• International conference on construction engineering and project management
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• 2015.10a
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• pp.44-48
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• 2015

Housing price is a key indicator of housing demand. Actual Transaction Price Index of Apartment (ATPIA) released by Korea Appraisal Board is useful to understand the current level of housing price, but it does not forecast future prices. Big data such as the frequency of internet search queries is more accessible and faster than ever. Forecasting future housing demand through big data will be very helpful in housing market. The objective of this study is to develop a forecasting model of ATPIA as a part of forecasting housing demand. For forecasting, a concept of time shift was applied in the model. As a result, the forecasting model with the time shift of 5 months shows the highest coefficient of determination, thus selected as the optimal model. The mean error rate is 2.95% which is a quite promising result.

• Journal of Digital Convergence
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• v.15 no.9
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• pp.167-178
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• 2017

Big Data is a key asset for the company and a key factor in boosting its competitiveness in the logistics sector. However, there is still a lack of research on how to collect, analyze and utilize Big Data in logistics. In this context, this study has developed a value model applicable to logistics companies based on the results of analysis and application of Big Data in the logistics of previous studies and DHL. The purpose of this study is to improve the operational efficiency and customer experience maximization level of logistics companies through utilization of big data analysis in logistics, to improve competitiveness of big data utilization and to develop new business opportunities. This study has a significance to newly create a value model for utilization of big data analysis in logistics sector and can provide implications for other industries as well as logistics sector in the future.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.44-53
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• 2016

The era of Big Data has come and the importance of Big Data has been rapidly growing. The part of transportation, the Four-Step Travel Demand Model(FSTDM), a traditional Trip-Based Model(TBM) reaches its limit. In recent years, a traffic demand forecasting method using the Activity-Based Model(ABM) emerged as a new paradigm. Given that transportation means the spatial movement of people and goods in a certain period of time, transportation could be very closely associated with spatial data. So, I mined Spatial Big Data from SNS. After that, I analyzed the character of these data from SNS and test the reliability of the data through compared with the attributes of TBM. Finally, I built a database from SNS for the operation of ABM and manipulate an ABM simulator, then I consider the result. Through this research, I was successfully able to create a spatial database from SNS and I found possibilities to overcome technical limitations on using Spatial Big Data in the transportation planning process. Moreover, it was an opportunity to seek ways of further research development.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.1075-1081
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• 2016

The big data analysis has received much attention from the researchers working in various fields because the big data has a great potential in detecting or predicting future events such as epidemic outbreaks and changes in stock prices. Reflecting the current popularity of big data analysis, many authors have proposed methods tracking influenza epidemics based on internet-based information. The recently proposed 'autoregressive model using Google (ARGO) model' (Yang et al., 2015) is one of those influenza tracking models that harness search queries from Google as well as the reports from the Centers for Disease Control (CDC), and appears to outperform the existing method such as 'Google Flu Trends (GFT)'. Although the ARGO predicts well the outbreaks of influenza, this study demonstrates that a classical seasonal autoregressive integrated moving average (SARIMA) model can outperform the ARGO. The SARIMA model incorporates more accurate seasonality of the past influenza activities and takes less input variables into account. Our findings show that the SARIMA model is a functional tool for monitoring influenza epidemics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.77-88
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• 2021

The development history of China's big data is relatively short, and it has only been ten years so far. Although the application level of big data in real life is not high, some achievements have been made in the supply chain. Various kinds of data will be generated in the actual operation of the supply chain. If these data can be effectively classified and used, the "bullwhip effect" of the operation of the supply chain can be also effectively improved. Thus this paper proposes the development of a supply chain collaborative inventory management model and application framework using big data. In this study, we analyzed the supply chain of beer industry, which is the most prominent consumption industry with "bullwhip effect", and further established a big data collaborative inventory management model for the supply chain of beer industry based on system dynamics. We used the Vensim software for simulation and sensitivity test and after appling our model, we found that the inventory fluctuations of the participants in the beer industry supply chain became significantly smaller, which verified the effectiveness of the model. Our study can be also applied to the possible problems of the large data supply chain collaborative inventory management model, and gives certain countermeasures and suggestions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1485-1491
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• 2019

As the type and size of vital signs become extensive at the moment lately, a research is actively progressing to define vital signs as big data and analyze it. We generally use a similar method of processing big data on social network as a way to treat vital signs as big data. Vital Sign Big Data should be extracted as feature data, stored separately, and analyzed with various analytical instruments. In other words, it should ensure interoperability and compatability of data, and the index expression in analytical tools should be concise. For this end, I defined the vital sign as the standard meta-model base of HL7 in this dissertation, and I propose a model for analyzing vital signs using OCL, the OMG's standard mathematical specification language. In addition, the proposed model can be confirmed the applicability by figuring out the consumption of calories using ECG data.

• International Journal of Contents
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• v.15 no.4
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• pp.113-119
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• 2019

It has been reported that large amounts of information on agri-foods were delivered to consumers through television and social networks, and the information may influence consumers' behavior. The purpose of this paper was first to analyze relations of social network service and broadcasting program on paprika consumption in the aspect of amounts to purchase and identify potential factors that can promote paprika consumption; second, to develop prediction models of paprika consumption by using structured and unstructured big data. By using data 2010-2017, cross-correlation and time-series prediction algorithms (autoregressive exogenous model and vector error correction model), statistically significant correlations between paprika consumption and television programs/shows and blogs mentioning paprika and diet were identified with lagged times. When paprika and diet related data were added for prediction, these data improved the model predictability. This is the first report to predict paprika consumption by using structured and unstructured data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2301-2306
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• 2015

The time to address the exabytes of data has come as the information age accelerates. Big data transfer technology is essential for processing large amounts of data. This paper posits to transfer big data in the optimal conditions by the proposed algorithm for predicting the optimal combination of Pipelining, Concurrency, and Parallelism (PCP), which are major functions of GridFTP. In addition, the author introduced a simple design process of Takagi-Sugeno-Kang (TSK) fuzzy model and designed a model for predicting transfer throughput with optimal combination of Pipelining, Concurrency and Parallelism. Hence, the author evaluated the model of the proposed algorithm and the TSK model to prove the superiority.