Table of Contents
- List of Figures
- List of Tables
- Title and Copyright Information
- Preface
- Changes in This Release for Oracle Big Data Spatial and Graph
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1
Big Data Spatial and Graph Overview
- 1.1 About Big Data Spatial and Graph
- 1.2 Spatial Features
- 1.3 Property Graph Features
- 1.4 Multimedia Analytics Features
- 1.5 Installing Oracle Big Data Spatial and Graph on an Oracle Big Data Appliance
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1.6
Installing and Configuring the Big Data Spatial Image Processing Framework
- 1.6.1 Getting and Compiling the Cartographic Projections Library
- 1.6.2 Installing the Image Processing Framework for Oracle Big Data Appliance Distribution
- 1.6.3 Installing the Image Processing Framework for Other Distributions (Not Oracle Big Data Appliance)
- 1.6.4 Post-installation Verification of the Image Processing Framework
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1.7
Installing the Oracle Big Data SpatialViewer Web Application
- 1.7.1 Assumptions for SpatialViewer
- 1.7.2 Installing SpatialViewer on Oracle Big Data Appliance
- 1.7.3 Installing SpatialViewer for Other Systems (Not Big Data Appliance)
- 1.7.4 Configuring SpatialViewer on Oracle Big Data Appliance
- 1.7.5 Configuring SpatialViewer for Other Systems (Not Big Data Appliance)
- 1.8 Installing Property Graph Support on a CDH Cluster or Other Hardware
- 1.9 Installing and Configuring Multimedia Analytics Support
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2
Using Big Data Spatial and Graph with Spatial Data
- 2.1 About Big Data Spatial and Graph Support for Spatial Data
- 2.2 Oracle Big Data Vector and Raster Data Processing
- 2.3 Oracle Big Data Spatial Hadoop Image Processing Framework for Raster Data Processing
- 2.4 Loading an Image to Hadoop Using the Image Loader
- 2.5 Processing an Image Using the Oracle Spatial Hadoop Image Processor
- 2.6 Loading and Processing an Image Using the Oracle Spatial Hadoop Raster Processing API
- 2.7 Using the Oracle Spatial Hadoop Raster Simulator Framework to Test Raster Processing
- 2.8 Oracle Big Data Spatial Raster Processing for Spark
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2.9
Oracle Big Data Spatial Vector Analysis
- 2.9.1 Multiple Hadoop API Support
- 2.9.2 Spatial Indexing
- 2.9.3 Using MVSuggest
- 2.9.4 Spatial Filtering
- 2.9.5 Classifying Data Hierarchically
- 2.9.6 Generating Buffers
- 2.9.7 Spatial Binning
- 2.9.8 Spatial Clustering
- 2.9.9 Spatial Join
- 2.9.10 Spatial Partitioning
- 2.9.11 RecordInfoProvider
- 2.9.12 HierarchyInfo
- 2.9.13 Using JGeometry in MapReduce Jobs
- 2.9.14 Support for Different Data Sources
- 2.9.15 Job Registry
- 2.9.16 Tuning Performance Data of Job Running Times Using the Vector Analysis API
- 2.10 Oracle Big Data Spatial Vector Analysis for Spark
- 2.11 Oracle Big Data Spatial Vector Hive Analysis
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2.12
Using the Oracle Big Data SpatialViewer Web Application
- 2.12.1 Creating a Hadoop Spatial Index Using SpatialViewer
- 2.12.2 Exploring the Hadoop Indexed Spatial Data
- 2.12.3 Creating a Spark Spatial Index Using SpatialViewer
- 2.12.4 Exploring the Spark Indexed Spatial Data
- 2.12.5 Running a Categorization Job Using SpatialViewer
- 2.12.6 Viewing the Categorization Results
- 2.12.7 Saving Categorization Results to a File
- 2.12.8 Creating and Deleting Templates
- 2.12.9 Configuring Templates
- 2.12.10 Running a Clustering Job Using SpatialViewer
- 2.12.11 Viewing the Clustering Results
- 2.12.12 Saving Clustering Results to a File
- 2.12.13 Running a Binning Job Using SpatialViewer
