The information explosion propelled by the advent of online social media, the Internet, and the global-scale communications has rendered statistical learning from Big Data increasingly important. At any given time around the globe, large volumes of data are generated by today’s ubiquitous communication, imaging, and mobile devices such as cell-phones, surveillance cameras, medical and e-commerce platforms, as well as social-networking sites. While Big Data can be definitely perceived as a big blessing, big challenges also arise with large-scale datasets. The sheer volume of data makes it often impossible to run analytics using a central processor and storage, and distributed processing with parallelized multi-processors is preferred while the data themselves are stored in the cloud. As many sources continuously generate data in real time, analytics must often be performed “on-the-fly” and without an opportunity to revisit past entries. Due to their disparate origins, the resultant datasets are often incomplete and include a sizable portion of missing entries. In addition, massive datasets are noisy, prone to outliers, and vulnerable to cyber-attacks. Given these challenges, ample signal processing opportunities arise. This special issue seeks to provide a venue for ongoing research in novel models applicable to a wide range of Big Data analytics problems, as well as data-adaptive algorithms and architectures to handle the practical challenges, while revealing fundamental limits and insights on the mathematical trade-offs involved.
Edited by: Gonzalo Mateos, Konstantinos Slavakis, Zhi Tian, Jean-Christophe Pesquet and Gesualdo Scutari