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A generic database indexing framework for large-scale geographic knowledge graphs

Authors:

Mohamed SarwatAuthors Info & Claims

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 289 - 298

https://doi.org/10.1145/3274895.3274966

Published: 06 November 2018 Publication History

Abstract

The paper proposes Riso-Tree, a generic indexing framework for geographic knowledge graphs. Riso-Tree enables fast execution of graph queries that involve spatial predicates (aka. GraSp). The proposed framework augments the classic R-Tree structure with pre-materialized sub-graph entries. Riso-Tree first partitions the graph into sub-graphs based on their connectivity to the spatial sub-regions. The proposed index allows for fast execution of GraSp queries by efficiently pruning the traversed vertexes/edges based upon the materialized sub-graph information. The experiments show that the proposed Riso-Tree achieves up to two orders magnitude faster execution time than its counterparts when executing GraSp queries on real knowledge graphs (e.g., WikiData).

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Cited By

Wei BGuo XLi XWu ZZhao JZou Q(2024)Construct and Query A Fine-Grained Geospatial Knowledge GraphData Science and Engineering10.1007/s41019-023-00237-49:2(152-176)Online publication date: 22-Jan-2024
https://doi.org/10.1007/s41019-023-00237-4
Al Rhman Sarsour ABouros PChondrogiannis T(2023)Towards Generating Realistic Geosocial NetworksProceedings of the 7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising10.1145/3615896.3628340(25-28)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3615896.3628340
Han BQu TTong XWang HLiu HHuo YCheng C(2023)AugGKG: a grid-augmented geographic knowledge graph representation and spatio-temporal query modelInternational Journal of Digital Earth10.1080/17538947.2023.229056916:2(4934-4957)Online publication date: 11-Dec-2023
https://doi.org/10.1080/17538947.2023.2290569
Show More Cited By

Index Terms

A generic database indexing framework for large-scale geographic knowledge graphs
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Graph-based database models
        Hierarchical data models
    2. Database management system engines
      1. Database query processing
        Query optimization
  2. Information systems applications
    1. Collaborative and social computing systems and tools
      1. Wikis
    2. Spatial-temporal systems
      1. Geographic information systems

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Published In

cover image ACM Conferences

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2018

655 pages

ISBN:9781450358897

DOI:10.1145/3274895

General Chairs:
Farnoush Banaei-Kashani
University of Colorado, Denver
,
Erik Hoel
Esri
,
Program Chairs:
Ralf Hartmut Güting
FernUniversität in Hagen, Germany
,
Roberto Tamassia
Brown University
,
Li Xiong
Emory University

Copyright © 2018 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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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

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Published: 06 November 2018

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National Science Foundation

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SIGSPATIAL '18

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SIGSPATIAL

SIGSPATIAL '18: 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 6 - 9, 2018

Washington, Seattle

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SIGSPATIAL '18 Paper Acceptance Rate 30 of 150 submissions, 20%;

Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Citations

Cited By

Wei BGuo XLi XWu ZZhao JZou Q(2024)Construct and Query A Fine-Grained Geospatial Knowledge GraphData Science and Engineering10.1007/s41019-023-00237-49:2(152-176)Online publication date: 22-Jan-2024
https://doi.org/10.1007/s41019-023-00237-4
Al Rhman Sarsour ABouros PChondrogiannis T(2023)Towards Generating Realistic Geosocial NetworksProceedings of the 7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising10.1145/3615896.3628340(25-28)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3615896.3628340
Han BQu TTong XWang HLiu HHuo YCheng C(2023)AugGKG: a grid-augmented geographic knowledge graph representation and spatio-temporal query modelInternational Journal of Digital Earth10.1080/17538947.2023.229056916:2(4934-4957)Online publication date: 11-Dec-2023
https://doi.org/10.1080/17538947.2023.2290569
Wei BGuo XWu ZZhao JZou Q(2023)Construct Fine-Grained Geospatial Knowledge GraphDatabase Systems for Advanced Applications. DASFAA 2023 International Workshops10.1007/978-3-031-35415-1_19(267-282)Online publication date: 28-Sep-2023
https://doi.org/10.1007/978-3-031-35415-1_19
Jiang BYou XLi KLi TZhou XTan L(2022)Interactive Analysis of Epidemic Situations Based on a Spatiotemporal Information Knowledge Graph of COVID-19IEEE Access10.1109/ACCESS.2020.303399710(46782-46795)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2020.3033997
Rahimi MMalek MClaramunt CLe Pors T(2021)A topology-based graph data model for indoor spatial-social networkingInternational Journal of Geographical Information Science10.1080/13658816.2021.1912349(1-23)Online publication date: 14-Apr-2021
https://doi.org/10.1080/13658816.2021.1912349
Sun YYu JSarwat M(2019)Demonstrating Spindra: A Geographic Knowledge Graph Management System2019 IEEE 35th International Conference on Data Engineering (ICDE)10.1109/ICDE.2019.00235(2044-2047)Online publication date: Apr-2019
https://doi.org/10.1109/ICDE.2019.00235

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