Mobile crowdsensing (MCS) has emerged as a new revolutionary paradigm to collect large-scale data by the crowd. However, there is a lack of a holistic system than can provide an integrated design for this large volumes data coming from different sensors, and afford analytics capabilities. This paper provides an envision of a micro-service based architecture for implementing and automating ETL data management pipelines in MCS.

Wide spread use of sensors and mobile devices along with the new paradigm of Mobile Crowd-Sensing (MCS), allows monitoring air pollution in urban areas. Several measurements are collected, such as Particulate Matters, Nitrogen dioxide, and others. Mining the context of MCS data in such domains is a key factor for identifying the individuals’ exposure to air pollution, but it is challenging due to the lack or the weakness of predictors. We have previously developed a multi-view learning approach which learns the context solely from the sensor measurements. In this demonstration, we propose a visualization tool (COMIC) showing the different recognized contexts using an improved version of our algorithm. We also demonstrate the change points detected by a multi-dimensional CPD model. We leverage real data from a MCS campaign, and compare different methods.

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.