Oracle Spatial and Graph
Spatial Features, Network Data Model Graph
& RDF Semantic Graph


Oracle Spatial and Graph option includes advanced features for spatial data and analysis and for physical, network and social graph applications.




Spatial Features


The geospatial data features support complex geographic information systems (GIS) applications, enterprise applications, and location-based services applications, augmenting the Oracle Database Locator feature, which provides storage, analysis, and indexing of 2D location data accessible through SQL and standard programming languages.

These advanced spatial features include:

  • Spatial manipulation and analysis such as buffer generation, linear referencing;
  • Complete geocoding engine and routing engines;
  • Storage, indexing, and analysis of image and gridded raster data;
  • A persistent topology data model and schema for land management applications;
  • Location-based classification, binning, and correlation for geomarketing analysis;
  • 3D and point cloud and LiDAR data management and analysis.
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Graph Features


The graph database features include two graph models:

  • A Network Data Model Graph (NDM) to model and analyze link-node graphs to represent physical and logical networks used in transportation, utilities and telco
  • A RDF Semantic Graph to model and analyze data represented as triples for social network, linked data and other semantic applications.

The Network Data Model Graph features include:

  • A storage model to represent graphs and networks in link and node tables.
  • Java APIs to perform analysis in memory.
  • Numerous graph analysis functions including shortest path, within cost, nearest neighbors, traveling salesman, spanning tree, and more.
  • Explicit storage and connectivity of the graph with link- and node-level attributes.
  • Support for directed and undirected graphs with or without cost.
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The RDF Semantic Graph features include:

  • graph relationships represented as triples in compressed, partitioned tables
  • indexing, querying, and ontology management
  • RDFS, OWL and user-defined inferencing (parallel, batch and incremental)
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