11. The method as recited in claim 10 wherein said determining feature vectors for a plurality of historical database queries from said group of historical database queries further comprises: extracting historical query features from an historical database query of said plurality of historical database queries; and utilizing a plurality of said historical query features to develop a query plan vector for said historical query.
10. The method of claim 1 , wherein said accessing a prediction of query runtime tree built from historical query information regarding historical database queries previously executed on said database comprises: accessing a prediction of query runtime tree built by a method comprising: receiving historical query information regarding a group of historical database queries previously executed on a loaded database; determining feature vectors for a plurality of said historical database queries, such that said feature vectors comprise load attributes related to the impact of database load upon said plurality of historical database queries; using machine learning to build said prediction of query runtime tree, such that node elements and leaf elements of said prediction of query runtime tree correspond to query execution runtime ranges associated with sets of said plurality of historical database queries; and developing a classifier function for a node element of said prediction of query runtime tree, said classifier function developed from at least one attribute of feature vectors of a set of said plurality of historical database queries associated with said node, said classifier function configured for selecting a branching path for processing a database query through said prediction of query runtime tree to estimate a loaded execution runtime of said database query.