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.