Interactive Visual Analysis of Streaming Data

Abstract

Interactive Visual Analysis (IVA) has proven to be a robust set of methods for visually exploring complex data sets and generating hypotheses from data. Datasets and techniques where the temporal aspect is central has been an important area of study, both for the visualization field in general and for research on IVA. However, the challenge of handling streaming data sources for the purposes of decision support and analysis in real time, has been given comparatively little attention. This thesis presents a summary of the visualization literature addressing time-oriented and streaming data, with emphasis on Interactive Visual Analysis and its related techniques. We then explain the contemporary distinction between real-time data monitoring and retrospective data analysis, explore challenges that occur when a human user attempts to visually analyze data in real time, and use these observations to extend the scope of IVA such that it can be used to analyze streaming data in real time.