Similarity Search for an Extreme Application: Experience and Implementation

Investor logo

Warning

This publication doesn't include Faculty of Medicine. It includes Faculty of Informatics. Official publication website can be found on muni.cz.

Authors

MÍČ Vladimír RAČEK Tomáš KŘENEK Aleš ZEZULA Pavel

Year of publication 2021
Type Article in Proceedings
Conference Similarity Search and Applications: 14th International Conference, SISAP 2021, Dortmund, Germany, September 29 - October 1, 2021, Proceedings
MU Faculty or unit

Faculty of Informatics

Citation
Web https://link.springer.com/chapter/10.1007/978-3-030-89657-7_20
Doi http://dx.doi.org/10.1007/978-3-030-89657-7_20
Keywords Similarity search in metric space;Efficiency;Distance distribution;Dimensionality curse;Extreme distance function
Description 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.
Related projects:

You are running an old browser version. We recommend updating your browser to its latest version.

More info