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

Yuhan Sun, Mohamed Elsayed

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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).

Original languageEnglish (US)
Title of host publication26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
EditorsLi Xiong, Roberto Tamassia, Kashani Farnoush Banaei, Ralf Hartmut Guting, Erik Hoel
PublisherAssociation for Computing Machinery
Pages289-298
Number of pages10
ISBN (Electronic)9781450358897
DOIs
StatePublished - Nov 6 2018
Event26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018 - Seattle, United States
Duration: Nov 6 2018Nov 9 2018

Other

Other26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
CountryUnited States
CitySeattle
Period11/6/1811/9/18

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Keywords

  • GeoSpatial Knowledge Graph
  • Range query
  • Spatial index

ASJC Scopus subject areas

  • Earth-Surface Processes
  • Computer Science Applications
  • Modeling and Simulation
  • Computer Graphics and Computer-Aided Design
  • Information Systems

Cite this

Sun, Y., & Elsayed, M. (2018). A generic database indexing framework for large-scale geographic knowledge graphs. In L. Xiong, R. Tamassia, K. F. Banaei, R. H. Guting, & E. Hoel (Eds.), 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018 (pp. 289-298). Association for Computing Machinery. https://doi.org/10.1145/3274895.3274966