Mappings Make Data Processing Go 'Round

Whatever programming paradigm for data processing we choose, data has the tendency to live on the other side or to eventually end up there. The major paradigms for data processing are Cobol, object, relational and XML; each paradigm offers many facets and many versions; each paradigm provides specific forms of data models (object models, relational schemas, XML schemas, etc.). Each data-processing application depends on a horde of interrelated data models and artifacts that are derived from data models (such as data-access layers). Such conglomerations of data models are challenging due to paradigmatic impedance mismatches, performance requirements, loose-coupling requirements, and others. This ubiquitous problem calls for a good understanding of techniques for mappings between data models, actual data, and operations on data. This tutorial lists and discusses mapping scenarios, mapping techniques, impedance mismatches and research challenges regarding mappings.

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