- 2.12.14 Viewing the Binning Results
- 2.12.15 Saving Binning Results to a File
- 2.12.16 Running a Job to Create an Index Using the Command Line
- 2.12.17 Running a Job to Create a Categorization Result
- 2.12.18 Running a Job to Create a Clustering Result
- 2.12.19 Running a Job to Create a Binning Result
- 2.12.20 Running a Job to Perform Spatial Filtering
- 2.12.21 Running a Job to Get Location Suggestions
- 2.12.22 Running a Job to Perform a Spatial Join
- 2.12.23 Running a Job to Perform Partitioning
- 2.12.24 Using Multiple Inputs
- 2.12.25 Loading Images from the Local Server to the HDFS Hadoop Cluster
- 2.12.26 Visualizing Rasters in the Globe
- 2.12.27 Processing a Raster or Multiple Rasters with the Same MBR
- 2.12.28 Creating a Mosaic Directly from the Globe
- 2.12.29 Adding Operations for Raster Processing
- 2.12.30 Creating a Slope Image from the Globe
- 2.12.31 Changing the Image File Format from the Globe
- 3 Integrating Big Data Spatial and Graph with Oracle Database
- 4 Configuring Property Graph Support
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5
Using Property Graphs in a Big Data Environment
- 5.1 About Property Graphs
- 5.2 About Property Graph Data Formats
- 5.3 Getting Started with Property Graphs
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5.4
Using Java APIs for Property Graph Data
- 5.4.1 Overview of the Java APIs
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5.4.2
Parallel Loading of Graph Data
- 5.4.2.1 Parallel Data Loading Using Partitions
- 5.4.2.2 Parallel Data Loading Using Fine-Tuning
- 5.4.2.3 Parallel Data Loading Using Multiple Files
- 5.4.2.4 Parallel Retrieval of Graph Data
- 5.4.2.5 Using an Element Filter Callback for Subgraph Extraction
- 5.4.2.6 Using Optimization Flags on Reads over Property Graph Data
- 5.4.2.7 Adding and Removing Attributes of a Property Graph Subgraph
- 5.4.2.8 Getting Property Graph Metadata
- 5.4.3 Opening and Closing a Property Graph Instance
- 5.4.4 Creating Vertices
- 5.4.5 Creating Edges
- 5.4.6 Deleting Vertices and Edges
- 5.4.7 Reading a Graph from a Database into an Embedded In-Memory Analyst
- 5.4.8 Specifying Labels for Vertices
- 5.4.9 Building an In-Memory Graph
- 5.4.10 Dropping a Property Graph
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5.5
Managing Text Indexing for Property Graph Data
- 5.5.1 Configuring a Text Index for Property Graph Data
- 5.5.2 Using Automatic Indexes for Property Graph Data
- 5.5.3 Using Manual Indexes for Property Graph Data
- 5.5.4 Executing Search Queries Over Property Graph Text Indexes
- 5.5.5 Handling Data Types
- 5.5.6 Uploading a Collection's SolrCloud Configuration to Zookeeper
- 5.5.7 Updating Configuration Settings on Text Indexes for Property Graph Data
- 5.5.8 Using Parallel Query on Text Indexes for Property Graph Data
- 5.5.9 Using Native Query Objects on Text Indexes for Property Graph Data
- 5.5.10 Using Native Query Results on Text Indexes for Property Graph Data
- 5.6 Querying Property Graph Data Using PGQL
- 5.7 Using Apache Spark with Property Graph Data
- 5.8 Support for Secure Oracle NoSQL Database
- 5.9 Implementing Security on Graphs Stored in Apache HBase
- 5.10 Using the Groovy Shell with Property Graph Data
- 5.11 REST Support for Property Graph Data
- 5.12 Exploring the Sample Programs
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5.13
Oracle Flat File Format Definition
- 5.13.1 About the Property Graph Description Files
- 5.13.2 Vertex File
- 5.13.3 Edge File
- 5.13.4 Encoding Special Characters
- 5.13.5 Example Property Graph in Oracle Flat File Format
- 5.13.6 Converting an Oracle Database Table to an Oracle-Defined Property Graph Flat File
