Download Oracle Big Data Spatial and Graph

Introduction to Oracle Semantic
Technologies
V1.0
Shintaro Nagaoka
Presales
Oracle Netherlands
January, 2016
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
Oracle Confidential –
Oracle Spatial and Graph and Big Data Spatial and Graph:
Graph Overview
Speaker:
Bill Beauregard
Senior Principal Product Manager, Oracle
3
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
Graph Data Model
B
C
• What is a graph?
– A set of links and nodes (and optionally attributes)
– A graph is simply linked data
A
• Why do we care?
D
F
E
– Rise in Commercial use of Big Data
• Web log files, Twitter feeds, sensor readings, Internet of Things
• Cyber networks, power grids, protein interaction graphs
• Knowledge graphs (IBM Watson, Apple SIRI, Google Knowledge Graph)
– Graphs are intuitive and flexible
• Easy to navigate, easy to form a path, natural to visualize
• Do not require a predefined schema
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
4
Oracle’s Graph Database Strategy
Support Graph Data Types…
…On all enterprise platforms
• Oracle Database
• Add graph analytics to applications,
tools, and information technology
platforms
• Deliver a scalable, secure, and high
performing product
• Simplify development with integrated
graph analysis, APIs and services
• Cloudera with Apache Hadoop
• Oracle NoSQL Database
• Oracle Big Data Appliance
• Oracle Exadata Database Machine
• Oracle Cloud
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
5
3 Graph Models / 3 Domain Use Cases
Use Case
Spatial Network
Analysis
Graph Model
Industry Domain
Network Data Model
• Network path analysis
• Multi-model modeling
RDF Data Model
Linked Data /
Semantic
Mediation
• Data federation
• Knowledge representation
• Master Metadata Mgmt
Property Graph Model
Social Network
Analysis
• Graph Search & Analysis
• Big Data analytics
• Entity analytics




Logistics
Transportation
Utilities
Telcoms




Life Sciences
Finance
Publishing
Public Sector




Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
National Intelligence
Public Safety
Social Media search
Marketing - Sentiment
6
Oracle Spatial and Graph
RDF Semantic Graph Overview
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
7
“Semantic technologies include software standards and
methodologies that are aimed at providing more explicit meaning
from the information that’s at our disposal.”
• The CIO’s Guide to Semantics
• Dave McComb, Semantic Arts, Inc.
• Standards defined by W3C & OGC
–
–
RDF, RDF/S, OWL, SKOS
SPARQL, RDFa, RDB2RDF, R2RML GeoSPARQL
• RDF embeds semantics in the data
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
8
Fundamental Concepts and “building blocks”
1) Anything can be described by its unique relationship to something else
– John Smith Is At OpenWorld
Subject
Relationship
Item
– OpenWorld Is In San Francisco
John Smith
Is At
Openworld
– Seema Is Presenter of OOW Semantic
Session
Openworld
Is In
San Francisco
Oracle
Has A Conference
Called
Openworld
Seema Rao
Works At
Oracle
Seema Rao
Is Presenter of
OOW Semantic
Session
John Smith
Is Registered for
OOW Semantic
Session
OOW
Semantic
Session
Is Held
10/6/11, 12:00
Noon
– This is called a “triple”
– Uniqueness in the triple is enforced
by the inclusion of a URI
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
Fundamental Concepts and “building blocks”
2) Implied relationships can be found in the data using rules
This is called “inferencing”
RULE:
1. OOW is the same as Openworld
“John and Seema were in San
Francisco on 10/6/11”
Derived ( inferred ) information
Subject
Relationship
Item
Openworld
Is In
San Francisco
Openworld
Has A Session Called
OOW Semantic
Session
Seema Rao
Is Presenter of
OOW Semantic
Session
John Smith
Is Registered for
OOW Semantic
Session
OOW
Semantic
Session
Is Held
10/6/11, 12:00
Noon
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
Fundamental Concepts and “building blocks”
3) Standard sets of related concepts can be stored to describe relationships and
referenced to enhance query and discovery
This is called an “ontology”
Type of Relationship
What you evaluate
What you compare
Opposite/Inverse
Relationship
Lends to
Businesses and related
parties
Businesses
Borrows from
Owns
Institutions and related
parties
Institutions
Is owned by
Now known as
Corporate names and
symbols
Corporate names
Previously known as
Operates in
Geographic hierarchy
Geographic name
No presence in
-- Holding companies own banks  banks lend to other institutions  …
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
Fundamental Concepts and “building blocks”
4) Conceptually, Semantic applications look at things as being represented as
graphs, rather than tables
Type of Relationship
What you evaluate
What you compare
Opposite/Inverse
Relationship
Lends to
Businesses and related
parties
Businesses
Borrows from
Owns
Institutions and related
parties
Institutions
Is owned by
Now known as
Corporate names and
symbols
Corporate names
Previously known as
Geographic hierarchy
Geographic name
Operates in
Wells Fargo
Is owned by
No presence in
Now known as
Wachovia
Norwest
In Oracle Database, we use Triples
and Key relationships to represent nodes
and links in the Graph.
