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  • 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

    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

    Whats different in a graph store from a database perspective is the sheer volume of connections, or relationshipshow people, places, and things relate to one another through those interactions. If your data is rich, youll 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

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  • 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, mult