A computational approach to edge detection
Readings in computer vision: issues, problems, principles, and paradigms
Efficient Evaluation of Aggregates on Bulk Types
DBLP-5 Proceedings of the Fifth International Workshop on Database Programming Languages
GIS: A Computing Perspective, 2nd Edition
GIS: A Computing Perspective, 2nd Edition
Discovering Flow Anomalies: A SWEET Approach
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Trajectory Outlier Detection: A Partition-and-Detect Framework
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Using Time Series Segmentation for Deriving Vegetation Phenology Indices from MODIS NDVI Data
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Change detection in time series data using wavelet footprints
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Mining robust neighborhoods for quality control of sensor data
Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoStreaming
Discovering persistent change windows in spatiotemporal datasets: a summary of results
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
Hi-index | 0.00 |
Given a spatiotemporal (ST) dataset and a path in its embedding spatiotemporal framework, the goal is to to identify all interesting sub-paths defined by an interest measure. Sub-path discovery is of fundamental importance for understanding climate changes, agriculture, and many other application. However, this problem is computationally challenging due to the massive volume of data, the varying length of sub-paths and non-monotonicity of interestingness throughout a sub-path. Previous approaches find interesting unit sub-paths (e.g., unit time interval) or interesting points. By contrast, we propose a Sub-path Enumeration and Pruning (SEP) approach that finds collections of long interesting sub-paths. Two case studies using climate change datasets show that SEP can find long interesting sub-paths which represent abrupt climate change. We provide theoretical analyses of correctness, completeness and computational complexity of the proposed approach. We also provide experimental evaluation of two traversal strategies for enumerating and pruning candidate sub-paths.