Characterizing sensor datasets with multi-granular spatio-temporal intervals
Michael P. McGuire, Vandana Pursnani Janeja, Aryya Gangopadhyay · 2011
Data from sensors and sensor networks are being collected at astronomical rates. This results in a massive dataset that is increasingly difficult to navigate to find interesting time periods where the spatial pattern of a process changes. The ability to navigate to such areas can lead to new knowledge about the factors that contribute to a spatio-temporal process. This paper proposes a method to automatically characterize sensor datasets based on a measure of spatial change over time resulting in a set of multi-granular spatio-temporal intervals. The resulting intervals can be used to focus knowledge discovery tasks at multiple temporal granularities within the dataset. Furthermore, the intervals enable a drill-down-style analysis where events of varying magnitudes can be identified within each granularity. Experiments were performed on a real-world dataset measuring NEXRAD precipitation accumulation. The results show that the multi-granular spatio-temporal intervals identify interesting time periods in the dataset as evidenced by naturally occurring events.