Short and Long-term Pattern Discovery Over Large-Scale Geo-Spatiotemporal Data
Published in The 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD 2019). (Anchorage, AK), 2019
Authors
Sobhan Moosavi, Mohammad Hossein Samavatian, Arnab Nandi, Srinivasan Parthasarathy, Rajiv Ramnath
Summary and Download
This paper is a new framework to discovery short and long-term patterns over geo-spatiotemporal data (e.g. traffic, weather, etc.). Short-term patterns show propagation of geo-spatiotemporal entities which cause other entities to happen (e.g. rain –> accident –> traffic jam). Long-term patterns show the influence of a temporally long-term entity on its spatiotemporal neighborhood (e.g. major construction –> more traffic issue).
Download poster presented at KDD here
Access list of all 90 unique propagation (short-term) patterns here
Recognition and Awards
This paper received the best poster award in the 2nd Workshop on Data-driven Intelligent Transportation (DIT 2019), jointly held with the ACM SIGKDD 2019.
