Download - Choosing the Right Graph Database to Succeed in Your Project

Choosing the Right Graph Database to Succeed in Your

Project

Marin Dimitrov (CTO)

Feb 2016

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

Some of Our Customers

3 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


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)


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)


Semantic Graph Databases – Inferring New Facts


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


Use Cases – Content Management & Recommendation


Use Cases – Metadata-Driven Content Management & Recommendation


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


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)


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


LinkedLifeData – Knowledge Graph


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/



“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



“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


http://www.pwc.com/us/en/technology-forecast/2015/remapping-database-landscape.html












• Q & A


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


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


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


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


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


+ 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


Fully Managed Database-as-a-Service


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






• Q & A


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


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)



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)



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)



Production

• Priorities

− Performance & scalability

− High availability

• Recommended

− GraphDB Enterprise

• Also consider

− GraphDB Standard (on premise / on AWS)



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


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/


http://ontotext.com/products/graphdb/






http://graphdb.ontotext.com/








http://ontotext.com/company/customers/





http://ontotext.com/knowledge-hub/case-studies/










Choosing the Right Graph Database to Succeed in Your Project

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Download - Choosing the Right Graph Database to Succeed in Your Project

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