Download - On-Demand RDF Graph Databases in the Cloud
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On-Demand RDF Graph Databases in the Cloud
A webinar with
Marin Dimitrov, CTO of Ontotext
Jun 11th, 2015
On-Demand RDF Graph Databases in the Cloud #1 Jun 2015
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• The Self-Service Semantic Suite (S4)
• RDF graph databases
• On-demand RDF databases in the Cloud
• Demo
• Roadmap
• Q&A session
Today’s topics
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About Ontotext
• Provides products & solutions for content enrichment, metadata management & information discovery
– 70 employees, headquarters in Sofia (Bulgaria)
– Sales presence in London & New York
• Major clients and industries
– Media & Publishing
– Health Care & Life Sciences
– Cultural Heritage & Digital Libraries
– Government
– Education
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Some of our clients
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Our vision for Smart Data management
Graph Database • Flexible RDF graph
data model • Ontology based
metadata layer
Semantic Search • Semantic,
exploratory search • Metadata driven
content
Text Mining & Interlinking • Interlink people,
locations, organisations, topics
• Discover implicit relations • Reuse open knowledge
graphs
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Ontotext and AstraZeneca
Profile • Global, Bio-pharma company • $28 billion in sales in 2012 • $4 billion in R&D across three continents
Goals • Efficient design of new clinical studies • Quick access to all of the data • Improved evidence based decision-making • Strengthen the knowledge feedback loop • Enable predictive science
Challenges • Over 7,000 studies and 23,000 documents
are difficult to obtain • Searches returning 1,000 – 10,000 results • Document repositories not designed for
reuse • Tedious process to arrive at evidence
based decisions
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Ontotext and the Financial Times
Profile • Top 3 business media • Focused both on B2C publishing and B2B
services
Goals • Create a horizontal platform for content
enrichment and recommendation based on semantics
Challenges • Critical part of the entire workflow • Move fast from inception to production
deployment • GraphDB used not only for data, but for
content storage as well
• Horizontal platform with focus on organizations, people and relations between them
• Automatic extraction of all these concepts and relationships
• Personalised recommendations of relevant content across the entire media
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Ontotext and LMI
Profile • Established in 1961 to enable federal
agencies • Specializes in logistics, financial,
infrastructure & information management
Goals • Unlock large collections of complex
documents • Improve analyst productivity • Create an application they can sell to US
Federal agencies
Challenges • Analysts taking hours to find, download
and search documents, using inaccurate keyword searches
• Needed a knowledge base to search quickly and guide the analysts – highly relevant searches
• Extracts knowledge from collection of documents
• Uses GraphDB to intuitively search and filter • More than 90% savings in analyst time • Accurate results
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The Self-Service Semantic Suite (S4)
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• Capabilities for text analytics, content enrichment and smart data management
– Text analytics for news, life sciences and social media
– RDF graph database as-a-service
– Access to large open knowledge graphs
• Available on-demand, anytime, anywhere
– Simple RESTful services
• Simple pay-per-use pricing
– No upfront commitments
What is S4?
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What is S4?
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Today’s webinar focus
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• Enables quick prototyping
– Instantly available, no provisioning & operations required
– Focus on building applications, don’t worry about infrastructure
• Free tier!
