Abstract
We present Discovery Dashboard, a visual analytics system for exploring large volumes of time series data from mobile medical field studies. Discovery Dashboard offers interactive exploration tools and a data mining motif discovery algorithm to help researchers formulate hypotheses, discover trends and patterns, and ultimately gain a deeper understanding of their data. Discovery Dashboard emphasizes user freedom and flexibility during the data exploration process and enables researchers to do things previously challenging or impossible to do — in the web-browser and in real time. We demonstrate our system visualizing data from a mobile sensor study conducted at the University of Minnesota that included 52 participants who were trying to quit smoking.
Highlights
When medical researchers conduct mobile sensor field studies, they often collect large amounts of time series data across many participants over prolonged periods of time
To test hypotheses and obtain a deep understanding of ones data, researchers need both low-level and high-level exploration tools for visualizing raw data, interactively inspecting it to formulate hypotheses, and discovering trends and patterns. To address both low-level and high-level exploration, we present Discovery Dashboard: a visual analytics system that offers intuitive visualization of mobile sensor time series data, supports multiple interaction techniques for data and pattern exploration, and integrates a data mining algorithm for motif discovery
Scalability for Interactive Exploration To support interactive exploration on data with high resolution, Discovery Dashboard needs to scale to large datasets; we introduced multiple caching layers to achieve such scalability
Summary
When medical researchers conduct mobile sensor field studies, they often collect large amounts of time series data across many participants over prolonged periods of time. To test hypotheses and obtain a deep understanding of ones data, researchers need both low-level and high-level exploration tools for visualizing raw data, interactively inspecting it to formulate hypotheses, and discovering trends and patterns.
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