Now known as
Now known as
Core
States
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
First
Union
Now known as
Crocker National
Now known as
First Nat Bank of
Philadelphia
Fundamental Concepts and “building blocks”
5) Querying is based on graphs
Prime_M
Ex: Find sub-prime mortgage
exposure for “Wells Fargo” bank… AutoLoan
RMBS
Sub-prime_M
Sub-prime M
Is type of
MortgageLoan
CDOs
SecuredLoan
Is type of
SELECT SUM
(?subprime_amount) AS
exposure
WHERE
{?loan_instance rdf:type
:mortgage_loan
?lending_institution rdfs
:subclassOf
:wells_fargo
?loan_instance :subprime
_loan ?subprime_amount
2/28 ARM
Lender and Lending
Institution are same
LoanProducts
Is seller of
Wells Fargo
Lender
Lending_Institution
Is name of
Is owned by
Now known as
Wachovia
Norwest
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
Is name of
Wells Fargo Is name of
JPMC
BofA
Recap: Key ideas
• Based on fundamentally different Open World Assumption
– What is unknown is undefined (not false) - that supports discovery
• Schema are flexible, evolving, can’t be known in advance
– Rich, real world relationships are modeled in the data
• Every data element is uniquely identified - supports integration
– Data & relationships are machine-readable
• Pattern query language supports discovery workflows
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
Two Application Use Cases For RDF Semantic Graph
Linked Data
• Unified metadata model for
distributed data sources
Entity Analytics
 SPARQL pattern matching
• Flexible model for sparse and
evolving data
 Detecting related entities across
large, sparse, disparate collections
of data
• Validate semantic and structural
consistency
 Inferencing: Applying rules on
asserted data
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
Semantic Technologies Partners
Integrated Tools and Solution Providers:
Ontology Engineering
Reasoners
Open Source Frameworks
Joseki
NLP Entity Extractors
Standards
Sesame
Applications
SI / Consulting
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
Linked Data: Industry Adoption
Industries
• Life Sciences
• Finance
• Media
Hutchinson
3G Austria
• Networks & Communications
• Defense & Intelligence
• Police
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17
Novartis Institutes for BioMedical Research (NIBR)
Business Challenge
• Link database information on genes, proteins,
metabolic pathways, compounds, ligands, etc.
to original sources.
• Increase productivity for accessing, sharing,
searching, navigating, cross-linking, analyzing
internal /external data
Solution
• Semantic integration layer on RDF graph
• Rich domain-specific terminology (biology,
chemistry and medicine) 1.6 M terms
• Terminology Hub: 8 GB of referential data that
cross-references between data repositories.
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
18
EU Publications Office
Linked Metadata Platform for European Union
Objectives
 Common metadata model supports:
 Search and discovery of EU Publications
 Multiple domains and languages
Solution
 Validate and tag EU law, tenders, and
publicity to standardized vocabularies
 Unified RDF graph metadata model
 Supports discovery of content through
user’s terminology and language
 Provides variety of dissemination modes
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
19
Object-based Intelligence/Entity Analytics
Extracted Entities &
Relationships
Information
Extraction
Country: UK
Nationality: Somalian
Feature Extraction,
Term Extraction
Has
Currently resides
Group: Al Shabab
Country: Morocco
Person: Abduwali
Abdukhadir Muse
Member of
Search, Presentation, Report,
Visualization, Query
Link ?
