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Fractals for secondary key retrieval

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S. RosemanAuthors Info & Claims

PODS '89: Proceedings of the eighth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems

Pages 247 - 252

https://doi.org/10.1145/73721.73746

Published: 29 March 1989 Publication History

Abstract

In this paper we propose the use of fractals and especially the Hilbert curve, in order to design good distance-preserving mappings. Such mappings improve the performance of secondary-key- and spatial- access methods, where multi-dimensional points have to be stored on an 1-dimensional medium (e.g., disk). Good clustering reduces the number of disk accesses on retrieval, improving the response time. Our experiments on range queries and nearest neighbor queries showed that the proposed Hilbert curve achieves better clustering than older methods (“bit-shuffling”, or Peano curve), for every situation we tried.

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Cited By

Gao JDing JSudhir SMadden S(2024)Learning Bit Allocations for Z-Order Layouts in Analytic Data SystemsProceedings of the Seventh International Workshop on Exploiting Artificial Intelligence Techniques for Data Management10.1145/3663742.3663975(1-9)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3663742.3663975
Qiu QZhang YGe WWang C(2024)Edge Pair-Based Layout Pattern Matching using Space-filling Curve2024 2nd International Symposium of Electronics Design Automation (ISEDA)10.1109/ISEDA62518.2024.10617982(404-409)Online publication date: 10-May-2024
https://doi.org/10.1109/ISEDA62518.2024.10617982
Pienaar JBoman AMalan K(2024)Hilbert Curves for Efficient Exploratory Landscape Analysis Neighbourhood SamplingApplications of Evolutionary Computation10.1007/978-3-031-56855-8_18(293-309)Online publication date: 3-Mar-2024
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Index Terms

Fractals for secondary key retrieval
1. Information systems
  1. Data management systems
    1. Data structures
      1. Data access methods
    2. Database management system engines
      1. Database query processing
  2. Information retrieval
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query processing and optimization (theory)

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cover image ACM Conferences

PODS '89: Proceedings of the eighth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems

March 1989

401 pages

ISBN:0897913086

DOI:10.1145/73721

Chairman:
Avi Silberschatz
Univ. of Texas, Austin

Copyright © 1989 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 29 March 1989

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Cited By

Gao JDing JSudhir SMadden S(2024)Learning Bit Allocations for Z-Order Layouts in Analytic Data SystemsProceedings of the Seventh International Workshop on Exploiting Artificial Intelligence Techniques for Data Management10.1145/3663742.3663975(1-9)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3663742.3663975
Qiu QZhang YGe WWang C(2024)Edge Pair-Based Layout Pattern Matching using Space-filling Curve2024 2nd International Symposium of Electronics Design Automation (ISEDA)10.1109/ISEDA62518.2024.10617982(404-409)Online publication date: 10-May-2024
https://doi.org/10.1109/ISEDA62518.2024.10617982
Pienaar JBoman AMalan K(2024)Hilbert Curves for Efficient Exploratory Landscape Analysis Neighbourhood SamplingApplications of Evolutionary Computation10.1007/978-3-031-56855-8_18(293-309)Online publication date: 3-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56855-8_18
Sun LJin B(2023)Improving NoSQL Spatial-Query Processing with Server-Side In-Memory R*-Tree Indexes for Spatial Vector DataSustainability10.3390/su1503244215:3(2442)Online publication date: 30-Jan-2023
https://doi.org/10.3390/su15032442
Wang LHu LFu CYu YTang PZhang FLiu R(2023)SLBRIN: A Spatial Learned Index Based on BRINISPRS International Journal of Geo-Information10.3390/ijgi1204017112:4(171)Online publication date: 15-Apr-2023
https://doi.org/10.3390/ijgi12040171
Li JWang ZCong GLong CKiah HCui B(2023)Towards Designing and Learning Piecewise Space-Filling CurvesProceedings of the VLDB Endowment10.14778/3598581.359858916:9(2158-2171)Online publication date: 1-May-2023
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Gu TFeng KCong GLong CWang ZWang S(2023)The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial DataProceedings of the ACM on Management of Data10.1145/35889171:1(1-26)Online publication date: 30-May-2023
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Owczarek VFranco PMullot R(2023)An alternative for data visualization using space-filling curveData Mining and Knowledge Discovery10.1007/s10618-023-00943-737:6(2281-2305)Online publication date: 7-Aug-2023
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Tian FWu ZGui XNi JShen X(2022)Fine-Grained Query Authorization With Integrity Verification Over Encrypted Spatial Data in Cloud StorageIEEE Transactions on Cloud Computing10.1109/TCC.2020.301091510:3(1831-1847)Online publication date: 1-Jul-2022
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Yan JDing ZZhang S(2022)An Object Separated Storage Framework Towards Spatiotemporal Point Data Fast QueryInformation Integration and Web Intelligence10.1007/978-3-031-21047-1_19(211-223)Online publication date: 28-Nov-2022
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