• Journal of the Korea Institute of Information Security & Cryptology
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• v.9 no.3
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• pp.13-26
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• 1999

Widespread use of Internet despite numerous positive aspects resulted in increased number of system intrusions and the need for enhanced security mechanisms is urgent. Systematic collection and analysis of log data are essential in intrusion investigation. Unfortunately existing logs are stored in diverse and incompatible format thus making an automated intrusion investigation practically impossible. We examined the types of log data essential in intrusion investigation and implemented a tool to enable systematic collection and efficient analysis of voluminous log data. Our tool based on RBDMS and SQL provides graphical and user-friendly interface. We describe our experience of using the tool in actual intrusion investigation and explain how our tool can be further enhanced.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.2
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• pp.91-100
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• 2009

"How does advertising affect sales?" is the fundamental issue of modern advertising research. There is an interesting issue for estimating carryover effects of advertising on sales, and the aggregated data biases exist in the duration of advertising effect. This research suggests an extended model of Koyck Model which is employed for micro-data (Koyck 1954) to estimate aggregated advertising data, and empirically shows the aggregated data bias. Our developed model with the aggregated level of actual advertising data is more appropriate than the basic Koyck model for micro-data. The result figures out that it is important to consider the disaggregated data level in the analysis of dynamic effects of adverting such as carryover effects.

• International Journal of Contents
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• v.17 no.3
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• pp.74-83
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• 2021

A location-aware algorithm is proposed in this study to optimize the system performance of distributed systems for processing big data with low data reliability and application performance. Compared with previous algorithms, the location-aware data block placement algorithm uses data block placement and node data recovery strategies to improve data application performance and reliability. Simulation and actual cluster tests showed that the location-aware placement algorithm proposed in this study could greatly improve data reliability and shorten the application processing time of I/O interfaces in real-time.

• Magazine of the Korean Society of Agricultural Engineers
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• v.31 no.4
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• pp.96-110
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• 1989

To suggest the fundamental data for the estimation of crop evaportranspiration by the ca- lculated coefficients for estimating the radiation suitable to the different regions of korea in application of Penman equcation, the daily data such as sc(skycover), n(actual sunshine hours), N(possible sunshine hours), Rs(horizontal solar radiation) and Ra(extraterrestial solar radiation) for 10 years (from 1977 to 1986) collected from 19 meteorological stations were analysed. The results are summarized as follows : 1. The coefficients a, b and c for estimating the radiation taken by the regression method with the daily and monthly mean data of the skycover and the ratio of Rs to Ra were shown as a=0.619, b= -0.0202, c= -0.0023 and a=0.64, b=0.0377 c=0.0001 in ave- rage respectively. 2. The coefficients a and b for estimating the radiation analysed by the regression and arithmetic method from the daily ratio of sunshine hours and Rs to Ra were shown as a= 0.157, b= 0.529, and a=0.119, b= 0.726 in average, respectively. 3. The coefficients a and b for estimating the radiation calculated by the regression me- thod based on the monthly ratio of sunshine hours and radiation were shown as a=0. 319 and b= 0.557 in average. 4. The values of a and b for estimating the radiation taken from the relationship between the daily ratio of sunshine hours and radiation showed high significance level. 5. The standard deviation and the coefficient of variance between the radiation calculated from the coefficients by the regression and arithmetic method with the daily data and the actual radiation were analysed and compared to the results by the coefficients of the modified Penman method (a=0.18, b=0.55) and by those of the F.A.O inodified Penman method(a=0.25, b=0.5). The standard deviation and the coefficient of varia- nce by the regression method in this study showed the lowest value. 6. From the above results, it is suggested that regression method using the coefficients taken from the relationship between the ratio of sunshine hours and the ratio of radia- tion based on the daily data has the highest accuracy in estimating the radiation. 7. The average reference crop evapotranspiration estimating by the modified Penman me- thod using the coefficients a and b derived by the regression method from the daily meterological data was closer to the actual evapotsranspiration of grass measured in Suwon area than the estimated evapotranspiration by the modified Penman method and the F.A.O modified Penman method.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.343-350
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• 2022

It is worth verifying the effectiveness of data integration between data with different features. This study investigated whether the data integration affects the accuracy of deep neural network (DNN), and which integration method shows the best improvement. This study used two different public datasets. One public dataset was taken in an actual farm in India. And another was taken in a laboratory environment in Korea. Leaf images were selected from two different public datasets to have five classes which includes normal and four different types of plant diseases. DNN used pre-trained VGG16 as a feature extractor and multi-layer perceptron as a classifier. Data were integrated into three different ways to be used for the training process. DNN was trained in a supervised manner via the integrated data. The trained DNN was evaluated by using a test dataset taken in an actual farm. DNN shows the best accuracy for the test dataset when DNN was first trained by images taken in the laboratory environment and then trained by images taken in the actual farm. The results show that data integration between plant images taken in a different environment helps improve the performance of deep neural networks. And the results also confirmed that independent use of plant images taken in different environments during the training process is more effective in improving the performance of DNN.