Supports
Currently resides
Person: Chehab
Abdouljamid Bouyaly
Link ?
Ideology: Islamist
Member of
Supports
Group: ?
Person: ?
Member of
Has
Group: al Qaeda
Currently resides
Country: Pakistan
Nationality: Pakistani
RDF
Intelligence Ontologies
SQL/SPARQL
Enterprise Data
Spatial
images Documents
Data Sources
Contents Repository
Databases
Web resources
Blogs, Mails, news, RSS feeds
National Intelligence Scenario
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
Oracle Database 12c Spatial and Graph Tooling
Transaction
Systems
Unstructured
Content
RSS, email
Other Data
Formats
Transform &
Load, Query
Applications &
Modeling Tools
& Inference
Analysis Tools
Relational2RDF
Support for Protégé
Support for Apache
Jena
Natural Language
Processing Extraction
(partners)
• RDF/OWL Data
Management
• SQL & SPARQL Query
• OWL Inferencing
• Semantic Rules
• Scalability & Security
• Semantic Indexing
Data Sources
• Java, HTTP access
• JSON, XML output
• Graph visualization
(Cytoscape)
• Oracle Advanced
Analytics (R, Mining)
• Oracle Business
Intelligence (OBIEE)
• Map (GIS) Visualization
Oracle Database 12c
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
Oracle Database 12c RDF Semantic Graph Database
• Compression & partitioning
• Parallel load, inference, query
• High availability
• Label security: triple-level
• W3C standards compliance
• Semantic Indexing of text
• Enterprise Manager
• Support for Open Source
• Development framework, ontology
editing, visualization
• Exadata ready
Load /
Storage
Query
Reasoning
Analytics
• Native RDF graph data store
• Manages billions of triples
• Optimized storage architecture
• RDF Views on Relational Data
• SPARQL-Jena/Joseki
• SQL/graph query, B-tree indexing
• Ontology assisted SQL query
• RDFS, OWL2 RL, EL, SKOS
• User-defined rules
• Incremental, parallel reasoning
• User-defined inferencing
• Plug-in architecture
• Semantic indexing framework
• Integration with
• OBIEE, Oracle R Enterprise
• Oracle Data Mining
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
Manageability of RDF Semantic Graph
Built in support from Oracle Database utilities and tools
Ingest / Replicate /
Recover
Tune / Analyze
Bulk load:
Tune load/ query/ inference:
• Apache Jena bulk loader
• Oracle external tables &
• SQL*Loader (Direct Path)
w/ PL/SQL Bulk Load API
• Parallelism
• Btree indexing triple/quad
• Typed literals indexing
• SPARQL query hints
• Statistics gathering
• Dynamic Sampling
Replicate & recover:
• Data Guard: physical standby
• Data Pump: staging tables
• Recovery Manager: RMAN
Analyze performance:
• Enterprise Manager: view
optimizer plans, monitor
execution / resource usage
Manage
Control query execution:
• in database & Jena client
Create & monitor graph
w/ SQL Developer:
• Semantic Network
• Models, virtual models
• Btree indexes
• Rule bases
• Entailments
• Security data labels
• Semantic index policies
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
23
World’s Fastest Big Data Graph Benchmark
1 Trillion Triple RDF Benchmark with Oracle Spatial and Graph
• World’s fastest data loading
performance
Oracle Database 12c can load, query and
inference millions of RDF graph edges
per second
• World’s fastest query performance
• Worlds fastest inference performance
• Massive scalability: 1.08 trillion edges
Millions of triples per second
2.00
1.42
1.50
• Platform: Oracle Exadata X4-2 Database Machine
1.00
• Source: w3.org/wiki/LargeTripleStores,
9/26/2014
0.50
1.52
1.13
0.00
Query
Load Inference
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
24
Oracle RDBMS with RDF Graph
Similar to Spatial. Combining the strength of relational and object-relational approach
• Allows meta-data storage
• Repository ( created post installation )
• Operators and functions
• Storage of common RDF objects in the relational tables
• SQL and SPARQL support
• Leveraging all the traditional Oracle RDBMS Strength
– Security, availability, scalability, manageability
• Exadata Ready
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
25
Many Known Graphs and Vocabularies on the Web
• DBPedia
• Wordnet
• Semanitc XBRL
• SIOC
• Drug Bank
• US Census
• NCI
• ACM
• YAGO
• SNOMED
• Daily Med
• Cyc/Open Cyc
• FOAF
• Linked CT
• PubMed
• Geonames
• Eurostat
• Freebase
• CIA World Fact Book
• KEGG
• Gene Ontology
• DBLP
• Data.gov.uk
• UniRef
• UniProt
• Music Brainz Data
• Smart Link
• UniParc
• Semantic Tweet
• Reactome
• CiteSeer
• CO2 Emission
• Diseasome
And so much more !
