Metrics

Comments on some of your metrics*

3. Mapping Implementation*: The approaches to convert RDB data to RDF 
can be broadly classified as either a static Extract Transform Load 
(ETL) or a query-driven dynamic implementation. The ETL implementation, 
also called “RDF dump”, uses a batch process to create the RDF 
repository from RDB. The query-driven approach implements the conversion 
dynamically in response to a query. There are multiple advantages and 
disadvantages associated with each of the approaches such as the ETL 
approach may not reflect the most current data, while the query-driven 
approach may have performance penalty due to the on-demand conversion.
[AM] If you create a map from each OWL class to a SQL query and then 
translate the SPARQL query over the ontology
to one or more SQL queries that execute directly on the RDB there is, in 
effect, no mapping (no transformation of data). So, I don't
understand your final " the query-driven approach may have performance 
penalty due to the on-demand conversion."

*4. Query Implementation*: The query implementation can be either a 
direct execution of SPARQL query over a RDF repository or the SPARQL 
query may be mapped to a SQL query which is subsequently executed over a 
RDB.
[AM] It may be worth adding Orri Erling's comment that the generated SQL 
query may be very large and complex and difficult to optimize.
*
5. Application Domain*: As discussed in “Mapping Approach” section, an 
important aspect of RDB to RDF mapping is the incorporation of domain 
semantics. Hence, by identifying the application domain, we may be able 
identify unique domain-specific and some cross-domain common mapping 
characteristics.
[AM] This is a complicated and wide open subject. Perhaps an example 
from the Ordnance Survey presentation may help make it more concrete.

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All the best, Ashok

Received on Friday, 2 May 2008 17:51:40 UTC