firms between the survey response and the statistical classification methods. The firm growth level distinguished five stages and was divided into the period of start-up and declines. A classification method of big data uses popularly k-mean cluster analysis, hierarchical cluster analysis, artificial neural network, and decision tree analysis. I used variables that asset increase, capital increase, sales increase, operating profit increase, R&D investment increase, operation period and retirement number. The research results, each big data analysis technique showed a large difference of samples sized in the group. In particular, the decision tree and neural networks' methods were classified as three groups rather than five groups. The groups size of all classification analysis was all different by the big data analysis methods. Furthermore, according to the variables' selection and the sample size may be dissimilar results. Also, each classed group showed a number of competitive differences. The research implication is that an analysts need to interpret statistics through management theory in order to interpret classification of big data results correctly. In addition, the choice of classification analysis should be determined by considering not only management theory but also practical experience. Finally, the growth of venture firms needs to be examined by time-series analysis and closely monitored by individual firms. And, future research will need to include significant variables of the company's maturity stages.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.165-166
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• 2019

Big data analysis is the ability to collect, store, manage and analyze data from existing database management tools. Big data refers to large scale data that is generated in a digital environment, is large in size, has a short generation cycle, and includes not only numeric data but also text and image data. Big data is data that is difficult to manage and analyze in the conventional way. It has huge size, various types, fast generation and velocity. Therefore, companies in most industries are making efforts to create value through the application of Big data. In this study, we analyzed the meaning of keyword using Social Matrix, a big data analysis tool of Daum communications. Also, the theoretical implications are presented based on the analysis results.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.104-106
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• 2004

There are many under grounds facilities like oil pipes, gas pipes, water pipes, oil tanks, etc. and severe corrosion of these facilities made big problems. Fire, wide area water and soil pollution, massive and hazardous explosion, etc. can make big problems and cause big economical loss. So, various technologies were developed to keep these undergrouns facilities safely, and cathodic protection is one of it. For cathodic protection, one must detect potential of pipes, and there are so many test box to check pipes potentials. In this thesis, we describe on the development of small size corrosion monitoring system that measure pipes potentials easily and economically.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.91-112
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• 2019

The IoT environment has led to explosive growth of existing enterprise data, and how to utilize such big data is becoming an important issue in the management field. In this paper, major factors affecting the decisions of companies to utilize big data have been studied. And also, the effect of big data utilization on the management quality is studied empirically. During this process, we have studied the difference according to the award of Korean national quality award. As a result of the study, we confirmed that the five factors such as cost from technology, organization and environment perspective, compatibility, company size, chief officer support, and competitor pressure are key factors influencing big data utilization. Also, it was confirmed that the use of big data for management activities has an important influence on the six management quality factors based on MBNQA, and that the management quality level of Korean national quality award companies is relatively high. This paper provides practical implications for companies' use of big data because it demonstrates for the first time that big data utilization has an impact on management quality improvement.