M2RML: Mapping Multidimensional Data to RDF

In this thesis we provide a mapping language that facilitates mapping of multidimensional data to RDF datasets using Data Cube Vocabulary (DCV), W3C candidate for publishing statistical data on the web in RDF format. RDF is W3C standard for data interchange which allows data, structured and semi-structured with different underlying schemas to be mixed, exposed and shared among different applications.The language design is similar to recently published R2RML used for mapping relational datasets to RDF datasets having similar core elements. As we design the mapping language general enough to work with various data sources of statistical data we also provide a framework specific to publishing OLAP cubes based on our mapping language. In this framework we address selective issues of DCV and propose an extension which improves DCV for representing multidimensional data and allows one-to-one mapping between an OLAP cube and RDF/QB elements.