Mining, indexing, and querying historical spatiotemporal data

  • Authors:

  • Nikos Mamoulis;Huiping Cao;George Kollios;Marios Hadjieleftheriou;Yufei Tao;David W. Cheung

  • Affiliations:

  • University of Hong Kong;University of Hong Kong;Boston Universtiy;University of California, Riverside;City University of Hong Kong;University of Hong Kong

  • Venue:
  • Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
  • Year:
  • 2004

Quantified Score

Hi-index 0.00

Visualization

Abstract

In many applications that track and analyze spatiotemporal data, movements obey periodic patterns; the objects follow the same routes (approximately) over regular time intervals. For example, people wake up at the same time and follow more or less the same route to their work everyday. The discovery of hidden periodic patterns in spatiotemporal data, apart from unveiling important information to the data analyst, can facilitate data management substantially. Based on this observation, we propose a framework that analyzes, manages, and queries object movements that follow such patterns. We define the spatiotemporal periodic pattern mining problem and propose an effective and fast mining algorithm for retrieving maximal periodic patterns. We also devise a novel, specialized index structure that can benefit from the discovered patterns to support more efficient execution of spatiotemporal queries. We evaluate our methods experimentally using datasets with object trajectories that exhibit periodicity.