Visual Analytics at Scale: Supporting Bottom-up Explorations of Data

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The University of Victoria, Defence Research and Development Canada, and Thales Group propose to develop visualizations and innovative user interfaces to support bottom-up explorations of massive, multidimensional, temporal datasets. Visualizing data on a massive scale--Big Data--is a need facing the information visualization and visual analytics community. Existing tools from industry tend to focus on top-down exploration and analyses that supports trend and pattern discovery. In these cases, the user may have hypotheses or clear goal-oriented questions about the data at hand, and can specify high-level views and queries of the data available to meet their information needs. In contrast, bottom-up data exploration is a need that occurs across many information domains where the information and files to be analyzed may not be known a priori. This kind of situation occurs frequently in domains such as social media analysis, cyber security and forensics, where the analyst may require analysis and presentation tools to explore an unfamiliar mixture of different, possibly dynamic, schemas. Although the analysts from such domains have strong technical skills, finding a good starting point for the exploration is often challenging. From our review of the literature and examination and trials of available solutions, current visual analytic approaches either do not scale to the massive datasets we have encountered, lack appropriate cognitive support and visual metaphors for bottom-up explorations, or lack collaboration support. To address this shortfall, we aim to develop innovative techniques that will scale and support bottom-up investigations of large and complex datasets. We will provide insights on tool requirements, design tool prototypes, and evaluate these tools and requirements in industrial settings.