<Insert Picture Here>

Query Processing in 3-D Spatial Databases:

Experiences with Oracle Spatial 11g

Siva Ravada, Baris M. Kazar, Ravi Kothuri

2

3D Spatial Systems

• Data Model to handle a variety of 3D Objects– Landmarks, buildings, bridges, point clouds

• Data quality control– Validation rules

• Geo-Referencing– Ability to accurately describe the location of 3D objects

• Comprehensive Location based search and Analysis– Nearest, Within-Distance

• Handling level of detail for seamless operation

• High Performance dissemination of 3D data– Web services, load and publish

• Support High performance real-time 3D rendering

• Support for 3D standards

3

3-D Spatial Types in Oracle 11g

4

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 can not 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

5

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

6

Specialized Types for High Density 3D Point Data

• Large volumes of point data acquired by sensors

– LIDAR

– Sensors used to collect data inside buildings

• Millions of points used to model a scene

• SDO_GEOMETRY is not suitable for such data

• POINT CLOUD data type introduced to efficiently

manage this type of point data

• TIN to create triangulation of such points

7

Efficient Management of Large 3D Point Data

Pts:

LOBs

BLK_EXTENT:

SDO_GEOMETRYBlkidObjid

Point Block TablePartition the

Input data

8

TABLE of Blocks

SDO_POINT_CLOUD in Oracle

Part it ion into mult iple blocks for granularity of access

Pts:

LOBs

BLK_EXTENT:

SDO_GEOMETRYBlkidObjid

Max_res,Min_res, Attrs,..

Table Column:SDO_POINT_CLOUD

9

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

10

Creating TINs for Very Large Data sets

Safe triangles

11

Table Column:SDO_TIN

Triangles:

LOBBlk_extent, Pts..Blkid

TABLE of SDO_TIN_BLK

Res

level

SDO_TIN in Oracle

SDO_PC_BLK

Objid

Features:

– Part it ion into mult iple blocks for granularity of access

– Can store upto 4Billion* 2Billion points

12

Geometry types mapped to City Objects

Composite Solid

Solid Solid

SurfaceSurface

Polygon

Building

House GuestHouse

WallsRoof

Window

13

Indexing and Querying of Geometry Data

• SDO_ANYINTERACT

• SDO_WITHIN_DISTANCE

• SDO_NEAREST_NEIGHBORS

Spatial

Index

Geometry

Engine

Candidate

set

Result

setQuery Spatial

Index

Geometry

Engine

Candidate

set

Result

setQuery

14

Indexing and Querying on Point Clouds and

TINs

Spatial

Candidate Set of blocks

Query Index on

Blk_extents

Geometry Engine Anyinteract

(query, Block/array of pts/triangles)

Result blocks

Pts:

LOBs

BLK_EXTENT:

SDO_GEOMETRYBlkidObjid

Max_res,Min_res, Attrs,..

Table Column:SDO_POINT_CLOUD

15

3-D Geometry Engine: AnyInteract Operation

Solid-Solid

Solid-Surface

Solid-Line

Solid-Point

Surface-Surface

Surface-Line

Surface-Point

Line-Line

Line-Point

Point-Point

If G1 and G2 are solids, we need all of the above routines to

find out if they interact with each other or not.

Set of Primitive operations

16

AnyInteract Operation for Solid-Solid

Solids G1, G2

MBVs

intersect

Eliminate Surfaces of G1 whose

MBVs don’t intersect with

MBVs of G2 (and vice-versa)

Compute R-trees for

Surfaces of G1,G2

Spatial Join of the

two R-trees

Candidate pairs of

intersecting Surfaces

Decompose into

Surfaces

yes

In memory R-tree

17

3-D Geometry Engine- Distance Operation

Geometries

G1, G2

Solid-Solid

Processing

Surface-Line

Processing

Surface-Surface

Processing

Line-Line

Processing

Decompose

Into surfaces

Compute Rtrees for

Surfaces of G1

Geometries

G1, G2

Solid-Solid

Processing

Geometries

G1, G2

Solid-Solid

Processing

Surface-Line

Processing

Surface-Surface

Processing

Line-Line

Processing

Line-Line

Processing

Decompose

Into surfaces

Compute Rtrees for

Surfaces of G1, G2

Geometries

Solid-Solid

Processing

Distance-based

Spatial Join of

two Rtrees

Distance-priority

List of pairs of

Neighbor elems

Top-lvl callyes

Return

Distance

Of First pair

result

18

Experimental Setup

• Comparing the anyinteract and distance processing cost for a TIN blockand a solid box query window.

• The number of points forms a 2-D grid in the TIN and is varied as follows:20-by-20, 50-by-50, 100-by-100, 150-by-150.

• We chose only these sizes of 400 to 22500 points as these numbersrepresent a typical block capacity of a TIN or a point cloud.

• The z value is randomly chosen using a random number within the queryrange.

• The query window itself is varied randomly across the extent of the TINblock and chosen to be either 1% or 0.1% of the underlying TIN blockextent.

19

Experimental Setup- Cont’d

• Using these data, we compared the following alternatives:

– Brute-force: This method compares each triangle in the TIN block to

the query window directly.

– ElemMBV check: This method performs an MBV check of each

element (i.e., each triangle) with the query and if successful, performs

a direct comparison.

– R-tree: This method constructs an R-tree on all the elements

(triangles) first, and then identifies relevant candidates using the R-

tree and performs lower-level comparison on the candidate results.

• Query has few sub-elements (e.g., solid box)

• Query has several (> 20) sub-elements

20

AnyInteract: Query window is a boxThe query response time for different database sizes (query times

on y-axis on logarithmic scale)

21

AnyInteract Experiment #2 Results

Query time (seconds)

R-tree 0.026

ElemMBV check 0.014

Table 1. Query times for a solid geometry (39 faces) vs.

a point query

Query time (seconds)

R-tree 0.036

ElemMBV check 0.093

Table 2. Query times for anyinteract of a Building of 39

faces with another Building of 24 faces

22

Distance Experiment Results:R-tree vs. brute force for distance computation

23

Discussion

• For anyinteract computation, use of in-memory R-trees on the geometries is faster if both geometries have a large number of (such as 20 or more) elements

• However, for anyinteract computation, if one geometry has very few sub-elements compared to the second, then R-trees are not the right choice – Instead, processing using ElemMBV check is faster.

• For distance computation, processing using R-trees on sub-elements of geometries is always faster by a factor of 2 or more based on the number of elements