@inproceedings{d67c58dd349c4d2fb390d6e84cd5fbfc,
title = "Reinforcement Learning for Time-Series Query Optimization",
abstract = "Time-series databases (TSDBs) are essential for managing large-scale time-series data in fields like finance, IoT, and agriculture. However, traditional query optimization methods, such as dynamic programming, struggle with high computational complexity and inaccurate cost estimates. This paper proposes a novel query optimization module for TSDBs using reinforcement learning (RL), specifically Deep Q-Networks (DQN) and Double Deep Q-Networks (DDQN). These algorithms dynamically learn optimal join orders based on query workloads and connection costs. Experiments show that RL-based methods achieve better optimization performance and stability compared to traditional heuristics, especially under complex cost models. This work highlights the potential of RL in improving query optimization for TSDBs.",
keywords = "Distributed Indexing, LSM-Tree, Non-Primary Key Query",
author = "Songling Zou and Yang, \{Dong Hua\} and Mengmeng Li and Haifeng Guo and Hongqiang Wang and Hongzhi Wang",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 11th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2025 ; Conference date: 19-09-2025 Through 21-09-2025",
year = "2026",
doi = "10.1007/978-981-95-2566-9\_19",
language = "英语",
isbn = "9789819525652",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "271--281",
editor = "Yi Yu and Haiwei Pan and Qilong Han and Hongzhi Wang and Chen Yu and Haiyi Liu and Xianhua Song and Zeguang Lu",
booktitle = "Data Science - 11th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2025, Proceedings",
address = "德国",
}