Learning Multi-Dimensional Indexes

@article{Nathan2020LearningMI,
  title={Learning Multi-Dimensional Indexes},
  author={V. Nathan and J. Ding and M. Alizadeh and T. Kraska},
  journal={Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data},
  year={2020}
}
  • V. Nathan, J. Ding, +1 author T. Kraska
  • Published 2020
  • Computer Science
  • Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
  • Scanning and filtering over multi-dimensional tables are key operations in modern analytical database engines. To optimize the performance of these operations, databases often create clustered indexes over a single dimension or multi-dimensional indexes such as R-Trees, or use complex sort orders (e.g., Z-ordering). However, these schemes are often hard to tune and their performance is inconsistent across different datasets and queries. In this paper, we introduce Flood, a multi-dimensional in… CONTINUE READING
    20 Citations
    Qd-tree: Learning Data Layouts for Big Data Analytics
    • 10
    • PDF
    Leveraging Soft Functional Dependencies for Indexing Multi-dimensional Data
    • 2
    • Highly Influenced
    ALEX: An Updatable Adaptive Learned Index
    • 34
    • PDF
    Cuckoo index
    Cuckoo Index: A Lightweight Secondary Index Structure
    • PDF
    A Tutorial on Learned Multi-dimensional Indexes
    Effectively learning spatial indices
    • 1
    • PDF

    References

    SHOWING 1-2 OF 2 REFERENCES
    The Grid File : An Adaptable , Symmetric Multikey File Structure
    • 193
    • Highly Influential
    • PDF
    A computer Oriented Geodetic Data Base; and a New Technique in File Sequencing (PDF)
    • Technical Report. IBM
    • 1966