Relation-based aggregation: finding objects in large spatial datasets

Article type: Research Article

Authors: Huang, Xingang^a | Zhao, Feng^b

Affiliations: [a] Department of Computer and Information Science, The Ohio State University, 2015 Neil Avenue, Columbus, OH 43210, USA. E-mail: huang@cis.ohio-state.edu; URL: http://www.cis.ohio-state.edu/~huang | [b] Xerox Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, CA 94304, USA. E-mail: zhao@parc.xerox.com; URL: http://www.parc.xerox.com/zhao

Abstract: Regularities exist in datasets describing spatially distributed physical phenomena. Human experts often understand and verbalize the regularities as abstract spatial objects evolving coherently and interacting with each other in the domain space. We describe a novel computational approach for identifying and extracting these abstract spatial objects through the construction of a hierarchy of spatial relations. We demonstrate the approach with an application to finding pressure trough features in weather data sets.

Keywords: data analysis, multi-level reasoning, spatial reasoning, spatial data mining, weather data analysis

DOI: 10.3233/IDA-2000-4204

Journal: Intelligent Data Analysis, vol. 4, no. 2, pp. 129-147, 2000