1. A method for estimating a static execution time of a database query, said method comprising: building a prediction of query runtime tree from historical query information at a computer system, said building comprising utilizing 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 execution time ranges; receiving a database query at said computer system; and utilizing said prediction of query runtime tree to estimate said static execution time of said database query at said computer system said utilizing comprising traversing said prediction of query runtime tree on the basis of at least one query feature associated with said database query, such that a time range estimate of said static execution time is associated with said database query based upon a similarity of said at least one query feature to a historical query feature shared by a grouping of historical database queries.
1. A method for estimating a static execution time of a database query, said method comprising: building a prediction of query runtime tree from historical query information at a computer system, said building comprising utilizing 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 execution time ranges; receiving a database query at said computer system; and utilizing said prediction of query runtime tree to estimate said static execution time of said database query at said computer system said utilizing comprising traversing said prediction of query runtime tree on the basis of at least one query feature associated with said database query, such that a time range estimate of said static execution time is associated with said database query based upon a similarity of said at least one query feature to a historical query feature shared by a grouping of historical database queries. 4. The method as recited in claim 1 , wherein said building a prediction of query runtime tree from historical query information comprises: grouping an historical database query of said historical query information into an element of a binary decision tree based upon an execution time of said historical database query; and terminating construction of said element based upon a pre-defined termination condition.