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 object, relational, XML and Cobol, while each paradigm offers many facets and many versions. Within object-oriented dataprocessing applications, each external or internal data model has the tendency to imply a horde of dependent data models and object models (also: views, wrappers, APIs, etc.) that are needed for specific data-processing components (such as GUI binding, report generation and serialization) or for architectural reasons (such as loose coupling, independent evolution, performance and API conformance). This myriad of interand intra-paradigm data models calls for a good understanding of techniques for mappings between data models, actual data, and operations on data. This article offers a survey on mapping scenarios, mapping techniques, recurring mapping problems, and open research challenges.