Multi-level abstractions and multi-dimensional retrieval of cases with time series features

Abstract

Time series retrieval is a critical issue in all domains in which the observed phenomenon dynamics have to be dealt with. In this paper, we propose a novel, domain independent time series retrieval framework, based on Temporal Abstractions (TA). Our framework allows for multi-level abstractions, according to two dimensions, namely a taxonomy of (trend or state) symbols, and a variety of time granularities. Moreover, we allow for flexible querying, where queries can be expressed at any level of detail in both dimensions, also in an interactive fashion, and ground cases as well as generalized ones can be retrieved. We also take advantage of multi-dimensional orthogonal index structures, which can be refined progressively and on demand. The framework in practice is illustrated by means of a case study in hemodialysis. © 2009 Springer Berlin Heidelberg.