• Journal of Digital Convergence
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• v.13 no.1
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• pp.227-235
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• 2015

Big data analysis is a means of organizational problem solving. For an effective problem solving, approaches to problem solving should take into account the factors such as characteristics of problem, types and availability of data, data analytic capability, and technical capability. In this article we propose three approaches: logical top-down, data driven bottom-up, and prototyping for overcoming undefined problem circumstances. In particular we look into the relationship of creative problem solving with the bottom-up approach. Based on the organizational data governance and data analytic capability, we also derive strategic issues concerning the sourcing of big data analysis.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.195-205
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• 2020

Recently, after the 4th industrial revolution era has arrived, technological advances started to develop and these changes have led to widespread use of data. Big data is often used for the safety of citizens, including the administration, safety and security of the country. In order to enhance the efficiency of maintaining such security, it is necessary to understand the installation status of CCTVs. By comparing the installation rate of CCTVs and crime rate in the area, we should analyze and improve the status of CCTV installation status, and crime rate in each area in order to increase the efficiency of security. Therefore, in this paper, big data analytic system based on public data is developed to collect data related to crime rate such as CCTV, female population, entertainment center, etc. and to reduce crime rate through efficient management and installation of CCTV.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.115-117
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• 2017

R has become a popular analytics platform as it provides powerful analytic functions as well as visualizations. However, it has a weakness in which scalability is limited. As an alternative, the RHadoop package facilitates distributed processing of R programs under the Hadoop platform. This paper investigates usefulness of the RHadoop package when analyzing healthcare big data that is widely open in the internet space. To do this, this paper has compared analytic performances of R and RHadoop using the medical treatment records of year 2015 provided by National Health Insurance Service. The result shows that RHadoop effectively enhances processing performance of healthcare big data compared with R.

• The Journal of Bigdata
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• v.4 no.2
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• pp.85-91
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• 2019

Since online shopping has become common, people can easily buy fashion goods anytime, anywhere. Therefore, consumers quickly respond to various environmental variables such as weather and sales prices. Thus, utilizing big data for efficient inventory management has become very important in the fashion industry. In this paper, the changes in sales volume of fashion goods due to changes in temperature is analyzed via the proposed big data analysis algorithm by utilizing actual big data from Korean fashion company 'B'. According to the analytic results, the proposed big data analysis algorithm found both expected and unexpected changes in sales volume depending on the characteristics of the fashion goods.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.12
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• pp.553-564
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• 2014

In this paper, we propose the system for automatic generation of issue analysis report based on social big data mining, with the purpose of resolving three problems of the previous technologies in a social media analysis and analytic report generation. Three problems are the isolation of analysis, the subjectivity of experts and the closure of information attributable to a high price. The system is comprised of the natural language query analysis, the issue analysis, the social big data analysis, the social big data correlation analysis and the automatic report generation. For the evaluation of report usefulness, we used a Likert scale and made two experts of big data analysis evaluate. The result shows that the quality of report is comparatively useful and reliable. Because of a low price of the report generation, the correlation analysis of social big data and the objectivity of social big data analysis, the proposed system will lead us to the popularization of social big data analysis.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1238-1249
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• 2020

Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.

• The Journal of the Korea Contents Association
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• v.19 no.6
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• pp.359-367
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• 2019

As a strategy for strengthening local competitiveness, efficient use of regional assets is becoming more and more important. The key to regional identity and competitiveness is local assets. The purpose of this study is to derive the priority region for development by evaluating local assets. The analysis methods used in this study are Geographic Information System analysis, Big Data Trend analysis, and Analytic Hierarchy Process analysis. To assess the potential of local assets, the preference of assets, historical value, cluster of resources, wide-area transport accessibility, and population density were set as analysis indicators and itemized weights were applied using AHP to reflect the importance of each item. As a result of analyzing Yeongju city in Gyeongsangbuk-do, eight major points such as Buseoksa Temple, Sosu Seowon, Huibangsa Temple, Punggi Hot Spring Resort, Punggi Station, National Center for Forest Therapy, Yeongju east region and Museom Village were derived.

• Journal of Information Technology Services
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• v.17 no.1
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• pp.17-32
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• 2018

The city image is considered one of important symbolic and important factors in selecting the travel destination. Many cities are trying to be an attractive and popular city to tourists through the construction of a good brand image by utilizing their representative characteristics. This study measures the city brand image by applying a big data analytic method. In addition, the big data measurement results were rearranged and analyzed to identify further detailed city images by utilizing several previous interpretation methods. Our study has chosen Dubai since this city has the diverse images due to its regional as well as economic characteristics. In particular, nowadays Dubai has been recognized as one of the most important touristic places in the Middle East region for its modern and innovative images in spite of the limitations of location, weather, religion, and even political issues of neighbor countries. Founded on a big data analysis rather than a questionnaire-based survey, the presented interpretation methods are evaluated to improve the understanding of Dubai's diverse city images. In addition, based on the results of this research, it is expected to have a practical impact on establishing the effective marketing strategies to build and implement the valuable city brand image.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.2
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• pp.36.3-37
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• 2018

By utilizing large-scale graph analytic tools in the modern Big Data platform, Apache Spark, we investigate the topological structures of five different multiverses produced by cosmological n-body simulations with various cosmological initial conditions: (1) one standard universe, (2) two different dark energy states, and (3) two different dark matter densities. For the Big Data calculations, we use a custom build of stand-alone Spark cluster at KIAS and Dataproc Compute Engine in Google Cloud Platform with the sample sizes ranging from 7 millions to 200 millions. Among many graph statistics, we find that three simple graph measurements, denoted by (1) $n_\k$, (2) $\tau_\Delta$, and (3) $n_{S\ge5}$, can efficiently discern different topology in discrete point distributions. We denote this set of three graph diagnostics by kT5+. These kT5+ statistics provide a quick look of various orders of n-points correlation functions in a computationally cheap way: (1) $n = 2$ by $n_k$, (2) $n = 3$ by $\tau_\Delta$, and (3) $n \ge 5$ by $n_{S\ge5}$.

• Journal of Digital Convergence
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• v.14 no.7
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• pp.21-27
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• 2016

Big Data is gathering all the attention from every business community. Pervasive use of machine-to-machine (M2M) applications and mobile devices bring an explosion of data. By analyzing this data, the private and public sectors can benefit in the areas of cost reduction and productivity. The Korean government is actively pursuing Big Data initiatives to promote its usage. This paper aims to select industries which fit for the development of Big Data with a verification of the experts. The analytic hierarchy process (AHP) is applied to systematically derive the opinion of more than 50 professionals. Medical / welfare, transportation / warehousing, information and communications / information security, energy, the financial sector have been identified as promising industries. The results can be utilized in developing Big Data best practices thus contributing industrial development.