Neighborhood based detection of anomalies in high dimensional spatio-temporal Sensor Datasets

摘要

The behavior of spatial objects is under the influence of nearby spatial processes. Therefore in order to perform any type of spatial analysis we need to take into account not only the spatial relationships among objects but also the underlying spatial processes and other spatial features in the vicinity that influence the behavior of a given spatial object. In this paper, we address the outlier detection by refining the concept of a neighborhood of an object, which essentially characterizes similarly behaving objects into one neighborhood. This similarity is quantified in terms of the spatial relationships among the objects and other semantic relationships based on the spatial processes and spatial features in their vicinity. These spatial features could be natural such as a stream, and vegetation, or man-made such as a bridge, railroad, and chemical factory. The paper also addresses the identification of spatio-temporal outliers in high dimensions, in their neighborhood.