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With the advent of the World Wide Web, the amount of digital
information in the world has increased exponentially. The ability to organize this
deluge of data, retrieve it, and combine it with other data would bring
numerous benefits to organizations that rely on the analysis of this
data for their operations. The Semantic Web encompasses various
technologies that support better information organization and
access. 

This thesis proposes a data federation engine that
facilitates integration of data across distributed Semantic Web data sources, while
maintaining appropriate access policies. After discussing existing
literature in the field, the design and implementation of the system,
including its capabilities and limitations, are thoroughly described. 
Moreover, a possible application of the system at the
Massachusetts Department of Education is explored in detail, including
an investigation of the technical and non-technical challenges
associated with its adoption at a government agency. 

By using the federation engine, users would be able to exploit the
expressivity of the Semantic Web by querying for disparate data at a
single location without having to know how it is distributed or where
it is stored. Among this research's contributions to the fledgling
Semantic Web are: an integrated system for executing SPARQL queries;
and, an optimizer that faciliates efficient querying by exploiting
statistical information about the data sources.
