Big Data Series Analytics in the Context of Environmental Crowd Sensing

The new mobile crowd sensing (MCS) paradigm leads to the generation of a large amount of data originated from different sources. The inhomogeneous nature of produced data turns the process of data analytics and knowledge extraction extremely challenging and complicated. More specifically, mining such data needs special care and processing. In this paper, we focus on the analysis, processing, and exploration of such data. We advocate this process should benefit from the diverse data sources in the MCS context, including user's annotation and various ambient data collected through sensors.