On the Choice of General Purpose Classifiers in Learned Bloom Filters: An Initial Analysis Within Basic Filters
Contributo in Atti di convegno
Data di Pubblicazione:
2022
Citazione:
On the Choice of General Purpose Classifiers in Learned Bloom Filters: An Initial Analysis Within Basic Filters / M. Frasca, D. Malchiodi, R. Giancarlo, D. Raimondi, G. Fumagalli - In: Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods / [a cura di] M. De Marsico, G. Sanniti di Baja, A. Fred. - Prima edizione. - [s.l] : SciTePress, 2022. - ISBN 978-989-758-549-4. - pp. 675-682 (( Intervento presentato al 11. convegno International Conference on Pattern Recognition Applications and Methods ICPRAM 2022 tenutosi a Evento online nel 2022 [10.5220/0010889000003122].
Abstract:
Bloom Filters are a fundamental and pervasive data structure. Within the growing area of Learned Data Structures, several Learned versions of Bloom Filters have been considered, yielding advantages over classic Filters. Each of them uses a classifier, which is the Learned part of the data structure. Although it has a central role in those new filters, and its space footprint as well as classification time may affect the performance of the Learned Filter, no systematic study of which specific classifier to use in which circumstances is available.
We report progress in this area here, providing also initial guidelines on which classifier to choose among five classic classification paradigms.
Tipologia IRIS:
03 - Contributo in volume
Keywords:
Learned Bloom Filters; Learned Data Structures; Information Retrieval; Classification
Elenco autori:
M. Frasca, D. Malchiodi, R. Giancarlo, D. Raimondi, G. Fumagalli
Link alla scheda completa:
Titolo del libro:
Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods