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TODS3603-17 ACM-TRANSACTION August 2, 2011 16:25 Embedding-Based Subsequence Matching in Time-Series Databases

Summary: TODS3603-17 ACM-TRANSACTION August 2, 2011 16:25
Embedding-Based Subsequence Matching in Time-Series Databases
PANAGIOTIS PAPAPETROU, Aalto University, Finland
VASSILIS ATHITSOS, University of Texas at Arlington, TX
DIMITRIOS GUNOPULOS, University of Athens, Greece
We propose an embedding-based framework for subsequence matching in time-series databases that im-
proves the efficiency of processing subsequence matching queries under the Dynamic Time Warping (DTW)
distance measure. This framework partially reduces subsequence matching to vector matching, using an
embedding that maps each query sequence to a vector and each database time series into a sequence of vec-
tors. The database embedding is computed offline, as a preprocessing step. At runtime, given a query object,
an embedding of that object is computed online. Relatively few areas of interest are efficiently identified in
the database sequences by comparing the embedding of the query with the database vectors. Those areas
of interest are then fully explored using the exact DTW-based subsequence matching algorithm. We apply
the proposed framework to define two specific methods. The first method focuses on time-series subsequence
matching under unconstrained Dynamic Time Warping. The second method targets subsequence matching
under constrained Dynamic Time Warping (cDTW), where warping paths are not allowed to stray too much
off the diagonal. In our experiments, good trade-offs between retrieval accuracy and retrieval efficiency are
obtained for both methods, and the results are competitive with respect to current state-of-the-art methods.


Source: Athitsos, Vassilis - Department of Computer Science and Engineering, University of Texas at Arlington


Collections: Computer Technologies and Information Sciences