Learned Spatial Data Partitioning-Reference-Cited by-同舟云学术

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Learned Spatial Data Partitioning

Published:2023-06-18 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Sixth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management
language:
Short-container-title:

Author:

Hori Keizo¹^ORCID,Sasaki Yuya¹^ORCID,Amagata Daichi¹^ORCID,Murosaki Yuki¹^ORCID,Onizuka Makoto¹^ORCID

Affiliation:

1. Osaka University, Suita, Japan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3593078.3593932

Reference37 articles.

1. Hadoop GIS

2. AQWA

3. Apache Sedona. 2021. https://sedona.apache.org/. Apache Sedona. 2021. https://sedona.apache.org/.

4. Kristopher De Asis , J. Fernando Hernandez-Garcia , G. Zacharias Holland , and Richard S . Sutton . 2018 . Multi-step Reinforcement Learning: A Unifying Algorithm . arXiv (2018). Kristopher De Asis, J. Fernando Hernandez-Garcia, G. Zacharias Holland, and Richard S. Sutton. 2018. Multi-step Reinforcement Learning: A Unifying Algorithm. arXiv (2018).

5. Jialin Ding , Vikram Nathan , Mohammad Alizadeh , and Tim Kraska . 2020 . Tsunami: A learned multi-dimensional index for correlated data and skewed workloads. PVLDB (2020), 74--86. Jialin Ding, Vikram Nathan, Mohammad Alizadeh, and Tim Kraska. 2020. Tsunami: A learned multi-dimensional index for correlated data and skewed workloads. PVLDB (2020), 74--86.

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