Similarity Search for Interval Time Sequences

Abstract

Time sequences, which are ordered sets of observations, have been studied in various database applications. In this paper, we introduce a new class of time sequences of which each observation is represented by an interval rather than a number. Such sequences may arise in many situations. For instance, we may not be able to determine the exact value at a time point due to uncertainty or aggregation. In such a case, the observation may be represented better by a range of possible values. Similarity search for interval time sequences has not been studied to the best of our knowledge and poses a new challenge for research. We first address the issue of (dis)similarity measures for interval time sequences. We choose a $\mathcal{L}_1$ norm-based measure because it is semantically better than other alternatives. We next propose an efficient indexing technique for fast retrieval of similar interval time sequences from large databases. More specifically, we propose: (1) to extract a segment-based feature vector for each sequence, and (2) to map each feature vector to either a point or a hyper-rectangle in a multi-dimensional feature space. We then show how we can use existing multi-dimensional index structures such as the R-tree for efficient query processing. Our proposed method guarantees that no false dismissals would occur.

This work was supported by the Brain Korea 21 Project in 2003.