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
Oracle Big Data Spatial and Graph
Property Graph Overview
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30
Oracle Property Graph Data Model
• A set of vertices (or nodes)
–
–
–
each vertex has a unique identifier.
each vertex has a set of in/out edges.
each vertex has a collection of key-value properties.
• A set of edges
–
–
–
–
each edge has a unique identifier.
each edge has a head/tail vertex.
each edge has a label denoting type of relationship between two
vertices.
each edge has a collection of key-value properties.
• Blueprints Java APIs
• Implementations
•
Oracle, Neo4j, DataStax(Titan), Spark GraphX, Dato GraphLab Create,
InfiniteGraph, Dex, Sail, MongoDB …
• A property graph can be modeled as an RDF Graph
https://github.com/tinkerpop/blueprints/wiki/Property-Graph-Model
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
31
RDF Graph v. Property Graph
RDF Semantic Graphs
• Use Case:
– Linked data, semantic metadata layer
• Analytics:
– pattern matching, Inferencing
Property Graph
• Use Case:
–Social network analysis
• Analytics:
–Clustering, centrality, page rank,
path finding
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
32
Common (Property) Graph Analysis Use Cases
Recommend the most
similar item purchased
by similar people
Product
Recommendation
Find out people that are
central in the given
network – e.g.
influencer marketing
Influencer Identification
Identify group of people
that are close to each
other – e.g. target
group marketing
Community Detection
Find out all the sets of
entities that match to
the given pattern – e.g.
fraud detection
Graph Pattern Matching
customeritems
Purchase Record
Communication
Stream (e.g. tweets)
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
33
CyberSecurity Modeling / Internet of Things (IoT)
•Property graph model
•Dynamic construction of IP
network
•The graph includes metadata as
well as events/enriched data
•Extensible by other data source
(add properties, relations)
•Search – Text search on graph DB
proprieties
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
35
Oracle Property Graph Overview
• Massively-Scalable Graph Database
Detecting Components and
Communities
Ranking/Walking
– Scales securely to trillions edges
– Optimized & Secure schemas:
• Apache Hbase, NoSQL Database
– Parallel Loading
– Support open & Oracle optimized file formats
• GML, GraphML, GraphSON, Oracle
• In-Memory Analyst
Evaluating Communities
Path-Finding
– 35 built-in parallel graph analysis algorithms
– Flexible deployment: Embedded, Remote, YARN
• Simple interfaces
∑
∑
– Java: Tinkerpop: Blueprints, Gremlin, Rexster
– Search: Apache Lucene and SolrCloud
– Scripting languages: Groovy, Python…
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
36
Architecture for Property Graph Support
Graph Analytics
Parallel In-memory Analytic Engine
Apache Lucene
and/or
Apache Solr
(SolrCloud)
Graph Data Access Layer
(APIs)
Java APIs
REST/Groovy
Text Search
Scalable and Persistent Storage
Apache HBase
Oracle NoSQL Database
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
Graph
Graphformats
Model
and format
Property Graph
Formats
RDF
(RDF/XML, NGraphML,
Triples,
N-Quads,
GML,
TriG,N3,JSON)
Graph-SON,
Flat Files
Key Features: In-Memory Analyst
Built-in Algorithms and Graph Mutation
A rich set of built-in, parallel algorithms
Parallel graph mutation operationsCreate Undirected
Graph
Detecting Components
and Communities
Tarjan’s, Kosaraju’s,
Weakly Connected
Components, Label
