ANTOL, Matej, Jaroslav OĽHA, Terézia SLANINÁKOVÁ and Vlastislav DOHNAL. Learned metric index - proposition of learned indexing for unstructured data. Information Systems. Elsevier, 2021, vol. 100, No 101774, p. 1-12. ISSN 0306-4379. Available from: https://dx.doi.org/10.1016/j.is.2021.101774. |
Other formats:
BibTeX
LaTeX
RIS
@article{1757938, author = {Antol, Matej and Oľha, Jaroslav and Slanináková, Terézia and Dohnal, Vlastislav}, article_number = {101774}, doi = {http://dx.doi.org/10.1016/j.is.2021.101774}, keywords = {Index structures;Learned index;Unstructured data;Content-based search;Metric space}, language = {eng}, issn = {0306-4379}, journal = {Information Systems}, title = {Learned metric index - proposition of learned indexing for unstructured data}, url = {http://dx.doi.org/10.1016/j.is.2021.101774}, volume = {100}, year = {2021} }
TY - JOUR ID - 1757938 AU - Antol, Matej - Oľha, Jaroslav - Slanináková, Terézia - Dohnal, Vlastislav PY - 2021 TI - Learned metric index - proposition of learned indexing for unstructured data JF - Information Systems VL - 100 IS - 101774 SP - 1-12 EP - 1-12 PB - Elsevier SN - 03064379 KW - Index structures;Learned index;Unstructured data;Content-based search;Metric space UR - http://dx.doi.org/10.1016/j.is.2021.101774 N2 - The main paradigm of similarity searching in metric spaces has remained mostly unchanged for decades - data objects are organized into a hierarchical structure according to their mutual distances, using representative pivots to reduce the number of distance computations needed to efficiently search the data. We propose an alternative to this paradigm, using machine learning models to replace pivots, thus posing similarity search as a classification problem, which stands in for numerous expensive distance computations. Even a relatively naive implementation of this idea is more than competitive with state-of-the-art methods in terms of speed and recall, proving the concept as viable and showing great potential for its future development. ER -
ANTOL, Matej, Jaroslav OĽHA, Terézia SLANINÁKOVÁ and Vlastislav DOHNAL. Learned metric index - proposition of learned indexing for unstructured data. \textit{Information Systems}. Elsevier, 2021, vol.~100, No~101774, p.~1-12. ISSN~0306-4379. Available from: https://dx.doi.org/10.1016/j.is.2021.101774.
|