Date of Award
Doctor of Philosophy (PhD)
Michele C. Weigle
Michael L. Nelson
Justin F. Brunelle
Dimitrie C. Popescu
Web archives preserve the live Web for posterity, but the content on the Web one cares about may not be preserved. The ability to access this content in the future requires the assurance that those sites will continue to exist on the Web until the content is requested and that the content will remain accessible. It is ultimately the responsibility of the individual to preserve this content, but attempting to replay personally preserved pages segregates archived pages by individuals and organizations of personal, private, and public Web content. This is misrepresentative of the Web as it was. While the Memento Framework may be used for inter-archive aggregation, no dynamics exist for the special consideration needed for the contents of these personal and private captures.
In this work we introduce a framework for aggregating private and public Web archives. We introduce three "mementities" that serve the roles of the aforementioned aggregation, access control to personal Web archives, and negotiation of Web archives in dimensions beyond time, inclusive of the dimension of privacy. These three mementities serve as the foundation of the Mementity Framework. We investigate the difficulties and dynamics of preserving, replaying, aggregating, propagating, and collaborating with live Web captures of personal and private content. We offer a systematic solution to these outstanding issues through the application of the framework. We ensure the framework's applicability beyond the use cases we describe as well as the extensibility of reusing the mementities for currently unforeseen access patterns. We evaluate the framework by justifying the mementity design decisions, formulaically abstracting the anticipated temporal and spatial costs, and providing reference implementations, usage, and examples for the framework.
Kelly, Matthew R..
"Aggregating Private and Public Web Archives Using the Mementity Framework"
(2019). Doctor of Philosophy (PhD), dissertation, Computer Science, Old Dominion University, DOI: 10.25777/1t52-wz02