Propagation (w/ variants),
Spasification
Evaluating Community
Structures
∑
∑
Conductance,
Modularity
Clustering Coefficient
(Triangle Counting)
Ranking and Walking
Pagerank, Personalized Pagerank,
Betwenness Centrality (w/
variants),
Closeness Centrality, Degree
Centrality,
Eigenvector Centrality, HITS,
Random walking and sampling (w/
variants)
Path-Finding
Hop-Distance (BFS)
Dijkstra’s,
Bi-directional Dijkstra’s
Bellman-Ford’s
a
f
d
Left Set: “a,b,e”
d
a
g
f
g
b
b
d
e
e
h
g
i
b
i
c
e
c
h
i
c
Create Bipartite
Graph
a
d
g
b
f
Sort-By-Degree (Renumbering)
d
e
g
b
i
Link Prediction
a
The original graph
SALSA
(Twitter’s Who-to-follow)
e
b
Other Classics Vertex Cover
d
i
a
f
c
g
e
h
h
c
Filtered
Subgraph
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
i
Simplify Graph
38
Features: Support Big Data SQL
Apache
Hive
External
table
Oracle RDBMS
SQL based aggregation
and analytics
Apache HBase
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
39
Performance on Oracle NoSQL Database
Oracle NoSQL Database on a 6-Node BDA cluster (128GB RAM/node )
2+ billion edges
Loading Time of LiveJ Graph
Execution Time of Basic Operation (ms)
1000000
80
60
50
40
30
Time (secs)
70
100000
10000
20
10
0
1000
1
2
4
8
16
32
DOP
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40
Performance on Apache HBase
Apache HBase on a 6-Node BDA cluster (128GB RAM/node)
5+ billion edges
Loading performance
Time (min)
310
146
100
71
Time (secs)
5000
1000
1027
541
500
37
21
16
14
13
279
149
93
10
75
76
GetEdgesPart
itioned:
splitsPerRegi
on=1,
splits=24
50
1
1
1
2
4
8
16
24
32
48
2
4
8 16 24 32
Degree of Parallelism (DOP)
Degree of Parallelism
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
41
1000
100
10
1
0.1
1000
100
10
1
Execution Time (secs)
Spark (16)
Web
Eigenvector Centrality
10000
1000
100
10
1
0.1
Spark (4)
Spark (2)
Oracle
Twitter
Spark (16)
Spark (8)
Spark (4)
Web
Spark (2)
Oracle
Spark (16)
Spark (8)
Spark (4)
Spark (2)
Oracle
Spark (16)
Spark (8)
Spark (4)
Spark (2)
Spark (8)
1
Spark (16)
10
Spark (8)
100
Spark (4)
Spark (2)
Oracle
Spark (16)
Spark (8)
Spark (4)
Spark (2)
Oracle
Spark (16)
Execution Time (secs)
Single-Source Shortest Path
Twitter
1000
Twitter
10000
Web
Hop-Dist
10000
Oracle
– CPU: Intel “Sandy Bridge”, Xeon E5-2660, 2.20
GHz, 8 Cores (x 2 HT)
– Memory: 256 GB (DDR3 – 1600)
– SSD: 3 x 256 GB (combination of OCZ Vertex 4 and
Samsung 840 Pro)
– Network Card: Mellanox Connect-IB (InfiniBand
Adapter)
– Switch: Mellanox SX6512 (InfiniBand Switch)
Spark (8)
Twitter
Spark (4)
Spark (2)
Oracle
Spark (16)
Spark (8)
Spark (4)
Spark (2)
Oracle
• Environment: homogeneous computing
cluster:
Pagerank
10000
Execution Time (secs)
• Oracle on a single node is up to 2 orders of
magnitude faster than Spark GraphX
distributed execution on 2 to 16 nodes
Execution Time (secs)
Oracle’s In-Memory Analyst vs Spark GraphX 1.1
Web
Data sets: Verticies / Edges
Twitter followership 2010:
41,652,230
Web page relations .UK domain: 77,741,046
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
1,468,365,182
2,965,197,340
42
Oracle’s In-Memory Analyst vs. GraphLab
• On a single machine is faster than
existing
–Distributed execution or
–Out-of-core execution
Two orders-of-magnitude
faster than disk-based
execution
Runtime in Seconds
3x – 10x faster than 16-machine
distributed execution
PageRank
1000
100
10
1
0.1
0.01
LiveJ
PGX (SPARC)
In-Memory analyst : x86 and SPARC (T5)
PGX (X86)
Twitter
GraphLab (X86 x 16)
SQL (X86)
Triangle Counting
GraphLab (state-of-art distributed
framework)
SQL: disk-based
