Patterns are concise, but rich in semantic, representation of data (data mining results, multimedia features, Web structural information, etc.). Only few approaches exist to deal with heterogeneous patterns, even if this is an hot topic in most data-intensive and distributed applications. The aim of this thesis is to investigate the potentiality of pattern-based management. In particular, we plan to define sufficiently expressive pattern data models and languages to cope with different types of patterns. Besides, investigation of architectural issues concerning the usage of the proposed model and languages in centralized and advanced distributed environments will be exploited.
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