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Ph.D. Theses

Toward Energy-Aware Mobile Reasoning Agents for the Mobile Semantic Web

By Evan Patton
Advisor: Deborah L. McGuinness
June 20, 2016

Over the past decade there has been an uptake of semantic technologies on mobile devices. The hardness of semantic representation languages, such as OWL 2 DL's 2NEXPTIME upper bound, coupled with device and user constraints requires means of controlling expectation with respect to time, energy, and power use. In this talk, I present a hardware-based methodology for measuring for an Android smartphone the energy and power costs associated with the task of instance realization in OWL 2 knowledge bases across a number of OWL 2 reasoners of differing complexity. These findings are used to develop knowledge base metrics and predictive models that can be used to decide whether local or remote reasoning is a more efficient use of resources based on the available hardware. This is culminated into a framework called MEAR, the Mobile Energy-Aware Reasoner framework, and I show how predictive models for an OWL 2 RL reasoner built on this framework significantly decreases runtime, energy, and power consumption in the median case.

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