• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.27-35
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• 2017

In near future, IoT data is expected to be a major portion of Big Data. Moreover, sensor data is expected to be major portion of IoT data, and its' research is actively carried out currently. However, processed results may not be trusted and used if outlier data is included in the processing of sensor data. Therefore, method for detection and deletion of those outlier data before processing is studied in this paper. Moreover, we used Spark which is memory based distributed processing environment for fast processing of big sensor data. The detection and deletion of outlier data consist of four stages, and each stage is implemented with Mapper and Reducer operation. The proposed method is compared in three different processing environments, and it is expected that the outlier detection and deletion performance is best in the distributed Spark environment as data volume is increasing.

• Convergence Security Journal
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• v.15 no.4
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• pp.113-119
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• 2015

Recent, cyber violence is increasing in a school and the severity of the problems encountered day by day. In particular, the severity of the cyber force using the smart phone is recognized as a very high and great problems socially. Cyberbullying have long damage degree and a wide range time duration against of existed physical cyber violence. Then student's affects is very seriously. Therefore, we analyzes the relationship and languages in the classroom for students to use to identify signs of cyber violence that may occur between friends in the class. And we support this information to identified parent, classroom teachers and school sheriff for prevent cyberbullying accidents in the school. For this research, we will design and implement a messenger in the cyber classroom. It have many components that are Big-data vocabulary, analyzer, and communication interface. Our proposed messenger can analyze lingual sign and friendship between students using Big-data analysis method such as text mining. It can analysis relationship by per-student, per-classroom.

• Knowledge Management Research
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• v.21 no.3
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• pp.215-228
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• 2020

This study introduces the case of a company that newly jumped into the competitive cosmetics market with a brand concept developed through big data analysis. Skin Reverse Lab, which possesses anti-aging material technology, launched a new brand in the skincare cosmetics market. Using a big data analysis program called Luminoso, SNS data was analyzed in four areas, which were consumer attitudes toward overall cosmetics, skincare products, competitors, and consumers' experiences of product use. The age groups and competitors were analyzed through the emotional analysis technique including context, which is the strength of Luminoso, and insights on consumers were derived through the related word analysis and word cloud techniques. Based on the analysis results, Logically Skin have won various awards in famous magazines and apps, and have been recognized as products that meet global trend standards. Besides, it has entered six countries including the United States and Hong Kong. The Logically Skin case is a case in which a new company entered the market with a new brand by deriving consumer insights only from external data, and it is significant as a case of applying AI-based sentiment analysis.

• School Mathematics
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• v.18 no.4
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• pp.793-818
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• 2016

The purpose of this study is to explore in detail $5^{th}$ grade students' understanding on the big ideas related to addition of fraction with different denominators: fixed whole unit, necessity of common measure, and recursive partitioning connected to algorithms. We conducted teaching experiments on 15 fifth grade students who had learned about addition of fractions with different denominators using the current textbook. Most students approached to the big ideas related to addition of fractions in a procedural way. However, some students were able to conceptually understand the interpretations and algorithms of fraction addition by quantitatively thinking about the context and focusing on the structures of units. Building on these results, this study is expected to suggest specific implications on instruction methods for addition of fractions with different denominators.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.989-1009
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• 2018

Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed 'big data'. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.

• Journal of the Korean Society of Clothing and Textiles
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• v.35 no.12
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• pp.1454-1465
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• 2011

Menswear body types are often labeled on garments (to indicate how the garments are designed to fit) with indicators of a size category such as regular, portly, and stout, athletic, or big and tall. A drop (relationships between the chest and waist girths) is related to the fit of a tailored suit. However, current standards are not designed for various drops or body types. There is not enough information of categorizing men's body shapes for the apparel sizing systems. In this article, a set of men's data from SizeUSA sizing survey was analyzed to investigate men's body shapes and drops. Factor analysis and a cluster analysis method were used to categorize men's body shapes. In the results, twenty-five variables were selected through the factor analysis and found four factors: girth factor, height factor, torso girth factor, and slope degree factor. According to the factor and cluster analysis, various body shapes were found: Slim Shape (SS - tall ectomorphy), Heavy Shape (HS - athletic, big & tall, endomorphy and mesomorphy), Slant Inverted Triangle Shape (SITS - regular, slight ectomorphy and slight mesomorphy weight range from normal to slightly overweight), Short Round Top Shape (SRTS - portly and stout, endomorphy). Body shapes were related to fitting categories. SS and HS were related to big & tall fitting category. SITS was related to regular. SRTS was related to portly and stout. Shape 1 (31%) and Shape 2 (26%) were related to current big & tall category. Shape 3 (34%) were related to regular. Shape 4 (9%) were in portly and stout category. ASTM D 6240 standard was the only available standard that presented a regular fitting category. Various drops were found within a same chest size group; however, this study revealed great variances of drops by body shape.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.57-62
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• 2014

In this paper, we proposed and implemented a crime pattern analysis algorithm using big data. The proposed algorithm uses crime-related big data collected and published in the supreme prosecutors' office. The algorithm analyzed crime patterns in Seoul city from 2011 to 2013 using the spatial statistics analysis like the standard deviational ellipse and spatial density analysis. Using crime frequency, We calculated the crime probability and danger factors of crime areas, time, date, and places. Through a result we analyzed spatial statistics. As the result of the proposed algorithm, we could grasp differences in crime patterns of Seoul city, and we calculated degree of risk through analysis of crime pattern and danger factor.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.11
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• pp.2629-2636
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• 2015

Recently Big Data management have a rapid increases interest in IT field, much research conducting to solve a problem of real-time processing to Big Data. Lots of resources are required for the ability to store data in real-time over the network but there is the problem of introducing an analyzing system due to aspect of high cost. Need of redesign of the system for low cost and high efficiency had been increasing to solve the problem. In this paper, the document type of database, MongoDB, is used for design a log management system based a document type of database, that is good at big data managing. The suggested log management system is more efficient than other method on log collection and processing, and it is strong on data forgery through the performance evaluation.

• Journal of Convergence for Information Technology
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• v.9 no.11
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• pp.8-14
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• 2019

Due to the development of computer technology, IoT technology is being used in various fields of industry, economy, medical service and education. However, multimedia information processed through IoT equipment is still one of the major issues in the application sector. In this paper, a big data protection model for users of IoT based IoT is proposed to ensure integrity of users' multimedia information processed through IoT equipment. The proposed model aims to prevent users' illegal exploitation of big data information collected through IoT equipment without users' consent. The proposed model uses signatures and authentication information for IoT users in a hybrid cryptographic method. The proposed model feature ensuring integrity and confidentiality of users' big data collected through IoT equipment. In addition, the user's big data is not abused without the user's consent because the user's signature information is encrypted using a steganography-based cryptography-based encryption technique.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.183-188
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• 2020

This study is primarily aimed at providing a foundation for academic development and the leap forward of the Special Security Industry through the press report analysis on Korea's special security guard using big data. The research methods It was analyzed by the research methods in relation to keyword trends for 'special security guard' and 'special guards' using the Big Kinds program. According to the analysis based on the period of growth (quantitative and qualitative) of the special security industry, there were many press reports and exposure related to carrying firearms, national major facilities, and regular employees. Unlike the general security guards, the special security guards were released higher by media as a law was revised to allow them to carry or use firearms at important national facilities. There was a lot of media attention concerned about the side effects of misuse, and there were also high media reports on the transition of regular workers to improve poor treatment, such as the unstable status of special security guards and low wages. Therefore, the need for continuous development and improvement of professionalism and work efficiency of special security services are emphasized.