Julia Sheidin, is a PhD student in the Information Systems Department at University of Haifa under the supervision of Prof. Tsvi Kuflik and Prof. Joel Lanir. Her research examines how to explore the potential of Visual Analytics in reducing the overload users are facing when dealing with vast amounts of Multifaceted Temporal data. Julia did her Master degree in Information Systems (in University of Haifa), where she was a part of the collaboration project between the University of Haifa and the Italian Research Institute ITC-irst. The aim of the project was developing a Multimedia Mobile guide based PDA (Pocket PC), which allows group guided tours at the museum as a substitute for a human instructor. Julia’s master thesis topic was to design, implement and evaluate an adaptive user interface for mobile museum visitor’s guide. Afterwards, Julia worked for 7 years at Espro Acoustiguide Group, where her main job was to characterize, design and development of mobile applications in various work environments (iOS and Android products, company’s multimedia product). She is a full-time teaching faculty at Braude College in Karmiel, Israel, where she teaches Introduction to Software Engineering, Human Computer Interactions and Information Visualization.
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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