A Visualization-Analytics-Interaction Workflow Framework for Exploratory and Explanatory Search on Geo-located Search Data Using the Meme Media Digital Dashboard

摘要

Modern geo-position system (GPS) enabled smart phones are generating an increasing volume of information about their users, including geo-located search, movement, and transaction data. While this kind of data is increasingly rich and offers many grand opportunities to identify patterns and predict behaviour of groups and individuals, it is not immediately obvious how to develop a framework for extracting plausible inferences from these data. In our case, we have access to a large volume (more than half a billion individual records) of real user data from the Point smart phone application, and we have developed a generic and layered system architecture to incrementally find aggregate items of interest within that data. "Interest" is based on the semantics of the data, so include time and space correlations, e.g., Are people searching for dinner and a movie, distributions of usage patterns and platforms, e.g., Geographic distribution of Android, Apple, and Black-Berry users, and clustering to identify interesting and relatively complex search and movement patterns, e.g., Consumer trajectories from key word searches. Our integration of visualization tools is thus guided top-down, by semantic concepts in the application domain, rather than by bottom-up tool development. Our presentation here is preliminary in that we provide sketches of case-studies that demonstrate an application specific integration of the three major components of modern visual analytics: visualization, analytics, and interaction (VAI). Our case-study sketches show how an interactive system for visual data exploration can be used to alternate between exploratory search -- looking for ideas and new hypothesis in data -- and explanatory search -- looking for evidence to support a hypothesis. While we have not yet formulated experiments to directly measure the cognitive efficacy of our experimental system, we believe that our semantically-driven VAI workflows and the integration of vis- al methods and interaction provides some useful ideas about how to extend current frameworks for visual analytics systems.