Abstract

We present an active data mining mechanism for qualitative analysis of spatial datasets, integrating identification and analysis of structures in spatial data with targeted collection of additional samples. The mechanism is designed around the spatial aggregation language (SAL) for qualitative spatial reasoning, and seeks to uncover high-level spatial structures from only a sparse set of samples. This approach is important for applications in domains such as aircraft design, wireless system simulation, fluid dynamics, and sensor networks. The mechanism employs Gaussian processes, a formal mathematical model for reasoning about spatial data, in order to build surrogate models from sparse data, reason about the uncertainty of estimation at unsampled points, and formulate objective criteria for closing-the-loop between data collection and data analysis. It optimizes sample selection using entropy-based functionals defined over spatial aggregates instead of the traditional approach of sampling to minimize estimated variance. We apply this mechanism on a global optimization benchmark comprising a testbank of 2D functions, as well as on data from wireless system simulations. The results reveal that the proposed sampling strategy makes more judicious use of data points by selecting locations that clarify high-level structures in data, rather than choosing points that merely improve quality of function approximation.

Citations