A Context-Free Method of Visualizing Streaming Object Data for the Purpose of Identifying Known Events: an Implementation and Analysis
Carver, Quinn Gregory
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This year, every second, five gigabytes of new data will be streamed to storage and yet less than half a percent of all this data will ever be analyzed. Much of this muted data is high-variety object data, unnoticed in a void of sorely needed tools to make it readily understandable. A software tool for visualizing streaming object data in a context-free manner can be built, and this tool would aid in finding predictor data for known events which are functions of the data. The design of the tool to support the hypothesis is presented and field results from over 500 test trials are analyzed. Score distributions from participants using the tool are compared to a random score distribution. Users did correctly identify more known event predictor data than they would have if selections were made at random. Therefore, the software tool is useful.