Visual analytics

We present a technique to analyze the cause of traffic congestion based on the traffic flow theory. We extract vehicle flows from traffic data, such as GPS trajectory and Vehicle Detector data. We detect vehicle flow changes utilizing the entropy from the information theory. Then, we build cumulative vehicle count curves (N-curve) that can quantify the flow of the vehicles in the traffic congestion area. The N-curves are classified into four different traffic congestion patterns by a convolutional neural network. Analyzing the causes and influence of traffic congestion is difficult and requires considerable experience and knowledge. Therefore, we present a visual analytics system that can efficiently perform a series of processes to analyze the cause and influence of traffic congestion. Through case studies, we have evaluated that our system can classify the causes of traffic congestion and can be used efficiently in road planning.

Paper:

Pi M., Yeon H., Son H., Jang Y., "Visual Cause Analytics for Traffic Congestion", To appear in the IEEE Transactions on Visualization and Computer Graphics 

We present a predictive analysis and modeling to forecast incomplete data over time. We extend the Bayesian network model, which is relative data-driven approaches that explore similar data and weave them to create approximate inference margins. Besides, we propose a visual analytics system that enables us to design various predictive models that reflect individual growth pattern. Our visual analytics system assists us to discover new growth patterns in the process of analyzing the accuracy of previously designed predictive models. Moreover, the system allows us to optimize predictive models to fit unusual growth patterns.

Paper:

Yeon H., Son H., Jang Y., "Visual Predictive Modeling of Incomplete Time Series Panel Data", Proc. of the 12th International Symposium on Visual Information Communication and Interaction, 2019.

We present a predictive visual analytics system to provide predictive event patterns. We infer the future event evolution by combining contextually similar cases occurring in the past. We utilize social media data to detect interesting abnormal events and match the detected abnormal events within the past news media data to retrieve similar event patterns. Then, we extract future event patterns through compositing contextual relationship among topics included in the similar past patterns. To evaluate our VA system, we demonstrate three use cases in this paper and validate our system with possible predictive story lines. In addition, we present an informal user study and feedback to validate the effectiveness of our system and improve the system in the future.

Paper:

Yeon H., Kim S., Jang Y., "Predictive Visual Analytics of Event Evolution for User-Created Context", Journal of Visualization 20(3): 471-486, 2017.

Yeon H., Jang Y., "Predictive Visual Analytics using Topic Composition", Proc. of the 8th International Symposium on Visual Information Communication and Interaction, 2015.

The risk of infectious disease increases due to various factors including the dense population, development of various transportations, urbanization, and abnormal weather conditions. Since the speed of epidemic spread is fast, it is necessary to response quickly in order to prevent the high fatality rate. Therefore, we research for the fast search and proper analysis of disease spreading. 

patent: Jang, Y., Kim, S., Jeong, S., APPARATUS AND METHOD FOR EPIDEMIC SPREAD PREDICTION MODELING, 1019605040000 (2019.03.14)

We present a visual analytics system for Android security risk lifelog using app permissions to recognize the risk. Several linked visualizations are designed to present the risk lifelog.

Paper: 

Yoo, S., Ryu, H. R., Yeon, H., Kwon, T., & Jang, Y. (2017, August). Personal visual analytics for android security risk lifelog. In Proceedings of the 10th International Symposium on Visual Information Communication and Interaction (pp. 29-36). ACM. 

Yoo S., Ryu H. R., Yeon H., Kwon T., Jang Y., “Visual analytics and visualization for android security risk”, Journal of Computer Languages, Vol. 53, pp. 9-21, 2019