1. XML-Based RDF Data Management for Efficient Query Processing
Mo Zhou advised by Prof. Yuqing Wu
Problem and Related Works
RDF data and query RDF data model SPARQL Query
Relational approaches for storing RDF data
Triple Store (TS) Vertical Partition (VP) Property Table (PT)
The desired properties of RDF storage model
Preserve semantics High performance Scalable Small overhead
Our Proposal
Use XML technologies to achieve high storage and query efficiency of RDF data.
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RDF Schema Decomposition
gR2X: Decompose RDF schema into XML schemas with deep tree shape and small duplication
wR2X: Decompose RDF schema into XML schemas that can represent frequent queried paths in a workload.
RDF Data Decomposition
SPARQL-to-XQuery Rewrite
Class/Predicate determination: Associate possible classes to each node and possible matching to each predicate variables in the SPARQL query.
SPARQL query decomposition: Decompose the SPARQL query graph into trees.
XQuery query construction: Construct XQuery queries based on the trees.
gR2X
wR2X
Decompose RDF data conforming to the RDF schema to XML documents conforming to the XML schemas.
Experimental Results
The experiments were carried out on MonetDB using Berlin Benchmark.
Performance comparison on 5 million triples Scalability Comparison
XML-Based RDF Data Management for Efficient Query Processing, Mo Zhou, Yuqing Wu, technical report TR683, Computer Science, Indiana University, 2010