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 influence of a temporally long-term entitiy on its spatiotemporal neighbhorhood (e.g. major construction –> more traffic issue).

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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.