• Journal of Information Processing Systems
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• v.19 no.6
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• pp.722-729
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• 2023

Visual analytic for intangible cultural heritage has recently developed in China. Using advanced interactive visualization tools experts can observe data distribution trends and explore the implicit relationships among data within a short time. It can enhance human cognitive and analytical abilities and improve the scientific preservation of intangible cultural heritage. To support this research topic, we have reviewed recent visualization works on intangible cultural heritage in China. We divide these works into three types: text visualization, multi-dimensional visualization, and geographical visualization. Each type is illustrated by several representative works. New development trends in this area are also discussed.

• Journal of Korea Game Society
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• v.12 no.3
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• pp.77-86
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• 2012

In recent years, micro-blogging services such as 'Twitter' and 'Me2day' have rapidly become major social networking services. However, it is difficult to grasp the relationship between a user and his/her friends in these micro-blogging services because they simply list messages between them in chronological order. In this paper, we propose a visual analytics system that can help the user intuitively understand relationships with their friends on micro-blogging services by enabling them to analyze the messages quantitatively, qualitatively and temporally. In the visual analytics system, we also present a tool to provide the user with valuable advices after classifying the changing relation patterns with his/her friends, which in turn contributes to improving relationships with friends. The proposed system was successfully implemented as smartphone applications to show its potential to be a tool for analyses and improvement of social relations in micro-blogging services.

• Journal of Internet Computing and Services
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• v.17 no.4
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• pp.69-77
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• 2016

Recently, the importance of visual analytics has been recognized in the field of business intelligence. From the view of business intelligence, visual analytics aims for acquiring valuable insights for decision making by interactively visualizing a variety of business information. In this paper, we propose a treemap-based method for visualizing work-sharing relationships among business process performers. A work-sharing relationship is established between two performers who jointly participate in a specific activity of a business process and is an important factor for understanding organizational structures and behaviors in a process-centric organization. To this end, we design and implement a treemap-based visualization tool for representing work-sharing relationships as well as basic hierarchical information in business processes. Finally, we evaluate usefulness of the proposed visualization tool through an operational example using XPDL (XML Process Definition Language) process models.

• Journal of the Korea Computer Graphics Society
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• v.22 no.4
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• pp.21-30
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• 2016

Risk management system should be able to support a decision making within a short time to analyze stream data in real time. Many analytical systems consist of CPU computation and disk based database. However, it is more problematic when existing system analyzes stream data in real time. Stream data has various production periods from 1ms to 1 hour, 1day. One sensor generates small data but tens of thousands sensors generate huge amount of data. If hundreds of thousands sensors generate 1GB data per second, CPU based system cannot analyze the data in real time. For this reason, it requires fast processing speed and scalability for analyze stream data. In this paper, we present a fast visualization technique that consists of hybrid database and GPU computation. In order to evaluate our technique, we demonstrate a visual analytics system that analyzes pipeline leak using sensor and tweet data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2799-2823
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• 2019

With the help of learning analytics, MOOCs have wider potential to succeed in learning through promoting self-regulated learning (SRL). The current study aims to apply and validate visualization design guidelines for a MOOC dashboard to enhance such SRL capabilities based on learning analytics. To achieve the research objective, a MOOC dashboard prototype, LM-Dashboard, was designed and developed, reflecting the visualization design guidelines to promote SRL. Then, both expert and learner participants evaluated LM-Dashboard through iterations to validate the visualization design guidelines and perceived SRL effectiveness. The results of expert and learner evaluations indicated that most of the visualization design guidelines on LM-Dashboard were valid and some perceived SRL aspects such as monitoring a student's learning progress and assessing their achievements with time management were beneficial. However, some features on LM-Dashboard should be improved to enhance SRL aspects related to achieving their learning goals with persistence. The findings suggest that it is necessary to offer appropriate feedback or tips as well as to visualize learner behaviors and activities in an intuitive and efficient way for the successful cycle of SRL. Consequently, this study contributes to establishing a basis for the visual design of a MOOC dashboard for optimizing each learner's SRL.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1213-1224
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• 2021

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1155-1160
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• 2006

