Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 24th International Conference on Extending Database Technology (EDBT) |
| Herausgeber/-innen | Yannis Velegrakis, Yannis Velegrakis, Demetris Zeinalipour, Panos K. Chrysanthis, Panos K. Chrysanthis, Francesco Guerra |
| Seiten | 331-336 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 978-3-89318-084-4 |
| Publikationsstatus | Veröffentlicht - 2021 |
Publikationsreihe
| Name | Advances in database technology |
|---|---|
| ISSN (elektronisch) | 2367-2005 |
Abstract
Calculating correlation coefficients is one of the most used measures in data science. Although linear correlations are fast and easy to calculate, they lack robustness and effectiveness in the existence of non-linear associations. Rank-based coefficients such as Spearman's are more suitable. However, rank-based measures first require to sort the values and obtain the ranks, making their calculation super-linear. One of the use-cases that is affected by this is data enrichment for Machine Learning (ML) through feature extraction from large databases. Finding the most promising features from millions of candidates to increase the ML accuracy requires billions of correlation calculations. In this paper, we introduce an index structure that ensures rank-based correlation calculation in a linear time. Our solution accelerates the correlation calculation up to 500 times in the data enrichment setting.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Information systems
- Informatik (insg.)
- Angewandte Informatik
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
Proceedings of the 24th International Conference on Extending Database Technology (EDBT). Hrsg. / Yannis Velegrakis; Yannis Velegrakis; Demetris Zeinalipour; Panos K. Chrysanthis; Panos K. Chrysanthis; Francesco Guerra. 2021. S. 331-336 (Advances in database technology).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - COCOA: COrrelation COefficient-Aware Data Augmentation
AU - Esmailoghli, Mahdi
AU - Quiané-Ruiz, Jorge-Arnulfo
AU - Abedjan, Ziawasch
N1 - Funding Information: We presented Cocoa, a new data enrichment system. It enables the efficient calculation of non-linear correlation coefficients to select the most correlating features for a user-defined ML task. In particular, we introduced an index structure that allows to calculate non-linear correlation coefficients in linear time complexity. Cocoa is designed to be general and hence it can be complemented with other table-based filters or used for any analytic task that depends on value rankings and rank-based scores. Acknowledgements. This project has been supported by the German Research Foundation (DFG) under grant agreement 387872445 and the German Ministry for Education and Research as BIFOLD — “Berlin Institute for the Foundations of Learning and Data” (01IS18025A and 01IS18037A).
PY - 2021
Y1 - 2021
N2 - Calculating correlation coefficients is one of the most used measures in data science. Although linear correlations are fast and easy to calculate, they lack robustness and effectiveness in the existence of non-linear associations. Rank-based coefficients such as Spearman's are more suitable. However, rank-based measures first require to sort the values and obtain the ranks, making their calculation super-linear. One of the use-cases that is affected by this is data enrichment for Machine Learning (ML) through feature extraction from large databases. Finding the most promising features from millions of candidates to increase the ML accuracy requires billions of correlation calculations. In this paper, we introduce an index structure that ensures rank-based correlation calculation in a linear time. Our solution accelerates the correlation calculation up to 500 times in the data enrichment setting.
AB - Calculating correlation coefficients is one of the most used measures in data science. Although linear correlations are fast and easy to calculate, they lack robustness and effectiveness in the existence of non-linear associations. Rank-based coefficients such as Spearman's are more suitable. However, rank-based measures first require to sort the values and obtain the ranks, making their calculation super-linear. One of the use-cases that is affected by this is data enrichment for Machine Learning (ML) through feature extraction from large databases. Finding the most promising features from millions of candidates to increase the ML accuracy requires billions of correlation calculations. In this paper, we introduce an index structure that ensures rank-based correlation calculation in a linear time. Our solution accelerates the correlation calculation up to 500 times in the data enrichment setting.
UR - http://www.scopus.com/inward/record.url?scp=85108943340&partnerID=8YFLogxK
U2 - 10.5441/002/EDBT.2021.30
DO - 10.5441/002/EDBT.2021.30
M3 - Conference contribution
T3 - Advances in database technology
SP - 331
EP - 336
BT - Proceedings of the 24th International Conference on Extending Database Technology (EDBT)
A2 - Velegrakis, Yannis
A2 - Velegrakis, Yannis
A2 - Zeinalipour, Demetris
A2 - Chrysanthis, Panos K.
A2 - Chrysanthis, Panos K.
A2 - Guerra, Francesco
ER -