Map-reduce-merge and Hadoop/Hbase RDF

 Just found this interesting presentation,

Map-Reduce-Merge:  Simpli?ed Relational  Data Processing on  Large Clusters
by Hung-chih Yang, Ali Dasdan Ruey-Lung Hsiao, D. Stott Parker; as presented by Nate Rober  (PDF)

Excerpts:

Extending MapReduce
1. Change to reduce phase
2. Merge phase
3. Additional user-de?nable operations
a. partition selector
b. processor
c. merger
d. con?gurable iterators

Implementing Relational Algebra Operations
1. Projection
2. Aggregation
3. Selection
4. Set Operations: Union, Intersection, Difference
5. Cartesian Product
6. Rename
7. Join

[for more detail see full slides]

Conclusion
MapReduce & GFS represent a paradigm shift in data processing: use a simpli?ed interface instead of overly general DBMS.
Map-Reduce-Merge adds the ability to execute arbitrary relational algebra queries.
Next steps: develop SQL-like interface and  a query optimizer.

Research paper: Map-reduce-merge: simplified relational data processing on large clusters (PDF for ACM people)

Linked from HRDF page in the Hadoop wiki, where there appears to be a proposal brewing to build an RDF store on top of the Hadoop/Hbase infrastructure.

Nearby: LargeTripleStores in ESW wiki

Not entirely unrelated: Google Social Graph API  (which parsers FOAF/RDF from ‘The Web’ but discards all but the social graph parts currently)