• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.96-98
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• 2016

Recently, big data technology develop due to the rapid data generation. Thus big data analysis tools for analyzing big data has been developed. Typical big data tools are the R program, Hive, Tajo and more. But data analysis based on Matlab is still common used. And it is still used in big data analysis. In this paper, it research into big data analysis system based on the Matlab for analyzing vital signals.

• Journal of the Korean Society of Systems Engineering
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• v.11 no.2
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• pp.13-29
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• 2015

With the era of big data, the big data has been expected to have a large impact in the NPP safety areas. Although high interests of the big data for the NPP safety, only a limited researches concerning this issue are revealed. Especially, researches on the logical/physical structure and systematic design methods for the big data platform for the NPP safety were not dealt with. In this research, we design a new big data pilot platform for the NPP safety especially focusing on health monitoring and early warning services. For this, we propose a tailored design process based on SE approaches to manage inherent high complexities of the platform design. The proposed design process is consist of several steps from elicitate stakeholders to integration test via define operational concept and scenarios, and system requirements, design a conceptual functional architecture, select alternative physical modules for the derived functions and assess the applicability of the alternative modules, design a conceptual physical architecture, implement and integrate the physical modules. From the design process, this paper covers until the conceptual physical architecture design. In the following paper, the rest of the design process and results of the field test will be shown.

• Journal of information and communication convergence engineering
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• v.16 no.2
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• pp.99-105
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• 2018

k-Nearest neighbor join (k-NN Join) is a computationally intensive algorithm that is designed to find k-nearest neighbors from a dataset S for every object in another dataset R. Most related studies on k-NN Join are based on single-computer operations. As the data dimensions and data volume increase, running the k-NN Join algorithm on a single computer cannot generate results quickly. To solve this scalability problem, we introduce the locality-sensitive hashing (LSH) k-NN Join algorithm implemented in Spark, an approach for high-dimensional big data. LSH is used to map similar data onto the same bucket, which can reduce the data search scope. In order to achieve parallel implementation of the algorithm on multiple computers, the Spark framework is used to accelerate the computation of distances between objects in a cluster. Results show that our proposed approach is fast and accurate for high-dimensional and big data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.12
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• pp.2305-2311
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• 2017

In recent years, there have been tries to open public data from various government agencies along with publicization of public information for the public interest. In other words, various kinds of electronic data generated and collected by the public institutions as a result of their work are opened in the public portal sites. However, users who use it are limited in their use of big data due to lack of understanding of data format, lack of data processing knowledge, difficulty in accessing and managing data, and lack of visualization data to understand collected and stored data. Therefore, in this study, we propose a big data collection, storing and visualization platform that can collect big data provided by various public sites using data set URL and API regardless of data format, re-process collected data using XML structure.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.290-296
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• 2022

Big Data is a data analysis technology empowered by late advances in innovations and engineering. In any case, big data involves a colossal responsibility of equipment and handling assets, making reception expenses of big data innovation restrictive to little and medium estimated organizations. Cloud computing offers the guarantee of big data execution to little and medium measured organizations. Big Data preparing is performed through a programming worldview known as MapReduce. Normally, execution of the MapReduce worldview requires organized joined stockpiling and equal preparing. The computing needs of MapReduce writing computer programs are frequently past what little and medium measured business can submit. Cloud computing is on-request network admittance to computing assets, given by an external element. Normal arrangement models for cloud computing incorporate platform as a service (PaaS), software as a service (SaaS), framework as a service (IaaS), and equipment as a service (HaaS).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1073-1079
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• 2014

One of the techniques that are most in the spotlight today, it can be said that Big data. With Big Data, technologies already prevalent in our lives is GPS. Based on the GPS data and Big Data, in this paper, we try to analyze the pattern and path of movement of a particular target. Specific target collects the GPS data by classifying weather and grade and sex of college students, and day of the week in college students of one university. The collected data is analyzed such as movement path, movement time, pattern of repetitive behavior. And visualize it. The analysis method will be classified according to the purpose of data. By identifying relationships with other data results obtained. Based on the present study, the future, we will derive the results of the data more reliable. For this purpose, a wide range of information to be collected will additionally. Research will be developed add to such as Season, time, blood type, occupation data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.11
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• pp.2027-2032
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• 2016

The rapid diffusion of smart phones and the development of data storage and analysis technology have made the field of big-data a promising industry in the future. In the marketing field, big-data analysis on social data can be used for understanding the needs of consumers as an effective and efficient marketing tool. Before the age of big-data, companies had relied upon the traditional methods such as questionnaire survey and marketing test in which a small number of consumers had participated. The traditional methods have still been used. Although both of big-data analysis and traditional methods are useful to understand consumers. It is need to check whether the results from both include similar implications. In this point, this study attempts to compare the results of big-data analysis with that of questionnaire survey on some cosmetics brands methods. As the results of this study, both results of big-data analysis and questionnaire survey include similar implications.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.194-202
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• 2019

In the information society represented by the Fourth Industrial Revolution, various types of data and information that are difficult to see are produced, processed, and processed and circulated to enhance the value of existing goods. The IoT(Internet of Things) paradigm will change the appearance of individual life, industry, disaster, safety and public service fields. In order to implement the IoT paradigm, several elements of technology are required. It is necessary that these various elements are efficiently connected to constitute one system as a whole. It is also necessary to collect, provide, transmit, store and analyze IoT data for implementation of IoT platform. We designed and implemented a big data processing IoT platform for IoT service implementation. Proposed platform system is consist of IoT sensing/control device, IoT message protocol, unstructured data server and big data analysis components. For platform testing, fixed IoT devices were implemented as solar power generation modules and mobile IoT devices as modules for table tennis stroke data measurement. The transmission part uses the HTTP and the CoAP, which are based on the Internet. The data server is composed of Hadoop and the big data is analyzed using R. Through the emprical test using fixed and mobile IoT devices we confirmed that proposed IoT platform system normally process and operate big data.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.259-266
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• 2021

Edge Computing enables real-time big data processing by performing computing close to the physical location of the user or data source. However, in an edge computing environment, various situations that affect big data processing performance may occur depending on temporary service requirements or changes of physical resources in the field. In this paper, we proposed a BigCrawler system that dynamically configures the computing module and storage module according to the big data collection status and computing resource usage status in the edge computing environment. And the feature of big data processing workload according to the arrangement of computing module and storage module were analyzed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.114-116
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• 2016

Today, the analysis of big data hae been used as an essential tool for finding customers' needs. Various big-data analysis sites have provided the analysis results with their own forms and styles according to their service and characteristics. Therefore, to use the analysis results for marketing fields, we have to understand the major characteristics on big data analysis tools. In this point, this study attempts to compare the characteristics of big data analysis results and styles from big data analysis sites.