TY - GEN
T1 - RadixSpline
T2 - 3rd International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, aiDM 2020
AU - Kipf, Andreas
AU - Marcus, Ryan
AU - Van Renen, Alexander
AU - Stoian, Mihail
AU - Kemper, Alfons
AU - Kraska, Tim
AU - Neumann, Thomas
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/6/14
Y1 - 2020/6/14
N2 - Recent research has shown that learned models can outperform state-of-the-art index structures in size and lookup performance. While this is a very promising result, existing learned structures are often cumbersome to implement and are slow to build. In fact, most approaches that we are aware of require multiple training passes over the data. We introduce RadixSpline (RS), a learned index that can be built in a single pass over the data and is competitive with state-of-the-art learned index models, like RMI, in size and lookup performance. We evaluate RS using the SOSD benchmark and show that it achieves competitive results on all datasets, despite the fact that it only has two parameters.
AB - Recent research has shown that learned models can outperform state-of-the-art index structures in size and lookup performance. While this is a very promising result, existing learned structures are often cumbersome to implement and are slow to build. In fact, most approaches that we are aware of require multiple training passes over the data. We introduce RadixSpline (RS), a learned index that can be built in a single pass over the data and is competitive with state-of-the-art learned index models, like RMI, in size and lookup performance. We evaluate RS using the SOSD benchmark and show that it achieves competitive results on all datasets, despite the fact that it only has two parameters.
UR - http://www.scopus.com/inward/record.url?scp=85087656827&partnerID=8YFLogxK
U2 - 10.1145/3401071.3401659
DO - 10.1145/3401071.3401659
M3 - Conference contribution
AN - SCOPUS:85087656827
T3 - Proceedings of the 3rd International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, aiDM 2020
BT - Proceedings of the 3rd International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, aiDM 2020
PB - Association for Computing Machinery, Inc
Y2 - 19 June 2020
ER -