Statistical learning techniques for costing XML queries
Ning Zhang, Peter J. Haas, Vanja Josifovski, Guy M. Lohman, Chun Zhang · 2005
Developing cost models for query optimization is significantly harder for XML queries than for traditional relational queries. The reason is that XML query operators are much more complex than relational operators such as table scans and joins. In this paper, we propose a new approach, called Comet, to modeling the cost of XML operators; to our knowledge, Comet is the first method ever proposed for addressing the XML query costing problem. As in relational cost estimation, Comet exploits a set of system catalog statistics that summarizes the XML data; the set of "simple path" statistics that we propose is new, and is well suited to the XML setting.