.png){kind=icon}
Return
Dependable cardinality forecasts for XQuery
.png){kind=icon}
PRE
AI
DOI：10.14778/1453856.1453908.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Though inevitable for effective cost-based query rewriting, the derivation of meaningful cardinality estimates has remained a notoriously hard problem in the context of XQuery. By basing the estimation on a relational representation of the XQuery syntax, we show how existing cardinality estimation techniques for XPath and proven relational estimation machinery can play together to yield dependable forecasts for arbitrary XQuery (sub)expressions. Our approach benefits from a light-weight form of data flow analysis.
Abstract domain identifiers
guide our query analyzer through the estimation process and allow for informed decisions even in case of deeply nested XQuery expressions. A variant of
projection paths
[15] provides a versatile interface into which existing techniques for XPath cardinality estimation can be plugged in seamlessly. We demonstrate an implementation of this interface based on
data guides.
Experiments show how our approach can equally cope with both, structure-and value-based queries. It is robust with respect to intermediate estimation errors, from which we typically found our implementation to recover gracefully.
Journal
No journal information available
Organization
No organization information available
Cited Papers
No cited papers available