oracle spatial and mapviewer problems from real world applications
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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. - PowerPoint PPT PresentationTRANSCRIPT
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Oracle Spatial and MapviewerProblems From Real World Applications
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Spatial Data Types
All Location/Spatial Data Stored in the Database
Spatial Indexing
Fast Access toSpatial Data
Spatial Access Through SQL
Spatial Analysis
Oracle Spatial Capabilities
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Manage ALL Geospatial Data Types
Data
Locations(points)
Networks(Connectivity) Parcels
(polygons)
Imagery(Raster)Structured Networks/Boundaries
(persistent topology)
3D data(models, LIDAR)
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Some Interesting Problems
From The Commercial World
Network Partitioning
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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
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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
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Spatial Network Partitioning
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Logical Network PartitioningGO2Keyword.rdf
UniProt.rdfGO.rdf
Keywords.rdf
Taxonomy.rdfPubMed.xm
l
Citation
IntAct.rdf
Organism
Enzymes.rdf
OMIM.rdf
GO2OMIM.rdf
GO2Enzyme.rdf
MIM Id
KEGG.rdf
KeywordGO2UniProt.rdf
Protein
Enzyme
ProbeSet.rdf
Gene
Probe
Pathway
Compound
Very Large networks (few hundred million nodes/links) Updates to the data are common
Automated Generation of 3D data
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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
(2,0,2)
(4,2,2)
(4,0,4)
Y
Z
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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
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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
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2d foot-print plus height values
+ (h1, …, hn) =
3D Extrusion • Extruding 2D foot-prints to valid 3D objects
Any arbitrary shape with holes
Can we generate such complex objects with extrusion ?
Generalization in 3D
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City GML Example
• Start with building models generating using CAD data• Generate generalized views of the data for large
volumes of data (city models)
Map Generalization
Map Simplification with Multiple Layers
• Mapshaper.org
Managing Very Large TINs
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TIN: Triangulated Irregular Network
Node No X Y Z
1 5 6 3
2 3 6 5
3 1 5 6
4 4 4 4
5 6 5 3
6 2 2 2
. . . .
• 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
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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
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Grid based TIN Generation