  This paper presents the Cascaded Metric Tree (CMT) for efficient satisfaction
of metric search queries over a dataset of N objects. It provides extra
information that permits query algorithms to exploit all distance calculations
performed along each path in the tree for pruning purposes. In addition to
improving standard metric range (ball) query algorithms, we present a new
algorithm for exploiting the CMT cascaded information to achieve near-optimal
performance for k-nearest neighbor (kNN) queries. We demonstrate the
performance advantage of CMT over classical metric search structures on
synthetic datasets of up to 10 million objects and on the 564K Swiss-Prot
protein sequence dataset containing over $200$ million amino acids. As a
supplement to the paper, we provide reference implementations of the
empirically-examined algorithms to encourage improvements and further
applications of CMT to practical scientific and engineering problems
