Efficiently supporting ad hoc queries in large datasets of time sequences
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Indexing spatio-temporal trajectories with Chebyshev polynomials
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast retrieval of time series using a multi-resolution filter with multiple reduced spaces
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Multiresolution similarity search in time series data: an application to EEG signals
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
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We propose a new multi-resolution indexing and retrieval method of the similarity search problem in time series databases. The proposed method is based on a fast-and-dirty filtering scheme that iteratively reduces the search space using several resolution levels. For each resolution level the time series are approximated by an appropriate function. The distance between the time series and the approximating function is computed and stored at indexing-time. At query-time, assigned filters use these pre-computed distances to exclude wide regions of the search space, which do not contain answers to the query, using the least number of query-time distance computations. The resolution level is progressively increased to converge towards higher resolution levels where the exclusion power rises, but the cost of query-time distance computations also increases. The proposed method uses lower bounding distances, so there are no false dismissals, and the search process returns all the possible answers to the query. A post-processing scanning on the candidate response set is performed to filter out any false alarms and return the final response set. We present experimentations that compare our method with sequential scanning on different datasets, using different threshold values and different approximating functions. The experiments show that our new method is faster than sequential scanning by an order of magnitude.