Authors:
Terrence Mason
1
;
Lixin Wang
2
and
Ramon Lawrence
1
Affiliations:
1
Iowa Database and Emerging Applications Laboratory, Computer Science, University of Iowa, United States
;
2
Iowa Database and Emerging Applications Laboratory, University of Iowa, United States
Keyword(s):
Inference, Query, Interface, Schema, Ambiguity, Database.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Enterprise Information Systems
;
HCI on Enterprise Information Systems
;
Human-Computer Interaction
;
Intelligent User Interfaces
;
User Needs
Abstract:
SQL is not appropriate for casual users as it requires understanding relational schemas and how to construct joins. Many new query interfaces insulate users from the logical structure of the database, but they require the automatic discovery of valid joins. Although specific query interfaces implement join determination algorithms, they are tied to the specific language and typically limited in scope or scalability. AutoJoin provides a general solution to the query inference problem, which allows more complex queries to be executed on larger and more complicated schemas. It enumerates query interpretations at least an order of magnitude faster than previous methods. In addition, the engine reduces the number of queries considered ambiguous. Experimental results demonstrate that query inference can be efficiently performed on large, complex schemas allowing simpler access to databases through keyword search or conceptual query languages. AutoJoin also provides programmers with a tool
to iteratively create SQL queries without requiring explicit knowledge of the structure of a database.
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