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About Ontotext
• Provides products & solutions for content enrichment and metadata management − Founded in 2000, 70 employees
− HQ in Sofia (Bulgaria), sales presence in NYC and London
• Major verticals
− Media & publishing
− Healthcare & life sciences
− Cultural heritage & digital libraries
− Government
− Financial information providers
− Education 2 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Smart Data Management
4
Semantic Graph Database
• Flexible graph data model
• Ontology data model & metadata layer
Enrichment, Search, Discovery
• Metadata driven content • Semantic, exploratory search • Information discovery + recommendations
Text Mining & Interlinking
• Organisations, people, locations, topics, relations
• Discover implicit relations • Reuse open Knowledge Graphs • Interlink with reference data
Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Presentation Outline
• Use Cases for Graph Databases
• GraphDB by Ontotext
• Choosing a Database for Your Project
• Q & A
5 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Graph Databases for Interconnected Data
• Integration of heterogeneous data sources
• Hierarchical or interconnected datasets
• Agile “schema-late” data integration
• Dynamic data models / schema evolution
• Relationship centric analytics / discovery
• Path traversal / navigation, sub-graph pattern matching
• Property graph DBs vs Semantic graph DBs (triplestores, RDF DBs)
6 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Semantic Graph Databases – Advantages
• Simple, graph based data model
• Exploratory queries against unknown schema
• Agile schema / schema-less / schema-late
• Rich, semantic data models (schema)
• Easily map between data models (schemas)
• Global identifiers of nodes & relations
• Inference of implicit facts, based on rules
• Compliance to standards (RDF, SPARQL), no vendor lock-in
• Easy to publish / consume open Knowledge Graphs (Linked Data)
7 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Semantic Graph Databases – Inferring New Facts
8 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Typical Use Cases
• Network analysis (social, influencer, risk, fraud, …)
• Recommendation engines
• Heterogeneous data integration
• Master Data Management
• Metadata driven content / dynamic content publishing
• Knowledge Graphs / data sharing & reuse
• Information discovery / semantic search
#9 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Use Cases – Knowledge Graphs
10 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Use Cases – Content Management & Recommendation
11 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Use Cases – Metadata-Driven Content Management & Recommendation
12 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Ontotext and AstraZeneca
13
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
Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Ontotext and Financial Times
14
• Goals − Create a horizontal platform for
both data and content based on semantics and serve all functionality through it
• Challenges − Critical part of FT.COM
− GraphDB used not only for data, but for content storage as well
− Personalized recommendation based on user behavior and semantic context (Related Reads)
Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Ontotext and EuroMoney
15
• Goals − Create a horizontal platform to
serve 100 different publications
− Platform which would include the latest authoring, storing, and display technologies including, semantic annotation, search and a triple store repository
• Challenges − Multiple domains covered
− Sophisticated content analytics including relation, template and scenario extraction
Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
LinkedLifeData – Knowledge Graph
16 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Graph Database Landscape
“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.”
Bloor Group report Graph Databases, April 2015 http://www.bloorresearch.com/technology/graph-databases/
17 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Graph Database Landscape
“Linking a few data sources is often simple, but to do so with significant amounts of heterogeneous data requires a radically new approach. Graph databases are a powerful optimized technology that link billions of pieces of connected data to help create new sources of value for customers and increase operational agility for customer service. […] they are well-suited for scenarios in which relationships are important.”
