Real databases can often be
very complex and their schemas can comprise thousands of tables,
elements, attributes, etc. Any one wishing to interact with such a
complex database first has the daunting task of understanding the
database schema. In this talk, I will propose the concept of schema
summary, which can provide a succinct overview of the underlying complex
schema and significantly reduce the human effort required to understand
the database. I will define criteria for good schema summaries, and
describe efficient algorithms for producing them.
User effort in locating schema elements needed to construct a structured
query can be greatly reduced with a schema summary, which allows the
user to explore only portions of the schema that are of interest.
Nonetheless, as the query complexity increases, this approach of
querying through exploration is no longer a viable option because a
significant percentage of the schema will have to be explored. By
leveraging schema summary and a novel schema-based semantics for
matching meaningful data fragments with structure-free search
conditions, I will propose a novel query model called Meaningful Summary
Query. The MSQ query model allows the users to query a complex database
through its schema summary, with embedded structure-free conditions. As
a result, an MSQ query can be generated with the knowledge of the schema
summary alone, and yet retrieve highly accurate results from the
database.