Re: Converting a variety of data formats, containingvarious ki

Hi Folks,

Scenario: You are tasked to build a system that will receive data from a variety of sources. The data arrives in different formats (e.g., CSV, binary, tab-delimited, JSON, XML). The data sources provide different kinds of data (e.g., one source provides book data, another source provides weather data, another source provides gardening data). The data will be converted to a common intermediary form and then from the intermediary form, placed into a data store.

Recently I heard this statement:

                Converting data that is in various formats and contains various kinds
               of data, into a common intermediate form is hard (computationally
               expensive). RDF is the only data model the enables such conversions
               to be done in a way that is computationally tractable. Therefore, we
               must use RDF. And, as a corollary, we must use a triple store to store
               the RDF data.

Do you agree with that statement? If you do, can you provide evidence that RDF is the only data model that supports such conversions in a computationally tractable manner?

If you do not agree with that statement, can you provide evidence on why the statement is false?

/Roger