• Easy to start, shorter learning curve
– Various add-ons, SDKs and demo code
• Based on enterprise semantic technology by Ontotext
Benefits
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Getting started in minutes
#13
1. Register a personal account at s4.ontotext.com
2. Generate an API key pair
3. Check out the docs, demos & code at
docs.s4.ontotext.com
4. Contact us with questions!
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• Text analytics services
– News annotation
– News categorisation
– Biomedical
• Entity linking & disambiguation
– Mappings to DBpedia & GeoNames instances
– Mappings to biomedical data sources (LinkedLifeData)
• HTML, MS Word, XML, plain text input
• Simple JSON output
Text analytics with S4
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News analytics example
#15
S4 result
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• SPARQL query endpoint to the FactForge semantic data warehouse
– 500 million entities / 5 billion triples
• Key LOD datasets integrated
– DBpedia, Freebase/WikiData, GeoNames, WordNet
– Dublin Core, SKOS, PROTON ontologies and vocabularies
Knowledge graphs with S4
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Knowledge graph query example
#17
SPARQL query using DBpedia
data
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RDF Graph Data Management
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• Schema-less data integration, easy querying of diverse data
• Standards compliance
– Based on a mature set of W3C standards: RDF/S, OWL, SPARQL
– Portability & interoperability across vendors
• Complex & exploratory queries
• Infer implicit relations in the graph
• Reuse open knowledge graphs (Linked Open Data)
RDF for smart data management
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A visual view of RDF data
#20
Sub-properties Sub-classes
Transitive relations Inference
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• High performance RDF database, 10s of billions of triples
• Full SPARQL 1.1 support
• Various reasoning profiles, including custom rules
• Efficient data integration (“sameAs” optimisations) and deletion of statements & their inferences
• Geo-spatial indexing & querying with SPARQL
• RDF Rank, full-text search, 3rd party plugins
• Connectors to Solr, ElasticSearch, NoSQL DBs
• GraphDB Workbench
GraphDB by Ontotext
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“Despite all of this attention the market is dominated by Neo4J and Ontotext (GraphDB), which are graph and RDF database providers respectively. These are the longest established vendors in this space (both founded in 2000) so they have a longevity and experience that other suppliers cannot yet match. How long this will remain the case remains to be seen.”
Graph databases report by Bloor
Bloor Group whitepaper Graph Databases, April 2015 http://www.bloorresearch.com/technology/graph-databases/
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On-demand RDF Databases in the Cloud
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• Ideal for customers who are…
– still evaluating and testing RDF technology
– In the early phase of adoption / PoC
• Enterprise grade RDF database in the Cloud
– No need for upfront payments for licenses & hardware
– Pay only for what you use, when you use it
– Instantly operational within minutes
– No need for complex planning - use as many DB instances for as long as needed
– Timely upgrades to the latest version
• Self-managed and fully managed options
RDF database in the Cloud with S4
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• Available from AWS Marketplace, “1-Click” purchasing
• Variety of hardware configurations
– 2 to 8 CPU cores / 8 to 61 GB RAM
– IOPS performance & encryption (EBS)
• Manage large data volumes
• Pay-per-hour pricing
• Users take care of operations
– Backups, restores
Self-managed RDF DB in the Cloud
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Self-managed RDF DB in the Cloud
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• Low-cost graph DBaaS available 24/7
• Ideal for small & moderate data & query volumes
– database options: 1M, 10M, 50M, 250M & 1B triples
• Instantly deploy new databases when needed
• Zero administration
– automated operations, maintenance & upgrades
• Users pay only for the actual database utilisation
• Standard OpenRDF REST API
Fully managed RDF DB in the Cloud
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Fully managed RDF DB in the Cloud
#28
Database type Max triples
micro 1 million
XS 10 million
S 50 million
M 250 million
L 1 billion
On-Demand RDF Graph Databases in the Cloud Jun 2015
FREE!
