• Founded in 2005; offices in Washington DC & Boston
• Application development & consulting
• Customers in US Gov't, banking/financial, energy, health/bio, retail
• Strong academic partnerships in US, UK, Europe, and Mexico
• Experts in all things semantic
• OWL/RDF/SPARQL/SWRL and any other acronym
• Information Integration, Expertise Location, Policy Management, Enterprise Decision Support, Application Development

• Graphs are everywhere
• Natural way to model many problems and domains
• Ideal for data integration
• Semantics are a natural complement
• Declarative, formal descriptions of nodes, edges, and their relationships
• Semantic Graphs
• Graphs with meaning
• Obvious choice for data integration and analysis problems

• Non-programmers exist!
• Not everything needs to be in code
• Especially the business logic
• Can't teach everyone programming
• What can do we do?
• Encode the business logic using semantics
• Frees it from the codebase, and thus programmers
• By encoding it in the graph
• Lets non-programmers do complex information processing without having to write code
• Actual experts in business logic can implement it

• Scale is not a necessary condition for utility
• Not all problems are solved by adding more data
• Getting value from data comes down to how easily you can do something with it
• Smart data is data with semantics attached to it
• Gives data meaning
• More specifically, data with a computer understandable meaning
• Which means the computer can help
• And that makes it easier to utilize data
  
• Analysis, BI, decision support, etc.

• Based on W3C standards
• HTTP as the protocol, URL/URI as identifiers
• RDF: graph data model
• SPARQL: Query over RDF
• OWL & Rules: encode business logic in the graph
• Data format, protocol, semantics; none are proprietary
• Promotes re-use and makes interoperability easier
• Semantics do not change; data retains original meaning
• Both schema-less and schema-rich
• And everything in between
• Maintain advantages of graph database while adding features valuable to an enterprise

• Leading RDF graph database
• Pure Java
• Community & Enterprise Editions
• Great developer experience
• Rich feature set
• Currently version 2.1.1

• Query
• Loading lots of data is not useful if you cannot query it
• Query 100M triples with a throughput of 3M+ queries per hour.  1B with nearly 500k queries/hour and 10B with nearly 20k queries/hour
• This is BSBM with 64 concurrent clients
• Fastest SP2B benchmark results at 5M, only known implementation to complete 25M, close to completing 100M
• Scale
• Up to 50B triples/quads on modest hardware
• Load rates up to 500k triples/second
• That's 100M triples in 3 minutes, 1B in 30, and 20B in 20 hours.

• Stardog powers the Best Buy Metis "Like for Like" service
• Given a product SKU, return most similar products
• Searches over 60 million triples representing the Best Buy product catalog
• Also used for the backend to their 2013 Stocking Stuffer promo

• Represent security policies in XACML
• Automatically convert policies to OWL
• Load into Stardog and using reasoning for ...
• Are there holes in the policy set?
• i.e. Do my policies do what I think they do?
• Do any policies allow actions that other policies deny?
• Are there any policies which are redundant?
• Create a unit test suite for policy sets
• Explanations for traceability into policy issues
• ICV to keep policy data consistent and correct

• Rapidly standing up new internal SPARQL endpoints
• Storing voiD and SPARQL service descriptions in Stardog
• Use as a catalog of what data is available, and where
• Using reasoning to specify ACLs for data sources 
  

• NASA is a big, interesting organization
• 100k+ employees across 12 centers throughout the US
• They have a universe of data
• But one simple, terrestrial problem, finding experts
• COTS solutions were not working
• NASA had all the information they needed in-house
• Enter POPS
• Treat expertise location as a data integration problem
  
• Create model to unify distinct, but relevant, data sources
• Use reasoning to infer new connections in the data
• Saved NASA $38M a year
• An official W3C case study for use of semantic technologies

• First and foremost, we are developers too
• We intend for the best out of box experience possible
• Excellent documentation
• Easy installation, just unzip
• We <3 the command line
• Modeled after the Git command line, autocomplete support
• Intended for devs & admins alike
• Like Java?  
• Jena, Sesame, Spring, SNARL
• Prefer something else?
• Javascript, Ruby, Python, .Net, Groovy
• Access via HTTP using W3C standards, or native binary protocol

• Integrity Constraint Validation keeps data safe and consistent
• Prevent modifications that violate your integrity constrains
• 'Guard mode'
• Constraint violations abort transactions
• Also support 'oracle' mode, aka 'middleware' mode
• Outside of a transaction
• Check if data valid w.r.t some constraints
• Violations can be explained
• Inferences can satisfy or violate a constraint
• Constraints expressed in SPARQL, OWL, SWRL, or Stardog Rules
• High-level declarative languages make it easy to write simple constraints, possible to write complex ones

