A Query-sensitive Cost Model for Similarity Queries with M-tree

We introduce a cost model for the M-tree access method [Ciaccia et al., 1997] which provides estimates of CPU (distance computations) and I/O costs for the execution of similarity queries as a function of each single query. This model is said to be query-sensitive, since it takes into account, by relying on the novel notion of “witness”, the “position” of the query point inside the metric space indexed by the M-tree. We describe the basic concepts underlying the model along with different methods which can be used for its implementation; finally, we experimentally validate the model over both real and synthetic datasets.

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