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Fully managed RDF DB in the Cloud
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• Evaluate the technology
• Instant deployment, faster experimentation
• Faster application development
• Data services / Open Data publishing
• Reducing TCO & risk
Use cases for an RDF DBaaS
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• Cloud native architecture, running on AWS
• Designed for elasticity & high availability
– More resources added whenever needed
– Failed nodes replaced immediately
• GraphDB is the RDF DB engine
– OpenRDF REST API
• Isolation of the multi-tenant databases
– Docker containers
– Private NAS volumes (EBS) for data storage
Fully managed RDF DB in the Cloud
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OpenRDF REST API
#32
resource operations comments
/repositories GET Get info on DB repos
/repositories/<REPOSITORY> GET, POST, PUT, DELETE Create*, delete, query a repository
/repositories/<REPOSITORY>/size GET Gets the number of triples in a repository
/repositories/<REPOSITORY>/statements GET, POST, PUT, DELETE Add, read, update, delete statements
repositories/<REPOSITORY>/rdf-graphs/<GRAPH> GET, POST, PUT, DELETE
Same as above
/settings GET, PUT Configure the DBaaS*
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Uploading data (OpenRDF Workbench)
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Uploading data (OpenRDF Workbench)
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Uploading data (curl)
#35
API_KEY=… KEY_SECRET=… USER=… DATABASE=… REPOSITORY=… SERVICE_ENDPOINT="https://$API_KEY:[email protected]/$USER/$DATABASE" curl -X POST -H "Content-Type:application/rdf+xml;charset=UTF-8" -T example.rdf $SERVICE_ENDPOINT/repositories/$REPOSITORY/statements
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Uploading data (Java / OpenRDF SDK)
#36
String dbaasURL = "<dbaas URL>"; String repositoryId="<repository ID>"; String pathToTheFile="<pathToTheFile>"; String ApiKey = "<api-key>"; String ApiPass = "<api-pass>"; //The base URI to resolve any relative URIs that are in the data against. String baseURI="http://www.example.org"; // Create a RemoteRepositoryManager RemoteRepositoryManager manager = RemoteRepositoryManager.getInstance(dbaasURL, ApiKey, ApiPass); // Open a connection to the repository Repository repository = manager.getRepository(repositoryId); RepositoryConnection repositoryConnection = repository.getConnection(); // upload RDF data File fileToUpload=new File(pathToTheFile); repositoryConnection.add(fileToUpload, baseURI, RDFFormat.RDFXML); // close the connection repositoryConnection.close();
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Querying data (OpenRDF Workbench)
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Querying data (OpenRDF Workbench)
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Querying data (curl)
#39
API_KEY=… KEY_SECRET=… USER=… DATABASE=… REPOSITORY=… SERVICE_ENDPOINT="https://$API_KEY:[email protected]/$USER/$DATABASE" SPARQL_QUERY="…" curl -X POST -H "Accept:application/sparql-results+xml" -d "query=$SPARQL_QUERY" $SERVICE_ENDPOINT/repositories/$REPOSITORY
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Demo
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• (Create a database)
• Create a repository
• Upload sample data
• Query the data
• Explore data with a 3rd party tool
Demo scenario
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Create a database
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Micro, XS, S, M, or L
R/O access to Open Data services or open knowledge
graphs
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Create a repository
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Inference ruleset
Cache distribution
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Uploading data (OpenRDF Workbench)
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Sample data (European country populations)
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Uploading data (OpenRDF Workbench)
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Uploading data (OpenRDF Workbench)
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Querying data (OpenRDF Workbench)
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Querying data (OpenRDF Workbench)
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Exploring data (Metreeca Graph Rover)
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Exploring data (Metreeca Graph Rover)
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Exploring data (Metreeca Graph Rover)
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Exploring data (Metreeca Graph Rover)
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Roadmap
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• Various improvements (backup & export)
• Gradually introduce XS, S, M and L databases
• Increased availability
– Cross-datacenter replication
• Integration with the GraphDB Workbench
Work in progress
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GraphDB Workbench
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Key Takeaways
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• S4 provides an enterprise RDF DBaaS
• Free graph databases up to 1M triples
• Instantly available whenever needed
• Easy to use: OpenRDF REST services
• Zero administration: automated operations, maintenance & upgrades
• Resilient design, high availability
• Check out http://s4.ontotext.com
Key Takeaways
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• Online documentation
– http://docs.s4.ontotext.com/
• Helpdesk
– http://support.s4.ontotext.com/
• Sample code & demos on GitHub
– https://github.com/Ontotext-AD/S4
– @Ontotext_S4
Additional S4 resources
#59 On-Demand RDF Graph Databases in the Cloud Jun 2015
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On-Demand RDF Graph Databases in the Cloud
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Jun 11th, 2015
#60 On-Demand RDF Graph Databases in the Cloud Jun 2015
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DBaaS architecture on AWS
#61 On-Demand RDF Graph Databases in the Cloud Jun 2015