• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.259-270
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• 2021

Recently, interests in the use of Sharing Bike is increasing in consideration of eco-friendly transportation and safety from viruses. As the technology for collecting and storing data is improved with the development of ICTs, research on mobility using the Sharing Bike Data is also actively progressing. Therefore, this paper analyzes the properties of Sharing Bike Data and cases of researches on it through literature review, and based on the results of the review, data of Eoulling, the Sharing Bike System of Sejong City is analyzed as a way to utilize Sharing Bike Data. Most of the selected literature used structured data, and analyzed it through statistical methods or data mining. Through data analysis, it identified the current status, found out problems of the Sharing Bike System, proposed a solution to solve them, developed plans to activate the use of Sharing Bike. This provides basic data for efficient management and operation plans for Sharing Bike System. Ultimately, it will be possible to explore ways to improve mobility in urban spaces by utilizing Sharing Bike Data.

• Education of Primary School Mathematics
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• v.14 no.1
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• pp.27-41
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• 2011

This study is the consideration to 'The Data' and 'On Divisions' of Euclid which is the historical start of analysis and synthesis. 'The Data' and 'On Divisions' compared to Euclid's Elements is not interested. In this study, analysis and synthesis were examined for significance. In this study, means for 'analysis' and 'synthesis' were examined through an analysis of 'The Data' and 'On Divisions'. First, the various terms including analysis and synthesis were examined and the concepts of the terms were analyzed. Then, analysis was divided into 'external analysis' and 'internal analysis'. And synthesis was divided into 'theoretical synthesis' and 'empirical synthesis'. On the basis of this classification problem presented in elementary textbooks and the practical applications were explored.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1133-1144
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• 2017

Cluster analysis with meteorological data allows to segment meteorological region based on meteorological characteristics. By the way, meteorological observed data are not adequate for cluster analysis because meteorological stations which observe the data are located not uniformly. Therefore the clustering of meteorological observed data cannot reflect the climate characteristic of South Korea properly. The clustering of $5km{\times}5km$ gridded data derived from a numerical model, on the other hand, reflect it evenly. In this study, we analyzed long-term grid data for temperatures and precipitation using cluster analysis. Due to the monthly difference of climate characteristics, clustering was performed by month. As the result of K-Means cluster analysis is so sensitive to initial values, we used initial values with Ward method which is hierarchical cluster analysis method. Based on clustering of gridded data, cluster of meteorological stations were determined. As a result, clustering of meteorological stations in South Korea has been made spatio-temporal segmentation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.4
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• pp.667-677
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• 2013

As the firewall is a typical network security equipment, it is usually installed at most of internal/external networks and makes many packet data in/out. So analyzing the its logs stored in it can provide important and fundamental data on the network security research. However, along with development of communications technology, the speed of internet network is improved and then the amount of log data is becoming 'Massive Data' or 'BigData'. In this trend, there are limits to analyze log data using the traditional database model RDBMS. In this paper, through our Method of Analyzing Firewall log data using MapReduce based on NoSQL, we have discovered that the introducing NoSQL data base model can more effectively analyze the massive log data than the traditional one. We have demonstrated execellent performance of the NoSQL by comparing the performance of data processing with existing RDBMS. Also the proposed method is evaluated by experiments that detect the three attack patterns and shown that it is highly effective.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.140-141
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• 2017

Big data analysis, in the large amount of data stored as the data warehouse which it refers the process of discovering meaningful new correlations, patterns, trends and creating new values. Thus, Big data analysis is an effective analysis of various big data that exist all over the world such as social big data, machine to machine (M2M) sensor data, and corporate customer relationship management data. In the big data era, it has become more important to effectively analyze not only structured data that is well organized in the database, but also unstructured big data such as the internet, social network services, and explosively generated web documents, e-mails, and social data in mobile environments. By the way, a meta analysis refers to a statistical literature synthesis method from the quantitative results of many known empirical studies. We reviewed a total of 750 samples among 50 studies published on the topic related as IDT between 2000 and 2017 in Korea.

• Proceedings of the Korean Association for Survey Research Conference
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• 2001.04a
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• pp.109.2-130
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• 2001

The purpose of this study is (1) to demonstrate that ignoring group differences may mislead to incorrect conclusion when analyzing correlation data (e.g., factor analysis), (2) to highlight the importance of the data analytic method considering group differences, and (3) to provide ways of incorporating group differences in data analysis. In study, 1, ignoring group difference in factor analysis may mislead to incorrect factor structure. To remedy this, z-transform method and group analysis tool in covariance structure models were suggested. In study 2, the group differences effect was illustrated by using real data (IQ test data).

• Survey Research
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• v.2 no.2
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• pp.109-130
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• 2001

The purpose of this study is (1) to demonstrate that ignoring group differences may mislead to incorrect conclusion when analyzing correlation data (e.g.. factor analysis). (2) to highlight the importance of the data analytic method considering group differences. and (3) to provide ways of incorporating group differences in data analysis. In study 1. ignoring group difference in factor analysis may mislead to incorrect factor structure. To remedy this. z-transform method and group analysis tool in covariance structure models were suggested. In study 2. the group differences effect was illustrated by using real data (IQ test data).

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.6
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• pp.1399-1410
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• 2015

Gartner is requiring companies to considerably change their survival paradigms insisting that companies need to understand and provide again the upcoming era of data competition. With the revealing of successful business cases through statistic algorithm-based predictive analytics, also, the conversion into preemptive countermeasure through predictive analysis from follow-up action through data analysis in the past is becoming a necessity of leading enterprises. This trend is influencing security analysis and log analysis and in reality, the cases regarding the application of the big data analysis framework to large-scale log analysis and intelligent and long-term security analysis are being reported file by file. But all the functions and techniques required for a big data log analysis system cannot be accommodated in a Hadoop-based big data platform, so independent platform-based big data log analysis products are still being provided to the market. This paper aims to suggest a framework, which is equipped with a real-time and non-real-time predictive analysis engine for these independent big data log analysis systems and can cope with cyber attack preemptively.

• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.313-322
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• 2009

Recently, According to computer technology has been improving, Massive customer data has stored in database. Using this massive data, decision maker can extract the useful information to make a valuable plan with data mining. Data mining offers service providers great opportunities to get closer to customer. Data mining doesn't always require the latest technology, but it does require a magic eye that looks beyond the obvious to find and use the hidden knowledge to drive marketing strategies Automotive market face an explosion of data arising from customer but a rate of increasing customer is getting lower. therefore, we need to determine which customer are profitable clients whom you wish to hold. This paper builds model of customer loyalty detection and analyzes customer patterns in automotive market with data mining using association rule and basic statics methods. With 4he help of information technology.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.165-166
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• 2017

It is demanding to receive information of data in real time anywhere and analyze it with meaningful data. Research on the platform for such analysis is actively underway. In this paper, we try to find out what are important factors in solving the problems of collecting and analyzing IoT data in real time. How much better than existing data collection methods and analytical methods can be the basis for judging the value of the data. It is important to accurately collect and store data more quickly and quickly from many sensors in real time in real time, and analytical methods that can derive values from the stored data. Therefore, an important requirement of the analysis platform in the IoT environment is to process large amount of data in real time and to centralize and manage it.