• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.313-321
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• 2014

In the current era of data-intensive services, the handling of big data is a crucial issue that affects almost every discipline and industry. In this study, we propose a classification method for large volumes of numeric data, which is implemented in a distributed programming framework, i.e., MapReduce. The proposed method partitions the data space into a grid structure and it then models the probability distributions of classes for grid cells by collecting sufficient statistics using distributed MapReduce tasks. The class labeling of new data is achieved by k-nearest neighbor classification based on Bayesian inference.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.79-89
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• 2019

Classification models pertaining to receiver operating characteristic (ROC) curve analysis have been extended from univariate to multivariate setup by linearly combining available multiple markers. One such classification model is the multivariate ROC curve analysis. However, not all markers contribute in a real scenario and may mask the contribution of other markers in classifying the individuals/objects. This paper addresses this issue by developing an algorithm that helps in identifying the important markers that are significant and true contributors. The proposed variable selection framework is supported by real datasets and a simulation study, it is shown to provide insight about the individual marker's significance in providing a classifier rule/linear combination with good extent of classification.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.24 no.4
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• pp.23-51
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• 2013

The purpose of the study is to examine the potentialities and limits of Business Reference Model (BRM) as records classification schemes in Korean central state institutions. The analysis is based on the data collected through focus group interviews of three times, in which six records professionals from central government agencies participate. This paper begins with inquiring the framework of records classification based BRM, required by Public Records Management Act. It explores the types of benefit of BRM application to government records classification. Based on the collected data from the interviews, it investigates how records are aggregated, and how transaction level (Danwi-Gwaje) of BRM is applied in the course of records aggregation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.61-68
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• 2018

In general, defects cause a lot of maintenance costs and serious damage to various stakeholders, such as the owners, contractors or occupants of apartments. For this reason, a systematic and efficient defect management method is needed to minimize defect disputes. This paper derives a defect classification framework and proposes a defect risk assessment model for different types of defects. For this purpose, 6,000 defect items are allocated to the defect classification framework; these items are associated with 34 apartment projects over ten years old. As a result of this analysis, it was confirmed that the defect risks are concentrated in the areas of RC and finishing work. Based on these results, it is necessary to prevent the major risks of defects according to their priority. Based on this research, it is judged that further research to develop a method of managing the risks of defects may be necessary.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.27-35
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• 2021

In this paper, we propose a new stacking ensemble framework for deep learning models which reflects the distribution of label embeddings. Our ensemble framework consists of two phases: training the baseline deep learning classifier, and training the sub-classifiers based on the clustering results of label embeddings. Our framework aims to divide a multi-class classification problem into small sub-problems based on the clustering results. The clustering is conducted on the label embeddings obtained from the weight of the last layer of the baseline classifier. After clustering, sub-classifiers are constructed to classify the sub-classes in each cluster. From the experimental results, we found that the label embeddings well reflect the relationships between classification labels, and our ensemble framework can improve the classification performance on a CIFAR 100 dataset.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.163-170
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• 2021

As a big data is being used in various industries, big data market is expanding from hardware to infrastructure software to service software. Especially it is expanding into a huge platform market that provides applications for holistic and intuitive visualizations such as big data meaning interpretation understandability, and analysis results. Demand for big data extraction and analysis using social media such as SNS is very active not only for companies but also for individuals. However despite such high demand for the collection and analysis of social media data for user trend analysis and marketing, there is a lack of research to address the difficulty of dynamic interlocking and the complexity of building and operating software platforms due to the heterogeneity of various social media service interfaces. In this paper, we propose a method for developing a framework to operate the process from collection to extraction and classification of social media data. The proposed framework solves the problem of heterogeneous social media data collection channels through adapter patterns, and improves the accuracy of social topic extraction and classification through semantic association-based extraction techniques and topic association-based classification techniques.

• The Journal of Information Systems
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• v.26 no.3
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• pp.247-271
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• 2017

Purpose This study aims to propose a framework for safety in domestic walking tourism, which defines the scope and type of safety information for tourists. Also, the framework provides a systematic classification for safety information in walking tourism. As the importance of safety in walking tourism due to the frequent occurrence of crimes and unexpected accidents increases, it is necessary to provide tourists with safety information on walking courses rapidly and systematically. However, most administrators (e.g., local governments) have mainly focused on providing tour information on walking courses through Internet websites or mobile apps, overlooking the safety issue. Besides, there is a lack of understanding of safety not only in walking tourism but also in the whole tourism industry. Design/methodology/approach This study proposes the framework that covers all possible safety-related information in walking tourism. Furthermore, to validate its availability, this study analyzes eight major platforms which offer tour information to tourists by applying the framework. Findings The major findings of this study are as follows. First, all platforms considered in this study do not provide any information on tourism safety, focusing on the provision of tour information. Second, three platforms currently provide an SOS function which means post-safety information. That is, no safety information is provided to prevent safety issues in advance. Finally, despite the increase of social concerns, all platforms do not provide safety information on public order and diseases in the social disaster classification as well as environmental pollution in the natural disaster classification. The details and implications of the findings are presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3865-3883
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• 2016

Infrastructure as a Service (IaaS) encapsulates computer hardware into a large amount of virtual and manageable instances mainly in the form of virtual machine (VM), and provides rental service for users. Currently, VM anomaly incidents occasionally occur, which leads to performance issues and even downtime. This paper aims at detecting anomalous VMs based on performance metrics data of VMs. Due to the dynamic nature and increasing scale of IaaS, detecting anomalous VMs from voluminous correlated and non-Gaussian monitored performance data is a challenging task. This paper designs an anomaly detection framework to solve this challenge. First, it collects 53 performance metrics to reflect the running state of each VM. The collected performance metrics are testified not to follow the Gaussian distribution. Then, it employs independent components analysis (ICA) instead of principal component analysis (PCA) to extract independent components from collected non-Gaussian performance metric data. For anomaly detection, it employs multi-class Bayesian classification to determine the current state of each VM. To evaluate the performance of the designed detection framework, four types of anomalies are separately or jointly injected into randomly selected VMs in a campus-wide testbed. The experimental results show that ICA-based detection mechanism outperforms PCA-based and LDA-based detection mechanisms in terms of sensitivity and specificity.

• Journal of Computational Design and Engineering
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• v.3 no.3
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• pp.274-285
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• 2016

A designer is mainly supported by two essential factors in design decisions. These two factors are intelligence and experience aiding the designer by predicting the interconnection between the required design parameters. Through classification of product data and similarity recognition between new and existing designs, it is partially possible to replace the required experience for an inexperienced designer. Given this context, the current paper addresses a framework for recognition and flexible retrieval of similar models in product design. The idea is to establish an infrastructure for transferring design as well as the required PLM (Product Lifecycle Management) know-how to the design phase of product development in order to reduce the design time. Furthermore, such a method can be applied as a brainstorming method for a new and creative product development as well. The proposed framework has been tested and benchmarked while showing promising results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.370-373
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• 2022

Recently, various deep learning framework models such as Tensorflow, Pytorch, Keras, etc. have appeared. In addition, CNN (Convolutional Neural Network) is applied to image recognition using frameworks such as Tensorflow, Pytorch, and Keras, and the optimization model in image classification is mainly used. In this paper, based on the results of training the CNN model with the Paitotchi and tensor flow frameworks most often used in the field of deep learning image recognition, the two frameworks are compared and analyzed for image analysis. Derived an optimized framework.