Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scaling Theorems for Zero-Crossings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scaling Theorems for Zero Crossings of Bandlimited Signals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern matching algorithms
Maintaining knowledge about temporal intervals
Communications of the ACM
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Algorithmics and applications of tree and graph searching
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Time Granularities in Databases, Data Mining and Temporal Reasoning
Time Granularities in Databases, Data Mining and Temporal Reasoning
Discovery of Temporal Patterns. Learning Rules about the Qualitative Behaviour of Time Series
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
A survey on tree edit distance and related problems
Theoretical Computer Science
Graph Theory: Modeling, Applications, and Algorithms
Graph Theory: Modeling, Applications, and Algorithms
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Processes and events in dynamic geo-networks
GeoS'05 Proceedings of the First international conference on GeoSpatial Semantics
Connecting the dots: constructing spatiotemporal episodes from events schemas
Transactions on Computational Science VI
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This paper introduces a novel framework for deriving and mining hierarchical event structures of spatiotemporal phenomena in data from in-situ sensor measurements. The framework builds on the hierarchical event schema as a cogitative construct for the understanding of dynamic phenomena and on the granularity tree as a hierarchical ontological construct for spatiotemporal phenomena. The construction of event schemas (and granularity trees) is carried out using scale-space analysis from which the interval tree, a hierarchical decomposition of the data is derived. We show that the interval tree fulfills the Axioms and conditions of both time granularity and granularity trees, and expand the granularity tree construct to support temporal order constraints. Once hierarchical decomposition is derived, the data mining problem is transformed to an ordered tree matching problem.