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The Semantic Web is a distributed model for publishing, utilizing, and extending information. This model, in combination with several enabling technologies – Resource Description Framework (RDF), RDF Schema (RDF-S), Web Ontology Language (OWL), the SPARQL Query Language, and Linked Open Data, has led to the existence of many online data sources whose contents can be “mashed-up”, i.e. queried and integrated on-the-fly, to produce web applications. However, there is a lack of open environments to perform such seamless integrations while preserving appropriate security and privacy policies (DIG, 2009; Langegger et. al, 2007).
Several issues need to be addressed before such environments are developed. For instance, an application developer/user would need to know what data is present in each source and in which format so that appropriate queries may be formulated to obtain the necessary information. Moreover, if a source’s content has been modified, the application developer need to adjust the query accordingly to ensure proper mash-ups. 
One possible solution to address these challenges is the federation paradigm for database systems (McLeod & Heimbigner, 1980). A federated query involves the concurrent search of multiple distributed data sources. In a federation model, the access to data sources is transparent. Under such a system, a user can submit a single query to the federation engine, which then formulates the query to suit the various data sources, and then returns the merged result. The lack of a shared model for security and privacy (DIG, 2009), however, impedes the transparency of the federated model as the federation engine is unable to execute requests across many domains on the fly. This means that most contemporary federations have static, previously negotiated policies in place, which prevents dynamic integration of data. 

\section{Thesis Overview}

In this thesis, I describe my proposed distributed propagation and
provenance system as follows:

Chapter~\ref{ch:background} introduces the concepts of data
propagation and the Representational State Transfer architectural
design.

Chapter~\ref{ch:design} details the design choices that influenced my
implementation of distributed propagation.

Chapter~\ref{ch:provenance} discusses how we may to extend existing
propagator networks to handle provenance.

Chapter~\ref{ch:issues} offers a discussion of some potential issues
with distributed propagation and provenance in the system I have
described.  I also discuss how I resolve these issues.

Chapter~\ref{ch:related} relates this work to other work relating to
data synchronization and distributed provenance.

Chapter~\ref{ch:summary} provides a summary of the
contributions of this work, as well as potential future work that may
be used to expand the distributed propagation system proposed here.

Finally, Appendix~\ref{ch:dprop} offers a more detailed description of
my implementation of distributed propagation and provenance, DProp.

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