Execution time of two popular graph
analysis algorithms (log scale, lower is
better)
Runtime in Seconds
100000
10000
1000
100
10
1
LiveJ
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
Web-UK
Summary
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
44
Property Graph and RDF Graph
RDF Graph
Property Graph
• Has formal theoretical foundation:
interpretation, entailment, description
logic
• Hard to associate properties with edges
• Simpler: no formal theoretical foundation, no
semantics, no inference
• Numerous W3C and OGC standards
• Community driven. No standards yet
• Very natural to handle multiple RDF
graphs at the same time
• Processing multiple property graphs is hard
• Has many curated terms, ontologies
• Has no standard terms, vocabularies
• An RDF graph can be modeled as a
property graph with a loss of semantics
• A property graph can be modeled as an RDF
Graph
• Easy to associate properties with edges
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
RDF Semantic Graph Summary
• Standards based: W3C, OGC
• Multi-platform: Oracle Database, NoSQL Database, Oracle Cloud
• Scalability: Trillions of triples
• Transactional: Concurrent loading and updates with ACID properties
• Security: OLS security labels at “triple” level (OLS).
• Manageable: Use existing DB tools, utilities and expertise
• Multi-type support: graph, relational, search, geospatial …
• Oracle & 3rd Party Tools: OBIEE, Oracle Advanced Analytics, TopQuadrant,
Tom Sawyer, IO Informatics, Jena, Protégé, Cytoscape
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
Property Graph Summary
• Complete platform:
• Secure databases + Text indexing/search + Built-in analytics + Open source Java APIs & scripting
languages for developers + Groovy console + integration w/ relational & SQL-based analytics
• Scalable:
• Distributed database and text indexing/search; parallel in-memory analytics are concurrent &
multiuser; filter queries refine graph data read into memory for analysis
• Fast
• Parallel everywhere - load, query and in-memory analytics
• Flexible:
• Deploy on-premise or in the Cloud, store in RDBMS, Hbase & NoSQL, text search w/ Lucene &
SolrCloud, 3 ways to deploy in-memory analytics, extensible analytics architecture
• Open Source-based: Apache, Java, Tinkerpop APIs; Groovy, Python… scripting languages
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
47
Q&A
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
48
Resources
• OTN: Oracle Spatial and Graph - RDF Semantic
Graphhttp://www.oracle.com/technetwork/database/options/spatialandgraph/overview/rdfsemantic-graph1902016.html
• OTN: Big Data Lite Virtual Machine (a free sandbox environment to get started):
http://www.oracle.com/technetwork/database/bigdata-appliance/oracle-bigdatalite-2104726.html
• Oracle.com:
https://www.oracle.com/database/big-data-spatial-and-graph
• OTN: Oracle Big Data Spatial and Graph – property graph (trial software downloads, doc, help forum)
http://www.oracle.com/technetwork/database/database-technologies/bigdata-spatialandgraph
• Blog: (technical examples and tips):
https://blogs.oracle.com/bigdataspatialgraph/
Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
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Safe Harbor Statement
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Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
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Copyright © 2015 Oracle and/or its affiliates. All rights reserved.
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