EventsVista: Enhancing Event Visualization and Interpretation
Mohamad Khalil Farhat, Ji Zhang, Xiaohui Tao, Tianning Li, Ting Jung Yu · 2023
The surge in technological advancements in the last decade has given rise to massive amounts of data. Researchers have since developed a wide range of software to visualize extensive data sets. Data visualization techniques ensure an enhanced analysis of substantial amounts of information and bypass the difficulty in understanding the data they reflect. These techniques facilitate the collating of information and detecting the overall trends of events, which plays a critical role in different fields such as finance, medical recording, cybersecurity, and essential day-to-day tasks. However, information can be poorly translated when scaling from large datasets to representative graphics, either by causing information loss or over-crowding the audience receiving the visual in any application or software. Understanding and using the appropriate methodologies becomes critical for the authentic translation of data as well as the engagement of the audience. In this paper, we demonstrate the evolution of big data, the key elements of data visualization, and the operations conventionally used in visualizing the space-time cube. Moreover, we introduce our newly developed data visualization software, EventsVista, which displays and analyzes events based on the assessment of previous data visualization methods. EventsVista explicitly demonstrates the effect of merging conventional operations together, as well as adding novel ones based on the Degree of Freedom (DoF) strategy. Furthermore, EventsVista was tested on events taking place at the University of Southern Queensland, Toowoomba, Australia, prior to its launch for public user assessment. Positive user experience reflected the success of the DoF strategy in improving the event’s interpretation using our distinctive and publicly accessible visualization application.