@inproceedings{a7d702b713e74b418dff07084a342834,
title = "Speeding-up the similarity search in time series databases by coupling dimensionality reduction techniques with a fast-and-dirty filter",
abstract = "In this paper we present a new generic frame that boosts the performance of different time series dimensionality reduction techniques by using a fast-and-dirty filter that we combine with the lower bounding condition of the dimensionality reduction technique to increase the pruning power. This fast-and-dirty filter is based on an optimal approximation of the segmented time series. The distances between these segmented time series and their approximating functions are computed and stored at indexing-time. This step is repeated using different resolution levels which correspond to different lengths of the segments. At query-time these pre-computed distances are utilized to prune those time series which are not similar to the given pattern using the least number of query-time distance computations. We conduct experiments that validate the theoretical basis of our proposed method.",
keywords = "Dimensionality reduction techniques, Multi-resolution, Similarity search, Time series data mining",
author = "{Muhammad Fuad}, {Muhammad Marwan} and Marteau, {Pierre Fran{\c c}ois}",
year = "2010",
month = nov,
day = "11",
doi = "10.1109/ICSC.2010.34",
language = "English",
isbn = "9780769541549",
series = "Proceedings - 2010 IEEE 4th International Conference on Semantic Computing, ICSC 2010",
publisher = "IEEE",
pages = "101--104",
booktitle = "Proceedings - 2010 IEEE 4th International Conference on Semantic Computing, ICSC 2010",
address = "United States",
note = "4th IEEE International Conference on Semantic Computing, ICSC 2010 ; Conference date: 22-09-2010 Through 24-09-2010",
}