Data Mining and Reverse Engineering

Today's database engineers are committed to the reuse of existing data, for performance and economic reasons. Moreover, they often have to complement enterprise data with data from external sources, where the corresponding semantics are rarely fully available. Accessing data via the Web is just an example of input from external autonomous repositories. Unfortunately, some database design processes neglect to properly record the initial specifications - because most of the intended semantics are not available anymore. As a consequence, both database researchers and practitioners are facing the issues involved in the discovery and understanding of the semantics hidden in whatever is available: data sets, data stores formats, database schemes, application programs, documentation, metadaa, forms and interviews. This volume focuses on the above issues from the perspective of database semantics, discussing theories, principles, models, methods, formalisms experiences, tools and prototypes for recovering, representing and organizing semantic information on application data from any possible source.This includes in particular the processes known as: data mining and knowledge acquisition/discovery; data reverse engineering; extraction of semantic information from programs; semantic elicitation and visualization; cooperative elaboration of semantics; automatic indexing, classification and clustering; languages and interfaces for data mining or reverse engineering; advanced query models; visual mining; and correspondence investigation in schema integration. This work should be of interest to computer professionals; academic researchers in the area of database management, information retrieval and information systems; and R&D students in computer science.