1 copyright © 2012, oracle and/or its affiliates. all …...• on exadata half rac: • 34.75...


Real Life Applications of Location Analytics Dan Geringer, Senior Software Development Manager, Oracle Jayant Sharma, Director Prod Mgmt, Oracle


The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.


Program Agenda

  Introducing Oracle Spatial and Graph

  Some Use Cases

  Discussion


INTRODUCING


Why rename this Oracle Database option

 Highlights existing graph capabilities in Oracle Spatial –  W3C RDF graph since Oracle 10gR2 –  Network Data Model graph since Oracle 10gR1

 Addresses increasing market demand for graph database capabilities –  Social Network Graph database popularity –  Multimodal and integrated transportation, utility and communications

networks

From “Oracle Spatial” to “Oracle Spatial and Graph”


Oracle Spatial and Graph

“Points” “Lines” “Polygons”

Rasters

Topologies

3D f1 f2 n1 n2

e1 e2 e3

e4

Network Graphs

Web Services (OGC)

Geocoding Routing

RDF Semantic Graphs

Oracle Spatial and Graph option


  Spatial Data Types and Models

–  2D and 3D Geometries

–  Raster Imagery and Gridded Data

–  Land Management Persistent Topology

–  Geographic and Whole Earth (Geodetic)

–  Network Data Model Graph

  Spatial Analysis

–  Spatial Search (Containment & Proximity)

–  Geocoding (Address conversion)

–  Routing (Turn by Turn directions)

–  Indexing of 2D & 3D

–  Full Coordinate System Support

Spatial Features



  Network Data Model graph

  W3C RDF Semantic graph

Graph Features



Agenda

  Introduction

  Some Use Cases

  Discussion


Display application content and context


Reports in operational applications


Use in operational workflows Network Pinning and Work Tagging


Spatial visualization in interactive reports


Map views in BI dashboards


  Show incidents by type

–  within or near specified location

Public safety

Usage Scenarios in (public sector) CRM


Usage Scenarios in (public sector) CRM Show KPIs by service district


Geomarketing examples


TMC: Oracle SiteHub & Retail Analytics


Sales by store


Members who shopped at selected store


Where do the frequent customers live


Reports/analysis using drive-time polygons


Or by custom (user-drawn) areas


Solution architecture

GeoDatawarehouse Schema Table

J2EE Container

JDBC

Oracle DBMS 11.2

Oracle WLS

Client (browser)

IsoChrone Area Editing

IsoChrone OBI-Report

Get the analysis area

BI Publisher

NDM WithinCost Analysis

MapViewer Polygon Editing

Jsp

SDO_Geom Package (Intersection, Union, Difference ..)

Bridge GeoBI Package


OVERVIEW

  Comprehensive cloud based GIS analytics platform

  Provide rich data and cutting edge analytics to help clients acquire, retain and grow high value customers

CHALLENGES / OPPORTUNITIES •  Ensure enterprise level security needs •  Provide mechanism for efficient and optimal data transfer •  Fast query response times for large customer and

transaction level datasets •  Frequent, transparent software and data updates •  Quick and easy set up

SOLUTION •  Oracle Database 11g Release 2 Enterprise Edition

•  Spatial Option with GeoRaster, Network Data Model •  Partitioning •  Advanced Security

•  Oracle Fusion Middleware 11g •  MapViewer

RESULTS •  Up to10x performance improvement for detailed

mapping with the use of MapViewer map cache •  Use of NDM partitioned blobs resulted in 300% quicker

isochrone creation times while increasing accuracy •  Consolidation of raster, vector, network data in 500 GB

sized central repository •  Utilize topology to maintain accurate geography

relationships •  Improved base data precision with 25M+ ZIP4 level data

TARGUSinfo ElementOne Analytics


  SDO_LRS to build comprehensive road network

  SDO_NET partitioning and load-on-demand for scalability and performance

  NDM APIs for custom costs and path analysis

Features used

Use of Spatial LRS & Network Data Model


  SDO_CS for transformations   buffer, union, difference,

convexhull etc for polygon generation

  sdo_validate, simplify, rectify_geom for cleansing

  sdo_tin for heatmaps

Advanced functions

Use of Oracle Spatial 11gR2 Features


  Same underlying data for geocoder and road network

  Customers geocode as link id and percentage (instead of longitude/latitude)

  5 mile Network Buffer generates all possible paths

  Each persisted path includes: –  Covered link IDs, nodes ID, and associated

costs

  Single database query to find closest store, and drive time, for each customer (join on link_id)