다양한 과학 분야와 공학 분야에서는 그들이 다루고 있는 특정한 주제의 정보를 좀 더 신속하고, 명확하게 사용자에게 전달하기 위해서 여러 가지 정보 가시화(information visualization) 기법을 사용한다. 정보를 가시화 할 때는 기본적으로 세 가지 과정을 거치는데, 원천 데이터(raw data)로부터 데이터 모델(data model)로 변환하고, 변환된 데이터 모델을 가시화 구조상(visual structure)에 매핑(mapping)시킨 후 정보화 모델(information model)로 변환하게 된다. 본 논문에서는 특정 행사가 진행되고 있는 건물내부에서 발생하는 시간, 공간적인 정보를 정리한 도표 메타포(table metaphor)를 토대로, 해당 데이터 모델로부터 추출한 다양한 정보를 3 차원 지도로 구성된 정보화 모델 상에 반영하기 위한 방법을 제안하였다. 또한, 정보를 단순히 공간상에 반영하기 보다는 사용자의 관심영역(interest area)에 따른 정보의 공간적 의미에 중점을 두어 3차원 공간상에 표현하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.07a
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• pp.175-176
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• 2014

본고에서는 교육 빅데이터의 개념, 가치, 처리 기술 및 분석 방법 등을 탐색하였다. '온라인과 오프라인 교수 학습 활동의 투입, 과정, 산출을 통해 생산되는 국가, 지역, 학교, 교사, 학생 수준의 자료'로 정의할 수 있는 교육 빅데이터는 Hadoop으로 대표되는 분산 컴퓨팅 기술을 통해 효율적으로 처리할 수 있다. 대규모 교육 자료에서 의미있고 유용한 결과를 도출하기 위해 주로 사용되는 분석 방법에는 교육 데이터 마이닝, 학습 분석학과 시각 자료 분석학이 있다. 교육 데이터 마이닝은 학생과 교사, 학교의 다양한 수준에서 자료를 폭넓게 분석하는 측면이 강한 반면에 학습 분석학은 학생 수준에서의 자료 분석에 더 초점을 맞추는 경향이 있으며, 시각 자료 분석학은 자료에 대한 분석 자체보다는 분석 결과를 효과적으로 표현하는 방식에 초점이 주어져 있다.

• Educational Technology International
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• v.18 no.2
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• pp.73-99
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• 2017

The learning analytics dashboard (LAD) is a supporting tool for teaching and learning in its personalized, automatic, and visual aspects. While several studies have focused on the effect of using dashboard on learning achievement, there is a research gap concerning the impacts of learners' characteristics on it. Accordingly, this study attempted to verify the differences in learning achievement depending on learning motivation level (high vs. low) and dashboard intervention (use vs. non-use). The final participants were 231 university students enrolled in a basic statistics course. As a research design, a 2 × 2 factorial design was employed. The results showed that learning achievement varied with dashboard intervention and the interaction effect was significant between learning motivation and dashboard intervention. The results imply that the impact of LAD may vary depending on learner characteristics. Consequently, this study suggests that the dashboard interventions should be offered after careful consideration of individual students' differences, particularly their learning motivation.

• Journal of Construction Engineering and Project Management
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• v.5 no.4
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• pp.16-22
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• 2015

This paper presents an automated computer vision-based system to update BIM data by leveraging multi-modal visual data collected from existing buildings under inspection. Currently, visual inspections are conducted for building envelopes or mechanical systems, and auditors analyze energy-related contextual information to examine if their performance is maintained as expected by the design. By translating 3D surface thermal profiles into energy performance metrics such as actual R-values at point-level and by mapping such properties to the associated BIM elements using XML Document Object Model (DOM), the proposed method shortens the energy performance modeling gap between the architectural information in the as-designed BIM and the as-is building condition, which improve the reliability of building energy analysis. Several case studies were conducted to experimentally evaluate their impact on BIM-based energy analysis to calculate energy load. The experimental results on existing buildings show that (1) the point-level thermography-based thermal resistance measurement can be automatically matched with the associated BIM elements; and (2) their corresponding thermal properties are automatically updated in gbXML schema. This paper provides practitioners with insight to uncover the fundamentals of how multi-modal visual data can be used to improve the accuracy of building energy modeling for retrofit analysis. Open research challenges and lessons learned from real-world case studies are discussed in detail.