• Journal of Distribution Science
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• v.13 no.9
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• pp.5-11
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• 2015

Purpose - The purpose of this research is to provide insights that can be used for deliberate decision making around challenging big data investments by measuring the economic value of such big data implementations. Research design, data, and methodology - We perform empirical research through an event study. To this end, we measure actual abnormal returns of companies that are triggered by their investment announcements in big data, or firm size information, during the three-year research period. The research period targets a timeframe after the introduction of big data at Korean firms listed on the Korea stock markets. Results - Our empirical findings discover that on the event day and the day after, the abnormal returns are significantly positive. In addition, our further examination of firm size impacts on the abnormal returns does not show any evidence of an effect. Conclusions - Our research suggests that an event study can be useful as an alternative means to measure the return on investment (ROI) for big data in order to lessen the difficulties or decision making around big data investments.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.285-304
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• 2017

Big data information and pattern analysis have applications in many industrial sectors. To reduce energy consumption effectively, the eco-driving method that reduces the fuel consumption of vehicles has recently come under scrutiny. Using big data on commercial vehicles obtained from digital tachographs (DTGs), it is possible not only to aid traffic safety but also improve eco-driving. In this study, we estimate fuel consumption efficiency by processing and analyzing DTG big data for commercial vehicles using parallel processing with the MapReduce mechanism. Compared to the conventional measurement of fuel consumption using the On-Board Diagnostics II (OBD-II) device, in this paper, we use actual DTG data and OBD-II fuel consumption data to identify meaningful relationships to calculate fuel efficiency rates. Based on the driving pattern extracted from DTG data, estimating fuel consumption is possible by analyzing driving patterns obtained only from DTG big data.

• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.265-276
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• 2015

In this paper, we propose hierarchical cluster analysis and multidimensional scaling for joint distribution valued data. Information technology is increasing the necessity of statistical methods for large and complex data. Symbolic Data Analysis (SDA) is an attractive framework for the data. In SDA, target objects are typically represented by aggregated data. Most methods on SDA deal with objects represented as intervals and histograms. However, those methods cannot consider information among variables including correlation. In addition, objects represented as a joint distribution can contain information among variables. Therefore, we focus on methods for joint distribution valued data. We expanded the two well-known exploratory methods using the dissimilarities adopted Hall Type relative projection index among joint distribution valued data. We show a simulation study and an actual example of proposed methods.

• International journal of advanced smart convergence
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• v.5 no.1
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• pp.47-50
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• 2016

Big Data is coming rapidly in recent times and keep the vast amount of data was utilized them. These data are utilized in many fields in particular, based on the patient data in the medical field to increase the therapeutic effect, as well as re-incidence to better treatment, lowering the readmission rates increased the quality of life. In this paper it is practiced to report basis of the analysis and verification of data using python. And it can be analyzed the data through a simple formula, from Select reason of Python to how it used; by Press analysis of Agriculture, Forestry and Fisheries research. In this process, a simple formula can be used that expression for analyzing the actual data so it taking advantage of the use of functions in real life.

• Journal of KIBIM
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• v.9 no.1
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• pp.22-31
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• 2019

In this paper, we construct a prototype model for city data prediction by using time series data of floating population, and use machine learning to analyze urban data of complex structure. A correlation prediction model was constructed using three of the 10 data (total flow population, male flow population, and Monday flow population), and the result was compared with the actual data. The results of the accuracy were evaluated. The results of this study show that the predicted model of the floating population predicts the correlation between the predicted floating population and the current state of commerce. It is expected that it will help efficient and objective design in the planning stages of architecture, landscape, and urban areas such as tree environment design and layout of trails. Also, it is expected that the dynamic population prediction using multivariate time series data and collected location data will be able to perform integrated simulation with time series data of various fields.