Abstract
Contemporary challenges for efficient similarity search include complex similarity functions, the curse of dimensionality, and large sizes of descriptive features of data objects. This article reports our experience with a database of protein chains which form (almost) metric space and demonstrate the following extreme properties. Evaluation of the pairwise similarity of protein chains can take even tens of minutes, and has a variance of six orders of magnitude. The minimisation of a number of similarity comparisons is thus crucial, so we propose a generic three stage search engine to solve it. We improve the median searching time 73 times in comparison with the search engine currently employed for the protein database in practice.
V. Mic and P. Zezula—This research was supported by ERDF “CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence” (No. CZ.02.1.01/0.0/0.0/16_019/0000822). Computational resources were supplied by the project “e-Infrastruktura CZ” (e-INFRA LM2018140) provided within the program Projects of Large Research, Development and Innovations Infrastructures.
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Notes
- 1.
This is an estimation made as an extrapolation from other query executions. Our search engine evaluates approximately 1,000 distances per average query. The vast majority of distances is not stored, so we do not know the precise number of distances evaluated in less than a second.
- 2.
No caching is used here except a re-using the distances evaluated in the previous phases of the same query execution.
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Mic, V., Raček, T., Křenek, A., Zezula, P. (2021). Similarity Search for an Extreme Application: Experience and Implementation. In: Reyes, N., et al. Similarity Search and Applications. SISAP 2021. Lecture Notes in Computer Science(), vol 13058. Springer, Cham. https://doi.org/10.1007/978-3-030-89657-7_20
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