Update on the Taxonomy of Use Cases

Taxonomy of Use Cases



   1. Motivation
      1. Derived from real-world scenario
      2. Derived from hypothetical scenario
      3. Small example intended to focus attention on particular issues.
   2. Integration Goals
      1. Source RDB structure at issue
         1. Structured

Consider only highly structured database content, and treat string/text
fields as atomic data types of secondary interest.

   1. Structured + Semistructured

Text fields in the database are elemental.

   1. Structured + Microparsed Tagged Text

Text fields in the database are parsed and tagged per an existing ontology.

   1. Other data source
         1. Structured
         2. Structured + Semistructured
         3. Structured + Microparsed Tagged Text
         4. Mash-up

RDB2RDF output commingled with other semantic data sources, but no detailed
federated data operations needed, i.e. no joins

   1. Role of Ontology
      1. Domain Ontology

Mapping problem includes, a priori, a federating ontology. (To discuss: Are
there other roles an existing domain ontology might play?)

   1. Putative

Mapping problem is not particular about an a posteri ontology. Ontology is
created from the RDB. (To discuss, this may include, or we may list as a
different subcase, “easy” renaming/assignment of labels to the output.
Either way, I advocate a distinct case when there is no existing ontology)

   1. Federating

Mapping problem includes forming the federating ontology in the course of
RDF enabling the databases.   i.e. schema integration of existing databases
is handled in the course of completing the use case; similar to creating an
Enterprise Schema at the beginning of a data warehousing effort.  (*of
similar text for data marts)*



   1. Expressivity
      1. Node Label Generation

Graph node names are synthesized from a function of database attributes

   1. Datatype expression
         1. Simple
         2. Relational data (cells) are mapped to rdf datatypes per SQL XSD
         mapping.
         3. Micorparsing

Relational data are parsed and mapped to rdf graphs.

Juan Sequeda
+1-575-SEQ-UEDA
www.juansequeda.com