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 Presentation

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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

3

Manage ALL Geospatial Data Types

Data

Locations(points)

Networks(Connectivity) Parcels

(polygons)

Imagery(Raster)Structured Networks/Boundaries

(persistent topology)

3D data(models, LIDAR)

4

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Some Interesting Problems

From The Commercial World

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

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

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Spatial Network Partitioning

9

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

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