The rise of new lightweight and cheap sensors has opened the door wide for new sensing applications. Mobile opportunistic sensing is one type of these applications which has been adopted in multiple citizen science projects including air pollution monitoring. However, the opportunistic nature of sensing along with campaigns being mobile and sensors being subjected to noise and missing values leads to asynchronous and unclean data. Analyzing this type of data requires cumbersome and time-consuming preprocessing. In this paper, we introduce a novel framework to treat such type of data by seeing data as functions rather than vectors. The framework introduces a new data representation model along with a high-level query language and an analysis module. Copyright