@inproceedings{11095afc38ad4184b3eab45f217e09c7,
title = "Work-in-Progress: Lark: A Learned Secondary Index Toward LSM-tree for Resource-Constrained Embedded Storage Systems",
abstract = "LSM-tree-based key-value stores are popular in embedded storage systems. With the growing demands of data analysis, the secondary index is created to support non-primary-key lookups. However, the lookup efficiency and space consumption of secondary index remain for further optimization. Inspired by the learned index, this paper presents Lark, a learned secondary index toward LSM-tree for resource-constrained embedded storage systems. Lark employs machine learning to speed up the non-primary-key queries and compress secondary indexes. Our preliminary evaluations show that, in comparison with traditional secondary index schemes, Lark achieves better lookup performance with less space consumption.",
keywords = "LSM-tree, machine learning, Secondary index",
author = "Jianan Yuan and Huan Liu and Shangyu Wu and Yiquan Lin and Tiantian Wang and Chenlin Ma and Rui Mao and Yi Wang",
year = "2022",
doi = "10.1109/CODES-ISSS55005.2022.00012",
language = "English",
series = "Proceedings - International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS",
publisher = "IEEE",
pages = "11--12",
booktitle = "Proceedings - 2022 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS 2022)",
address = "United States",
note = "2022 International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2022 ; Conference date: 07-10-2022 Through 14-10-2022",
}