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Breakout group computation

Explore the challenges and solutions for treating data from multiple systems as one integrated dataset, understanding semantic structure, data mapping, query capabilities, performance, scalability, federated query, and algorithm optimization.

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Breakout group computation

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  1. Breakout group computation RDF triple store vs relational database vs noSQL databases vs X

  2. Issues • Treat data from several systems or locations as one integrated data set (materially or virtually • Get a clear understanding of the semantic structure of data • Data mapping issues. A big part of this is understanding the semantic structure • What data can be expressed • What queries can be posed, esp. with respect to inference, including inference using ontology structure. Built-in query language features, e.g. transitive closure on subsumption • Performance as related to complexity of queries • Scalability to very large datasets • Federated query vs centralized store • Optimization of algorithms

  3. Practical experience Mayo: • SPARQL to SQL performance not practical • RDF materialized also has performance problems – a triple store with data on 100K patients, 3 billion triples needs a Cray supercomputer to get reasonable performance VIVO • many people are looking at performance issues • VIVO can work with different triple stores • There is a performance issue with federated search

  4. Conclusions • Use right tool for the right job, what is a good tool for what question with what kind of data • Understand the semantic structure of your data no matter what tool you use • Ontologies can be used with any data store, not bound to present model of linked data

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