Triggers over Nested Views of Relational Data.

Uložené v:
Podrobná bibliografia
Názov: Triggers over Nested Views of Relational Data.
Autori: Shao, Feng1 fshao@cs.cornell.edu, Novak, Antal2 afn@stanford.edu, Shanmugasundaram, Jayavel1 jai@cs.cornell.edu
Zdroj: ACM Transactions on Database Systems. Sep2006, Vol. 31 Issue 3, p921-967. 47p.
Predmety: *XML (Extensible Markup Language), *SQL, *RELATIONAL databases, *INFORMATION storage & retrieval systems, *MIDDLEWARE, *COMPUTER systems, *DATABASES
Abstrakt: Current systems that publish relational data as nested (XML) views are passive in the sense that they can only respond to user-initiated queries over the nested views. In this article, we propose an active system whereby users can place triggers on (unmaterialized) nested views of relational data. In this architecture, we present scalable and efficient techniques for processing triggers overnested views by leveraging existing support for SQL triggers over flat relations in commercial relational databases. We have implemented our proposed techniques in the context of the Quark XML middleware system. Our performance results indicate that our proposed techniques are a feasible approach to supporting triggers over nested views of relational data. [ABSTRACT FROM AUTHOR]
Databáza: Academic Search Index
Popis
Abstrakt:Current systems that publish relational data as nested (XML) views are passive in the sense that they can only respond to user-initiated queries over the nested views. In this article, we propose an active system whereby users can place triggers on (unmaterialized) nested views of relational data. In this architecture, we present scalable and efficient techniques for processing triggers overnested views by leveraging existing support for SQL triggers over flat relations in commercial relational databases. We have implemented our proposed techniques in the context of the Quark XML middleware system. Our performance results indicate that our proposed techniques are a feasible approach to supporting triggers over nested views of relational data. [ABSTRACT FROM AUTHOR]
ISSN:03625915
DOI:10.1145/1166074.1166080