Bounded Similarity Querying for Time-Series Data
Information and Computation (I&C), 194(2):203-241, November 2004.
Dina Goldin, Todd Millstein, Ayferi Kutlu
We define the problem of bounded similarity querying in time-series
databases, which generalizes earlier notions of similarity
querying. Given a (sub)sequence S, a query sequence Q,
lower and upper
bounds on shifting and scaling parameters, and a tolerance
considered boundedly similar to Q if S can be shifted and scaled
within the specified bounds to produce a modified sequence S' whose
distance from Q is within &epsilon.
We use similarity transformation to
formalize the notion of bounded similarity. We then describe a
framework that supports the resulting set of queries; it is based on a
fingerprint method that normalizes the data and saves the
normalization parameters. For off-line data, we provide an indexing
method with a single index structure and search technique for handling
all the special cases of bounded similarity querying. Experimental
investigations find the performance of our method to be competitive
with earlier, less general approaches.