RDF languages and storages part 2 - indexing semi-structure data

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RDF languages and storagespart 2 - indexing
semi-structure data

• Maciej JanikConrad IbanezCSCI 8350, Fall 2004

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Outline

• Jena storage
• Indexing techniques

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Jena

• Implemented in Java
• One of the most popularly used RDF storages and
  query engines
• Supports RDF, RDFS and OWL
• In memory and persistent storage (Oracle, MySQL,
  PostgreSQL)
• RDQL
• Reasoning/inference engine

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Jena - storage schema

• Previous version used normalized relational DB
  tables
• statements
• literals
• resources
• Taken approach to store triples as (Subject,
  Predicate, Object) in denormalized tables
• Optimization for common statement patterns -
  grouping of properties

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Jena - storage

• Normalized tables

• Denormalized

Efficient RDF Storage and Retrieval in Jena2 -
Wilkinson et al.
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Jena - storage

• Do certain trade-off for space and search time
• Cluster properties that are likely to be accessed
  together - optimize for common patterns
• Special treatment of reified statements

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Jena - graph abstraction

• Graph interface is separated from (persistent)
  triple storage layer
• Special support for different types of graphs -
  optimized for performance
• Support operations like add, delete, find.

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Jena - query processing

• Converting multiple patterns in query into one
  query to DB
• Use DB query optimizer instead of executing
  multiple queries from Jena level (as it was in
  Jena1)
• Associate a table with pattern (best) or span
  pattern between tables (requires join operation)
• Query may span between different graphs, but it
  can be optimized only if they are in the same
  database

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What to index? How to index?
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Indexing semistructured data

• XML cannot be indexed directly as relational DB
• Indexing may take advantage of tree structure
• depth of node
• common path from the root
• convert each path to string expression
• precalculate the path tree

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Indexing semistructured data

• Idea is based on Particias trie
• Index should scale with the growth of data
• Path together with leaf is encoded into string
  -gt the Index Fabric

A Fast Index for Semistructured Data - Brian F.
Cooper et al.
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A Layered Index
A Fast Index for Semistructured Data - Brian F.
Cooper et al.
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Index Fabric

• Index is used to accelerate path expressions -
  mainly for queries that ask for root-to-leaf path
• Idea of prefix encoding
• xml ltAgtalphaltBgtbetaltCgtgammalt/Cgtlt/Bgtlt/Agt
• paths ltAgtalpha ltAgtltBgtbeta ltAgtltBgtltCgtgamma
• encoded A alpha A B beta A B C gamma
• infix (not common) A alpha B beta C gamma
• Convert path to string for fast searches
• Replace tags with non-terminal characters (like
  in automata)

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Index Fabric - raw paths
A Fast Index for Semistructured Data - Brian F.
Cooper et al.
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Graphs - how to index?
Backbone

• http//www.aisee.com/

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Graphs - how to index?
Tree-type - prefixes - tries

• http//www.aisee.com/

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Graphs - how to index?
T-index Path templates
Index Structure for Path Expressions - Tova
Milo, Dan Suciu
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Graphs - how to index?
Landmarks

• http//www.aisee.com/

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Indexing - summary

• Indexing semistructure data
• index fabric - encoding, multilayered
• common prefixes - trie structure
• backbone - highways between points
• landmarks - county division
• path templates - precalculated expressions
• clustering - grouping by theme access
• Indexing such data is NOT easy, solution depends
  how you want to search the graph

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References

• Efficient RDF Storage and Retrieval in Jena2 -
  Kevin Wilkinson, Craig Sayers, Harumi Kuno, Dave
  Reynolds
• A Fast Index for Semistructured Data - Brian F.
  Cooper, Neal Sample, Michael J. Franklin, Gisli
  Hjaltason, Moshe Shadmon
• Index Structures for Path Expressions - Tova
  Milo, Dan Suciu