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Given a SpatioTemporal (ST) dataset and a path in its embedding spatiotemporal framework, the goal is 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, the authors propose a Sub-path Enumeration and Pruning (SEP) approach that finds collections of long interesting sub-paths.
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