Abstract
We develop a highly efficient access method, called Delta-Top-Index, to answer top-k subsequence matching queries over a time series data set. Compared to a naïve implementation, our index has a storage cost that is up to two orders of magnitude smaller, while providing answers within microseconds. We demonstrate the efficiency and effectiveness of our technique in an experimental evaluation with real-world data.
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Notes
- 1.
Altogether there are around twenty different parameters.
- 2.
We use integers to simplify the matter, the actual temperatures are represented by real numbers.
- 3.
Remember that \(P^{\varvec{x}\varvec{y}}[i,0] := 0\) and \(P^{\varvec{x}\varvec{y}}[0,j] := 0\).
- 4.
We avoid race conditions by protecting top list modifications with a critical section.
- 5.
We will look at a more sophisticated implementation in the following section.
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Piatov, D., Helmer, S., Gamper, J. (2017). Interactive Time Series Subsequence Matching. In: Kirikova, M., Nørvåg, K., Papadopoulos, G. (eds) Advances in Databases and Information Systems. ADBIS 2017. Lecture Notes in Computer Science(), vol 10509. Springer, Cham. https://doi.org/10.1007/978-3-319-66917-5_6
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DOI: https://doi.org/10.1007/978-3-319-66917-5_6
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