1 / 22

Oracle Spatial and Mapviewer Problems From Real World Applications

Oracle Spatial and Mapviewer Problems From Real World Applications. Oracle Spatial Capabilities. Spatial Analysis. Spatial Indexing. Spatial Data Types. Fast Access to Spatial Data. All Location/Spatial Data Stored in the Database. Spatial Access Through SQL.

dwight
Télécharger la présentation

Oracle Spatial and Mapviewer Problems From Real World Applications

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Oracle Spatialand MapviewerProblems From Real World Applications

  2. Oracle Spatial Capabilities Spatial Analysis • Spatial Indexing Spatial Data Types Fast Access to Spatial Data All Location/Spatial Data Stored in the Database Spatial Access Through SQL 2

  3. Manage ALL Geospatial Data Types Networks (Connectivity) Parcels (polygons) Locations (points) Data Imagery (Raster) 3D data (models, LIDAR) Structured Networks/Boundaries (persistent topology) 3

  4. <Insert Picture Here> Some Interesting Problems From The Commercial World 4

  5. Network Partitioning

  6. Network Data Model • Data Model • Store network (graph) structure in the database • Maintains connectivity of the network • Attributes at link and node level • Network Analysis Functions • Traditional network algorithms are based on main memory • Need new approaches to deal with large networks that are too big to fit into main memory 6

  7. Load On Demand Analysis • Supports load-on-demand approach for very large networks • Networks are logically partitioned • Each sub-network is small (thousands of nodes/edges) • Sub-networks are incrementally loaded into memory as needed for analysis • Partitioning utilities are available for partitioning large spatial networks 7

  8. Spatial Network Partitioning 8

  9. GO2Keyword.rdf Keywords.rdf ProbeSet.rdf Keyword GO2OMIM.rdf GO2UniProt.rdf Probe Protein Gene MIM Id OMIM.rdf IntAct.rdf GO.rdf GO2Enzyme.rdf UniProt.rdf Enzyme Organism Citation Compound Taxonomy.rdf Enzymes.rdf PubMed.xml KEGG.rdf Pathway Logical Network Partitioning Very Large networks (few hundred million nodes/links) Updates to the data are common 9

  10. Automated Generation of 3D data

  11. Y (4,2,2) (2,0,2) (4,0,4) Z SDO_GEOMETRY for 3D Data • Points • Lines • Simple Surfaces • All points of a surface lie in a 3D plane • A 3 point 3D polygon is the simplest surface • A simple surface can have any polygonal shape • Composite surfaces • has one or more connected simple surfaces • It can be closed or open • The simple surfaces in a composite surface cannot cross each other • surface of a cube is an example of a composite surface • Cube has six simple surfaces • Each simple surface is a 3D square 11

  12. SDO_GEOMETRY for 3D Data • Simple Solids • Solids are composed of closed surfaces • It has to have one outer surface and one or more interior surfaces • Cube is an example of a simple solid • A pyramid is another example of a simple solid • Composite Solids • Consists of n simple solids as a connected solid • Can be represented as a simple solid with a composite surface • Topologically there is an equivalent simple solid, but the composite solid representation is easier • Example: A building composed of rooms • Simple, composite solids: Always define a single contiguous volume 12

  13. 3D Data Extraction • Extract faces of buildings • Generation of valid 3D objects from primitive elements • Generating a valid multi-surface from a set of planar polygons • Generating a valid solid/multi-solid from a set of planar polygons 13

  14. + (h1, …, hn) = 2d foot-print plus height values Can we generate such complex objects with extrusion ? Any arbitrary shape with holes 3D Extrusion • Extruding 2D foot-prints to valid 3D objects 14

  15. Generalization in 3D

  16. City GML Example • Start with building models generating using CAD data • Generate generalized views of the data for large volumes of data (city models) 16

  17. Map Generalization

  18. Map Simplification with Multiple Layers • Mapshaper.org

  19. Managing Very Large TINs

  20. TIN: Triangulated Irregular Network • What is a TIN? • Vector-based topological data model used to represent terrain/surface • Contain a network of irregularly spaced triangles • 3D surface representation derived from irregularly spaced points • Each sample point has an x, y coordinate and a z value or surface value 20

  21. Disk based TIN Generation • Many main memory algorithms for creating TINs • These algorithms do not scale for very large number of points • Constrains add additional complexity • Break lines, stop lines • Void polygons 21

  22. Grid based TIN Generation 22

More Related