Time-Expanded Graphs for Flow-Dependent Transit Times
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Time Series Segmentation for Context Recognition in Mobile Devices
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Discovering and Summarising Regions of Correlated Spatio-Temporal Change in Evolving Graphs
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Change analysis in spatial datasets by interestingness comparison
SIGSPATIAL Special
Change detection in rainfall and temperature patterns over India
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
Temporal Neighborhood Discovery Using Markov Models
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Time-Aggregated graphs for modeling spatio-temporal networks
CoMoGIS'06 Proceedings of the 2006 international conference on Advances in Conceptual Modeling: theory and practice
An evolutionary approach to pattern-based time series segmentation
IEEE Transactions on Evolutionary Computation
Exploring multivariate spatio-temporal change in climate data using image analysis techniques
Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications
Mining robust neighborhoods for quality control of sensor data
Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoStreaming
Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets
Data Mining and Knowledge Discovery
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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.