The Gander search engine for personalized networked spaces
Jonas Michel · Texas ScholarWorks (Texas Digital Library) · 2012
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 network spaces (PNetS) and the requisite support for general-purpose expressive spa-tiotemporal 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. We present 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. We evaluate Gander through a user study that examines the perceived usability and utility of our mobile application, and we benchmark the performance of Gander in large PNetS through network simulation.