The vision of pervasive computing is one of a personalized space populated with vast amounts of data that can be exploited by humans. Such Personalized Networked Spaces (PNetS) and the requisite support for general-purpose expressive spatiotemporal search of the “here” and “now” have eluded realization, due primarily to the complexities of indexing, storing, and retrieving relevant information within a vast collection of highly ephemeral data. This thesis presents the Gander search engine, founded on a novel conceptual model of search in PNetS and targeted for environments characterized by large volumes of highly transient data. We overview this model and provide a realization of it via the architecture and implementation of the Gander search engine. Gander connects formal notions of sampling a search space to expressive, spatiotemporal-aware protocols that perform distributed query processing in situ. This thesis evaluates Gander through a user study that examines the perceived usability and utility of our mobile application, and benchmarks the performance of Gander in large PNetS through network simulation. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/19714 |
| Date | 05 March 2013 |
| Creators | Michel, Jonas Reinhardt |
| Source Sets | University of Texas |
| Language | en_US |
| Detected Language | English |
| Format | application/pdf |
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