- 5.13.7 Converting CSV Files for Vertices and Edges to Oracle-Defined Property Graph Flat Files
- 5.14 Example Python User Interface
- 5.15 Example iPython Notebooks User Interface
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6
Using the In-Memory Analyst (PGX)
- 6.1 Reading a Graph into Memory
- 6.2 Configuring the In-Memory Analyst
- 6.3 Reading Custom Graph Data
- 6.4 Storing Graph Data on Disk
- 6.5 Executing Built-in Algorithms
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6.6
Creating Subgraphs
- 6.6.1 About Filter Expressions
- 6.6.2 Using a Simple Edge Filter to Create a Subgraph
- 6.6.3 Using a Simple Vertex Filter to Create a Subgraph
- 6.6.4 Using a Complex Filter to Create a Subgraph
- 6.6.5 Combining Expression Filters
- 6.6.6 Using an Expression Filter to Create a Set of Vertices or Edges
- 6.6.7 Using a Vertex Set to Create a Bipartite Subgraph
- 6.7 Using Pattern-Matching Queries with Graphs
- 6.8 Starting the In-Memory Analyst Server
- 6.9 Deploying to Jetty
- 6.10 Deploying to Apache Tomcat
- 6.11 Deploying to Oracle WebLogic Server
- 6.12 Connecting to the In-Memory Analyst Server
- 6.13 Using the In-Memory Analyst in Distributed Mode
- 6.14 Reading and Storing Data in HDFS
- 6.15 Running the In-Memory Analyst as a YARN Application
- 6.16 Using Oracle Two-Tables Relational Format
- 6.17 Using the In-Memory Analyst to Analyze Graph Data in Apache Spark
- 6.18 Using the In-Memory Analyst Zeppelin Interpreter
- 6.19 Using the In-Memory Analyst Enterprise Scheduler
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7
Using Multimedia Analytics
- 7.1 About Multimedia Analytics
- 7.2 Processing Video and Image Data Stored in HDFS Using the Multimedia Analytics Framework
- 7.3 Processing Streaming Video Using the Multimedia Analytics Framework
- 7.4 Face Recognition Using the Multimedia Analytics Framework
- 7.5 Configuration Properties for Multimedia Analytics
- 7.6 Using the Multimedia Analytics Framework with Third-Party Software
- 7.7 Displaying Images in Output
-
A
Third-Party Licenses for Bundled Software
- A.1 Apache Licensed Code
- A.2 ANTLR 3
- A.3 AOP Alliance
- A.4 Apache Commons CLI
- A.5 Apache Commons Codec
- A.6 Apache Commons Collections
- A.7 Apache Commons Configuration
- A.8 Apache Commons IO
- A.9 Apache Commons Lang
- A.10 Apache Commons Logging
- A.11 Apache Commons VFS
- A.12 Apache fluent
- A.13 Apache Groovy
- A.14 Apache htrace
- A.15 Apache HTTP Client
- A.16 Apache HTTPComponents Core
- A.17 Apache Jena
- A.18 Apache Log4j
- A.19 Apache Lucene
- A.20 Apache Tomcat
- A.21 Apache Xerces2
- A.22 Apache xml-commons
- A.23 Argparse4j
- A.24 check-types
- A.25 Cloudera CDH
- A.26 cookie
- A.27 Fastutil
- A.28 functionaljava
- A.29 GeoNames Data
- A.30 Geospatial Data Abstraction Library (GDAL)
- A.31 Google Guava
- A.32 Google Guice
- A.33 Google protobuf
- A.34 int64-native
- A.35 Jackson
- A.36 Jansi
- A.37 JCodec
- A.38 Jettison
- A.39 JLine
- A.40 Javassist
- A.41 json-bignum
- A.42 Jung
- A.43 Log4js
- A.44 MessagePack
- A.45 Netty
- A.46 Node.js
- A.47 node-zookeeper-client
- A.48 OpenCV
- A.49 rxjava-core
- A.50 Slf4j
- A.51 Spoofax
- A.52 Tinkerpop Blueprints
- A.53 Tinkerpop Gremlin
- A.54 Tinkerpop Pipes
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B
Hive and Spark Spatial SQL Functions
- B.1 ST_AnyInteract
- B.2 ST_Area
- B.3 ST_AsWKB
- B.4 ST_AsWKT
- B.5 ST_Buffer
- B.6 ST_Contains
- B.7 ST_ConvexHull
- B.8 ST_Distance
- B.9 ST_Envelope
- B.10 ST_Geometry
- B.11 ST_Inside
- B.12 ST_Length
- B.13 ST_LineString
- B.14 ST_MultiLineString
- B.15 ST_MultiPoint
- B.16 ST_MultiPolygon
- B.17 ST_Point
- B.18 ST_Polygon
- B.19 ST_Simplify
- B.20 ST_SimplifyVW
- B.21 ST_Volume