A Model-Driven Framework for Meta-Data Harmonization in Business Intelligence

Datasets obtained from different sources are often heterogeneous: they do not share a common internal structure, even though they are nominally about the same subject matter. This makes reporting against datasets difficult without laborious, manual efforts to clean and transform the data. This project will investigate the feasibility of an approach to abstract the harvesting of metadata from multiple content generated Business Intelligence (BI) reporting systems. The interns will leverage previous work on model-driven engineering tools and meta-data harmonization to create a generic application framework for systematic, automated, meta-data harmonization to support business intelligence reporting. This will allow business analysts and researchers to flexibly define BI reports that integrate data from many different data sources in a much more efficient manner. This will enable Mark 3 Research Inc. to develop and market new and improved tools and products for this market space.

Faculty Supervisor:

Liam Peyton


Miguel Garzon


Mark 3 Research Inc


Engineering - computer / electrical


Information and communications technologies


University of Ottawa



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