Date of Award
Doctor of Philosophy
Carl K. Chang
In the era of Internet of Things (IoT), it is vital for smart environments to be able
to efficiently provide effective predictions of user’s situations and take actions in a
proactive manner to achieve the highest performance. However, there are two main
challenges. First, the sensor environment is equipped with a heterogeneous set of data
sources including hardware and software sensors, and oftentimes complex humans as
sensors, too. These sensors generate a huge amount of raw data. In order to extract
knowledge and do predictive analysis, it is necessary that the raw sensor data be cleaned,
understood, analyzed, and interpreted. Second challenge refers to predictive modeling.
Traditional predictive models predict situations that are likely to happen in the near future
by keeping and analyzing the history of past user’s situations. Traditional predictive
analysis approaches have become less effective because of the massive amount of data
that both affects data processing efficiency and complicates the data semantics. In this
study, we propose a data-driven, situation-aware framework for predictive analysis in
smart environments that addresses the above challenges.
Gholami, Hoda, "A data-driven situation-aware framework for predictive analysis in smart environments" (2018). Graduate Theses and Dissertations. 16356.