Forrester report Market Overview: Graph Databases, May 2015 https://www.forrester.com/Market+Overview+Graph+Databases/fulltext/-/E-RES121473
18 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Graph Database Landscape
“What’s different in a graph store from a database perspective is the sheer volume of connections, or relationships—how people, places, and things relate to one another through those interactions. If your data is rich, you’ll see lots of relationships between the entities in native graph form. Older database technologies place less emphasis on relationships, resulting in less context. Graphs offer the chance for richer context through more connections and any-to-any data models rather than the usual tabular or hierarchical models”
PwC report The promise of graph databases in public health, June 2015 http://www.pwc.com/us/en/technology-forecast/2015/remapping-database-landscape.html
19 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Presentation Outline
• Use Cases for Graph Databases
• GraphDB by Ontotext
• Choosing a Database for Your Project
• Q & A
20 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
GraphDB by Ontotext
• High performance semantic graph database, 10s of billions of triples
• Full compliance to W3C standards
• Various inference profiles, including custom rules
• Extensions
−Geo-spatial, RDF Rank, full-text search, Blueprints/Gremlin, 3rd party plugins
• Tooling for DBAs
21 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Advanced Features
• Connectors to Solr, Elasticsearch, MongoDB*
• Consistency checks
• RDF Rank for graph analytics
• Geo-spatial querying
• Notifications, plugin architecture for 3rd parties
• “Explain plan”
• High-availability cluster
22 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
GraphDB Connectors
Selective replication
Query Processor
Graph indexes Internal indexes
SPARQL SELECT with or without an embedded Solr / Elasticsearch query
Solr / Elasticsearch direct queries
Solr / Elasticsearch GraphDB engine
SPARQL INSERT/DELETE
23 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
High-Availability (Replication) Cluster
• Improved resilience & query performance
• Worker nodes can be added/removed dynamically
• “Graceful degradation” of cluster performance when one or more worker nodes fail
• Flexible topologies, multi-DC deployment
24 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
GraphDB Editions
• Free (+ AWS Marketplace)
• Standard (+ AWS Marketplace)
• Enterprise
• Database-as-a-Service
25 Feb 2016 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Ontotext GraphDB
26 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
+ Java based, deploy anywhere
+ Maven artefacts
+ Docker images
GraphDB on the AWS Marketplace
• “1-Click” purchasing
• Variety of hardware configurations
• Manage big RDF graph data
• Pay-per-hour pricing, 5-day trial
27 Nov 2015 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Fully Managed Database-as-a-Service
• Low-cost DBaaS for Ontotext GraphDB
• Ideal for small to moderate data & query volumes
−database options: 10M (free), 50M, 250M & 1B triples
• Instantly deploy new databases when needed
−Easily scale up / down as data volume changes
• Zero administration
−automated operations, maintenance & upgrades
• Faster experimentation & prototyping, reduced TCO
28 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Fully Managed Database-as-a-Service
29 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Ontotext GraphDB – Key Advantages
1. High availability cluster
2. Performance & scalability
3. Advanced features & extensions
4. Variety of deployment options
5. Developed by an established vendor
6. Full lifecycle support – data modelling, integration, deployment
7. Proven in high-profile business critical use cases
30 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Presentation Outline
• Use Cases for Graph Databases
• GraphDB by Ontotext
• Choosing a Database for Your Project
• Q & A
31 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
From Experimentation to Production
• Priorities: cost, ease of deployment, performance, availability
• GraphDB options: Free, Standard, Enterprise (HA)
• Deployment: on premise, AWS cloud, database-as-a-service
• Seamless upgrade paths
−all options based on the same engine
32 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Learning Prototype Pilot Production
Learning
• Priorities
−Free
−Easy & quick to set up, “sandbox” environment
• Recommended
−Database-as-a-Service (free 10M triples)
−GraphDB Free (on premise / on AWS)
33 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Learning Prototype Pilot Production
Prototype
• Priorities
−Free / low-cost
−Easy & quick to set up, “sandbox” environment
• Recommended
−GraphDB Free (on premise / on AWS)
−Database-as-a-Service (10M – 50M triples)
34 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Learning Prototype Pilot Production
Pilot
• Priorities
− Low-cost
− Performance & scalability
• Recommended
− GraphDB Standard (on premise / on AWS)
• Also consider
− Database-as-a-Service (250M – 1B triples)
− GraphDB Free (on premise / on AWS)
35 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Learning Prototype Pilot Production
Production
• Priorities
− Performance & scalability
− High availability
• Recommended
− GraphDB Enterprise
• Also consider
− GraphDB Standard (on premise / on AWS)
36 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Learning Prototype Pilot Production
Key Takeaways
• Graph databases are well suited for interconnected data, heterogeneous data integration, relationship-centric analytics & discovery, schema evolution
• Use cases include network analysis, MDM, knowledge graphs, metadata management, recommendations, …
• Ontotext GraphDB is an enterprise-grade semantic graph database, proven in mission-critical scenarios
• Various GraphDB deployment options, optimal for learning, prototyping & experimentation, production
37 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project
Links
• Ontotext GraphDB
−http://ontotext.com/products/graphdb/
−http://graphdb.ontotext.com/
−@OntotextGraphDB
• Customers & Verticals
−http://ontotext.com/company/customers/
−http://ontotext.com/knowledge-hub/case-studies/
38 Feb 2016 Choosing the Right Graph Database to Succeed in Your Project