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Wu consider tho problem of estimating CPU (distance computntlons) nnd I/O costs for processing range and k-nearest neighbors qucrics over metric spaces. Unlike the specific case of vector spaces, where information on data distribution has been exploited to derive cost models for predicting the performanco of multi-dimensional access methods, in a generic metric space there is no such a possibility, which makes the problem quite different and requires a novel approach. We insist that the distance distribution of objects can be profitably used to solve the problem, and consequently develop a concrete cost model for the M-tree access method [lo]. Our results rely on the assumption that the indexed dataset comes from a metric space which is "homogeneous" enough (in a probabilistic sense) to allow reliable cost estimations even if the distance distribution with respect to a specific query object is unknown. We experimentally validate the modol ovor both real and synthetic datasets, and show how the model can be used to tune the M-tree in order to minimlzo a combination of CPU and I/O costs. Finally, we sketch how the same approach can be applied to derive a cost model for the up-tree index structure [8].

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The signature file access method has proved to be a convenient indexing technique, in particular for text data Because it can deal with unformatted data, many application domains have shown interest in signature file techniques, e.g., office information systems, statistical and logic databases. We argue that multimedia databases should also take advantage of this method, provided convenient storage structures for organizing signature tiles are available, Our main concern here is the dynamic organization of signatures based on a partitioning paradigm called Quick Filter. A signature file is partitioned by a hashing function and the partitions are orgamzed by linear hashing, Thorough performance evaluation of the new scheme is provided, and it is compared with single-level and multdevel storage structures Results show that quick filter is economical in space and very convenient for applications dealing with large files of dynamic data, and where user queries result in signatures with high weights. These characteristics are particularly interesting for multimedia databases, where integrated access to attributes, text and images must be provided.