The proliferation of large scale smart multi-agent systems, also known as Internet-of-Things, Networked Control Systems, Wireless sensor and actuator networks, Cyber-physical Systems, etc., are providing us with a wealth of data with unprecedented time-space resolution which can trigger the next technological revolution. However, this trend is also posing a formidable challenge, often referred as Data Tsunami, which requires the analysis of a large-scale correlated time-series. In this talk,  the problem of estimating a map will be addressed, first in a static scenario and later in a dynamic scenario, based on noisy measurements collected by a large number of sensors in the presence of unreliable communication. In particular, he will explore the pros and cons of parametric and non-parametric estimation approaches and will propose some strategies that aim to merge ideas from control theory such as Gauss-Markov estimators and Kalman Filtering, and from Machine Learning such as Gaussian regression, Karhunen-Loève kernel expansions and Nystrom method.