Who Wants to Map?

• Semantic Web Scalers
  • Expose whatever there is as RDF, the next guy will unify terms, make search and apps

• Data Warehouse Keepers
  • Data is spread out, has implicit semantics, complex schemas, heterogeneous sources, ambiguous terms but we must make it join and aggregate cleanly

Present State

• SPARQL to SQL exists but still, complex integrations are data warehouses
• We'd really like to map, but...
• Can it be otherwise?

Why RDF Data Warehouse?

• Pros
  • Even query performance across all data
  • Possibility of forward-chaining inference
  • Some SPARQL features may be better supported, e.g. Unspecified predicates

• Cons
  • Keeping data up-to-date
  • Complex set up, needs dedicated servers: you don't build them on a whim

Why Map?

• No copying, no timeliness issues
• RDBMS outperforms RDF for analytics workloads
• Agile reconfiguration without reloading data

Virtuoso

• Mapping of SPARQL to SQL against any existing schema - whether stored in Virtuoso or elsewhere
• Physical quad store
• Federated/local RDBMS

For Mapping to Deliver...

• Tackle any SQL analytics workload in SPARQL without extra cost
• Deal with arbitrary SQL schema
• Produce single SQL statements, optimizable by target RDBMS
• Have intelligence for cases where one RDF entity can come from many relational sources

The Cases of Integration

• Bring similar but heterogeneous schemas into a unified ontology - Union View
• Translate FKs of one schema to PKs in another - Distributed Join
• Hide differences in normalization - Views for hiding joins
• - Unit/Terminology conversions

Defining a Mapping

Use SPARQL/SQL to:

• Define URI formats and their subclass relations
• Define which key-column-value combinations make a triple
• Arbitrary SQL is allowed for mapping values and filtering
• A single RDF node can be a composite of many columns, e.g. multipart key

The TPC-H Case

• The 22 queries as extended SPARQL
• Each generates a single SQL statement, executable by Virtuoso, Oracle, Others
• Next make several TPC-H databases on different servers and run the queries against the union

Where Problems Begin

• In OpenLink Data Spaces , 6 Collaborative apps all mapped to SIOC:

  select * from <ods> where {?s ?p ?o . ?s has_comment ?c . ?c has_author <xxx> }

• Trivially becomes a union of everything, 1000+ lines of SQL
• Intelligently (once per app) becomes a Union of:

select post.* from post, comment, user where c_post = p_id and c_author = u_id and u_name = f ('xxx')

What One Must Know

• Mapping for integration is not trivial
• Be careful when mapping multiple tables/columns to one class/property
• Make URI schemes which encode type and source, so that senseless joins are not attempted if types not specified in query
• Understand what the mapping logic can and cannot optimize
• Understand what SQL can and cannot optimize
• View resulting SQL for sanity check

SQL Extensions

• Mapping must work against any RDBMS/Schema, as is
• But there is Virtuoso SQL between the mapping and target RDBMS(s)
• Location and latency - conscious distributed cost model
• Breakup for making a wide result set into a row per property
• Inverse functions

Use Cases

• OpenLink Data Spaces - Blog, Wiki, News, Social Network, Feed Aggregation, Tag Clouds, Bookmarks etc.
• OpenLink Software 's own MIS - "total information awareness": URI for any CRM Object, Account, Product, Support Case, Email etc..
• Musicbrainz
• phpBB , Drupal , MediaWiki , WordPress , Bugzilla , and others.

OpenLink Software