For millions of customers, find closest store within a specified drive time

Large Scale Drive Time/Distance Analysis

Store Location

Customer Location


Benefits with Parallel Query, Exadata Database Machine


Parallel Query and Spatial Rail Application


Parallel Query And Spatial Operators

 Requirement –  GPS locations for each train collected throughout the day –  Each location has other attributes (time, speed, and more) –  GPS locations have a degree of error, so they don’t always fall on a track. –  Bulk nearest neighbor queries to find closest track, and project reported

train positions onto tracks

 This information is used for: –  Tracking trains –  Analysis for maintenance, ensure engineers are within parameters,etc…

Rail Application



 45,158,800 GPS train positions.  For each train position:

  Find the closest track to the train (with SDO_NN)   Then calculate the position on the track closest to

the train

What we tested


Parallel Query And Spatial Operators Rail Application

ALTER SESSION FORCE PARALLEL ddl PARALLEL 72; ALTER SESSION FORCE PARALLEL query PARALLEL 72;

CREATE TABLE results NOLOGGING SELECT /*+ ordered index (b tracks_spatial_idx) */ a.locomotive_id, sdo_lrs.find_measure (b.track_geom, a.locomotive_pos) FROM locomotives a, tracks b WHERE sdo_nn (b.track_geom, a.locomotive_pos, 'sdo_num_res=1') ='TRUE';



• On Exadata Half RAC: •  34.75 hours serially vs. 44.1 minutes in parallel •  48 database cores - 47x faster

•  On Exadata X2-2 Full Rack •  96 database cores – about 94x faster

•  X2-8 (128 cores) even faster

Exadata Results


Parallel Query and Spatial Validation Of Home Appraisals


Validation Of Home Appraisals

 Validate home appraisals for a Government Sponsored Enterprise (GSE)

 Requirement - Find all the parcels touching parcels to validate appraisals

 Processed 2,018,429 parcels –  Exadata ½ RAC:

  Serially – 38.25 minutes   Parallel - 48 cores (45x faster) - 50 seconds

–  Exadata X2-2 Full RAC (96 cores) about 90x faster –  Exadata X2-8 (128 cores) even faster

Exadata Results


Parallel Query and Spatial Parallel Enabled Pipeline Table Functions


Parallel Enabled Pipeline Table Function

 Parallelize a function that’s called a massive amount of times. –  Batch geocoding (sdo_gcdr.geocode_address) –  Batch reverse geocoding (sdo_gcdr.reverse_geocode) –  Batch Digital Elevation Model (DEM) raster lookups

(sdo_geor.get_cell_value)  Pipeline Table Function returns a table of results

–  Table of geocodes –  Table of reverse geocodes –  Table of elevation values



 Define cursor that selects the input to the batch process: –  SELECT customer_addresses

FROM address_table WHERE state = ‘NY’;

–  SELECT vehicle_location FROM vehicles WHERE sdo_anyinteract (location,:region) = ‘TRUE’

 Parallel Query distributes the batch process over a specified number of database cores.

How does it work?



ALTER SESSION FORCE PARALLEL DML PARALLEL 24; ALTER SESSION FORCE PARALLEL QUERY PARALLEL 24; -- To balance parallel slaves across nodes. ALTER SESSION SET "_parallel_load_balancing"=false;

CREATE TABLE results (id NUMBER, geom SDO_GEOMETRY);

INSERT /*+ append */ INTO results SELECT id, sdo_geometry (2001,8307,sdo_point_type(longitude,latitude,null),null,null) geom FROM table( geocode_pipelined ( CURSOR(SELECT id, streetname, city, state, zip, housenum FROM ascii_addrs) )); COMMIT;

Exadata ¼ RAC has 2 nodes, each with 12 cores (24 cores total)



ALTER SESSION FORCE PARALLEL DML PARALLEL 48; ALTER SESSION FORCE PARALLEL QUERY PARALLEL 48;

CREATE TABLE results (id NUMBER, geom SDO_GEOMETRY); INSERT /*+ append */ INTO results SELECT id, sdo_geometry (2001,8307,sdo_point_type(longitude,latitude,null),null,null)

geom FROM table( geocode_pipelined ( CURSOR(SELECT id, streetname, city, state, zip, housenum FROM ascii_addrs) )); COMMIT;

To Maximize Exadata ¼ RAC (24 cores)


Parallel Pipelined Table Function

 Exadata ¼ RAC Has 24 Cores

 Batch geocoding – 1365/second  Batch reverse geocoding – 3388/second  Batch DEM get_cell_value raster lookups – 8951/second

Exadata Results


Q&A

1 copyright © 2012, oracle and/or its affiliates. all …...• on exadata half rac: • 34.75...

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