RDF Triple Stores

1
RDF Triple Stores

• Nipun Bhatia
• Department of Computer Science. Stanford
  University

2
Contents

• Introduction
• Different Architectures
• Implications
• An Example Jena SDB
• Evaluations
• Evaluations using LUBM/DBPedia
• Open Research Issues
• Which RDF Store to choose for a particular
  application?
• Possible system diagram for Phenotype
  Annonations.

3
Introduction

• What is an RDF store?
• A system to provide a mechanism for persistent
  storage and access of RDF graphs.
• Potential Applications areas
• Plenty! Backend for Protege, BioPortal,
  Phenotype Annotations.

4
Different Architectures

• Based on their implementation, can be divided
  into 3 broad categories In-memory, Native,
  Non-native Non-memory.
• In Memory RDF Graph is stored as triples in
  main memory. Eg. Storing an RDF graph using Jena
  API/ Sesame API.
• Native Persistent storage systems with their
  own implementation of databases. Eg. Sesame
  Native, Virtuoso, AllegroGraph, Oracle 11g.
• Non-Native Non-Memory Persistent storage
  systems set-up to run on third party DBs. Eg.
  Jena SDB.

5
Implications

• Scalability
• Different query languages supported to varying
  degrees.
• Sesame SeRQL, Oracle 11g Own query language.
• Different level of inferencing.
• Sesame supports RDFS inference, AllegroGraph
  RDFS,
• Oracle 11g RDFS, OWL Prime
• Lack of interoperability and portability.
• More pronounced in Native stores.

6
Jena SDB

• SDB basically is a Java Loader.
• Multiple stores supported MySQL, PostgreSQL,
  Oracle, DB2.
• Takes incoming triples and breaks them down into
  components ready for the database.
• Multiple layouts
• Integration with the Joseki server.
• SPARQL supported.

7
Evaluations

• Third party evaluations for Sesame, Jena SDB,
  Virtuoso
• Oracle 11g company evaluations
• Methodology
• LUBM Lehigh University BenchMark
• DBPedia
• Multiple Queries
• Load Times

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Evaluations

• DB Pedia Database of structured information
  extracted from Wikipedia. Information about
  places, persons, music albums and films2
• LUBM Synthetically generated RDF data
  containing universities, departments, students
  etc.1
• Dataset size
• DataSet1 15,472,624 triples 2.1 GB
• DataSet 2 LUBM 50 2.75 Million LUBM 1000
  55.09 Million
• 3 Queries

9
Loading Time-DataSet1
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Results Query 1

• Simple select query 2 variables

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Query 2

• Unconstrained Select Query only predicate was
  specified.

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Query 3

• Complex Query Uses filter

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Oracle 11g DataSet 2
Ontology (size) RDFS RDFS OWL Prime OWL Prime
Ontology (size) Triples Time Triples Time
LUBM 50(6.8 Million) 2.75 M 12.14 min 3.05 M 8.01 min
LUBM 1000(133.6 M) 55.09M 7h 19m 65.25M 7h 12m
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Observations

• Native Stores perform better than systems using
  third party stores.
• Optimizations are possible
• Each of the systems uses different database
  layouts.
• Virtuoso OGPS,POGS,PSOG,SOPG
• SDB SPO,GSPO
• Hashing on SDB is very bad.

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Open Research Issues

• Inferencing4
• Present common implementations
• Make a number of small queries to propagate the
  effects of rule firing.
• Each of these queries creates an interaction with
  the database.
• Not very efficient
• Approaches
• Snapshot the contents of the database-backed
  model into RAM for the duration of processing by
  the inference engine.
• Performing inferencing in-stream.
• Precompute the inference closure of ontology and
  analyze the in-coming data-streams, add triples
  to it based on your inference closure.
• Assumes rigid seperation of the RDF Data(A-box)
  and the Ontology data(T-box)
• Even this maynot work for very large ontologies
  BioMedical Ontologies

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Open Research Issues

• Query Optimization
• Third party stores undos any optimization done
  at the API level.
• Better performance of native stores points to
  that direction.
• Some work in optimizing SPARQL queries for
  in-memory story.

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Which RDF store to choose for an app?

• Frequency of loads that the application would
  perform.
• Single scaling factor and linear load times.
• Level of inferencing.
• Support for which query language. W3C
  recommendations.
• Special system needs. Eg. Allegograph needs 64
  bit processor.

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Phenotype Annotations
Jena API
Jena API
j
Inferencing
Jena Model
SDB
Jena API
Set of Ontologies required for Phenotype
Annotationseg. PATO, Fly etc.
MySQL / Virtuoso
Phenotype Annotations
Jena API
j
Jena API
Jena Model
SDB
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References

• 1 http//esw.w3.org/topic/RdfStoreBenchmarking
• 2 http//www4.wiwiss.fu-berlin.de/benchmarks-200
  801/
• 3 Kurt Rohloff et al. An Evaluation of
  Triple-Store Technologies for Large Data Stores.
  Comparing Sesame, Jena and AllegroGraph. 2007
• 4N Bhatia, A Seaborne Ingestion pipeline for
  RDF