Dr. Julia Sheidin is a full-time faculty member at Braude Academic College of Engineering and a Research Fellow at the Information Systems department at the University of Haifa, specializes in information systems, human factors, and computer science, enriched by industry experience. With a PhD from the University of Haifa, her research focused on Visual Analytics for managing multifaceted temporal data overload. She holds a master's degree from the same institution, contributing to a joint project with the Italian Research Institute FBK-IRST.
Supervisor:Prof. Tsvika Kuflik and Prof. Joel Lanir.
Research Topic: Visual Analytics of Temporal Multifaceted Data.
We are living in the era of “big data”, where there is a large volume of data from a variety of sources, arriving continuously at high velocity. Therefore, turning the nightmare of information overload into an opportunity is a key issue in solving many real-world tasks. These tasks are related not only to emergency management, where immediate response is needed, but also to day-to-day operations in areas such as finance, network security, news analysis, and social networking. Visualization may play a key role here, since it can help the users to overcome their ability to attend only a small portion of the available information, and to focus on the most relevant or most interesting aspects of the data being presented. Recent advances in visualization research have shown that individual user needs, abilities and preferences can have a significant impact on their performance and satisfaction when using visualizations. In order to understand it better, we would like to explore existing techniques in order to understand how does the choice of visualization technique affects a user’s understanding of the evolution of multifaceted data over time? Do individual user characteristics impact user performance in analyzing trends over time when using alternative time series visualizations for the same tasks? If yes, how? In addition, we would like to suggest new techniques for integrating temporal multidimensional data into a single visual image that provides an informative and engaging view of various dimensions and will allow the user simultaneously to analyze more than one aspect of the data at once. Moreover, we would like to investigate a novel way to visualize the comparison between two competing items. The research will follow the design study approach and will be supported by user studies that will be used to evaluate the usability and effectiveness of the suggested visualizations.
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