Model-based search in large time series databases
An important theoretical topic in assistive environments is reason- ing about temporal patterns, that represent the sequential output of various sensors, and that can give us information about the health and activities of humans and the state of the environment. The re- cent growth in the quantity and quality of sensors for assistive envi- ronments has made it possible to create large databases of temporal patterns, that store sequences of observations obtained from such sensors over large time intervals. A topic of significant interest is being able to search such large databases so as to identify content of interest, for example activities of a certain type, or information about a patient’s well-being. In this paper, we study two different approaches for conducting such searches: an exemplar-based ap- proach, where we describe what we are looking for by giving an example, and a model-based approach, where we describe what we are looking for via a generative model. In particular, we describe the two different approaches, and we identify some important pros and cons for each approach. We also perform a comparative evalua- tion of exemplar-based search using dynamic time warping (DTW), and model-based search using Hidden Markov Models (HMMs), on large real datasets. In our experiments, when the number of training objects per model is sufficiently high, model-based search using HMMs produces more accurate search results than exemplar- based search using DTW.