S.: Similarity searches in heterogeneous feature spaces

Abstract

Abstract: Correlating event streams or development paths of observed behavior that involves disparate types of data is a common problem in many applications including biomedical and clinical diagnosis systems. We present a new formulation of the following dual problem: (a) given multiple event streams for which we have prior knowledge, specify a feature space with heterogeneous dissimilarity measures, and (b) find similar time series given these (expert) user-specified heterogeneities, both within the same feature and as combinations across multiple features. By allowing domain experts to describe their feature spaces (quantized representation of observations such as the size of an object, its primary axis, its shape, etc.) more accurately in this fashion, query matches are better suited to the domain experts ’ needs. The presented work augments the existing research of finding local similarity areas and overall patterns in time series data. Key-Words: database queries, dissimilarity measures, prior knowledge