Similarity Search Based on Shape k-d Tree for Multidimensional Time Sequences

Abstract

Multidimensional time sequences are an important kind of data stored in the information system. Similarity search is the core of their applications. Usually, these sequences are viewed as curves in multi-space, and the Euclidean Distance is computed to measure similarity between these curves. Although Euclidean Distance can reflect the whole deviation between two sequences or subsequences, it ignores their inherent changing features. To remedy it, this paper presents a new algorithm. In this algorithm, the shape features of sequences or subsequences are subtly combined with spatial index structure (k-d tree), which makes it possible to match shape of sequences or subsequences without any extra cost whiling searching the tree. The experimental result demonstrates that the algorithm is effective and efficient.