Supervisor subClassOf supervises some Employee  

Project and (fundedBy only InternalFundingSource) subClassOf approvedBy value InternalBudgetOffice 

select * where { 
    ?x a Project . 
    FILTER NOT EXISTS {
        ?x fundedBy ?y . 
        FILTER NOT EXISTS { 
            ?y a InternalFundingSource 
        } 
    } . 
    FILTER NOT EXISTS {
        ?x approvedBy InternalBudgetOffice 
    } 
} 

• If you are using ICV
• You may not understand why a violation occurred
• Or want to communicate it to the user
• Explanations
• Tells you why the violation occurred
• Shows exactly the data that caused the violation
• Gives you the proof used to derive the violation

• Make implicit information explicit
  
• Implicit in the schema, or data, or both
• Represent domain knowledge in a formal declarative model
• Called an ontology
• Like UML, but with formal semantics
• W3C specification called OWL, Web Ontology Language
• Reasoners consume ontologies to derive new information
• Answer queries, find inconsistencies
• Complex, but manageable
• OWL divided into profiles with less expressivity, but better computational properties 

• Unmatched OWL support
• All OWL2 profiles (RL, EL, QL, DL) and Stardog profile (SL)
• Caveats, no equality reasoning, no datatype reasoning, no DL reasoning over your ABox
• Query time reasoning
• No write performance penalty
• Pay for what you use
• Explanations
• Inference you don't understand?
• Reasoner will give you the proof used to derive it!
• Reasoning Services
• Consistency checking, satisfiability

• Stardog supports SWRL
• Part of the SL profile
  
• You cannot write it by hand, SWRL/RDF is unusable
• Much easier use Stardog Rules
• If-Then style rules based on SPARQL syntax:

PREFIX :
PREFIX math: 
IF {
    ?c a :Circle ;
         :radius ?r
    BIND (math:pi() * math:pow(?r, 2) AS ?area)
}
THEN {
    ?c :area ?area
} 

• SPARQL 1.1
• Update, query, graph protocol
• Custom query planner, optimized for complex queries
• Targets BI/analytic queries
• And also reasoning
• But does not sacrifice performance at low scales or with simple queries
• Scalable query answering
• Intermediate results can get big, and fast
• Runtime will automatically flow results off-heap, and then to disk as needed
• Query management  
  

• Embeds Lucene
• Automatically managed by database as if another RDF index
• Enables full-text searches over your RDF
  
• Literals are indexed by Lucene
• Lucene query language used search data
• Seamless integration via SPARQL
• Join results of full-text searches with regular SPARQL query
• Also available via SNARL Java API

• JMX server monitoring
• High Availability
• Hot Backup & Restore
  
• Access/Audit logging
• Web console built on Stardog Web Framework
• PROV and SKOS support
• ACID Transactions
• Rich Security model

• Graph analytics
• Model versioning
• Named graph security
• Stored Procedures
• GeoSPARQL
• Materialized views
• Equality reasoning
• Administrative Web Console
• And as always, faster & more scalable

• Focus on the Web part of Semantic Web
• Organizations don't always have experts in semtech available
• Provide a framework that abstracts away these details
• Stick to well-known web technologies
• HTML, CSS, Javascript, JSON as data
• backbone.js as a model layer, SPARQL Routes middleware
• Goal is to provide good out of the box capabilities
• Faceted browsing, semantic search, REST, CRUD, etc.
• Minimal programming or configuration required, just add data
• Provide basis for quickly building web apps based on semtech
• Aimed at data discovery/exploration use cases
• Stardog Web Console built on this technology

• Transactions
• ACID
• Guarded (optionally) by ICV
• 2 Phase Commit over all database components
• RDF Index, Lucene, KB, etc.
• Automatically managed by the database
• Security
• RBAC model
• Based on Apache Shiro
• R/W ACLs for access to individual databases
• Administrative controls for actions against DBMS
• Online/offline a database, modify security settings, etc.

• Coming in Stardog 2.2
• RDF graphs are still just graphs
• Graph measures: in-degree, out-degree, PageRank, betweenness centrality
• Clustering: weak/strongly connected components, clique finding
• Path finding: BFS and shortest path
• Seamless SPARQL integration

• For example, enforcing security (ACLs)
• Can Bob access Resource1?

Bob is-a Admin OR Bob created Resource1 OR (Bob hasRole ?r AND ?r canAccess Resource1) OR ... 

• Hard to maintain, encoded domain knowledge into the query
• Can leverage reasoning to simplify

Bob canAccess Resource1 

• More concise and maintainable
• Reasoner handles the implementing logic transparently