A generalized framework for mining spatio-temporal patterns in scientific data

In this paper, we present a general framework to discover spatial associations and spatio-temporal episodes for scientific datasets. In contrast to previous work in this area, features are modeled as geometric objects rather than points. We define multiple distance metrics that take into account objects' extent and thus are more robust in capturing the influence of an object on other objects in spatial neighborhood. We have developed algorithms to discover four different types of spatial object interaction (association) patterns. We also extend our approach to accommodate temporal information and propose a simple algorithm to derive spatio-temporal episodes. We show that such episodes can be used to reason about critical events. We evaluate our framework on real datasets to demonstrate its efficacy. The datasets originate from two different areas: Computational Molecular Dynamics and Computational Fluid Flow. We present results highlighting the importance of the identified patter...

• Type: Conference
• Year: 2005
• Where: KDD
• Authors: Hui Yang, Srinivasan Parthasarathy, Sameep Mehta
• Keywords: Data Mining, KDD 2005, Keywords Spatial Object, Spatial Object Interaction, Spatio-temporal Episodes