Signal Processing for Big Data [From the Guest Editors]
Georgios B. Giannakis, Francis Bach, Raphael Cendrillon, Michael Sean Mahoney, Jennifer Neville · IEEE Signal Processing Magazine · 2014
The articles in this special section delineate the theoretical and algorithmic underpinnings along with the relevance of signal processing tools to the emerging field of big data and introduce readers to the challenges and opportunities for SP research on (massive-scale) data analytics. The latter entails an extended and continuously refined technological wish list, which is envisioned to encompass high-dimensional, decentralized, parallel, online, and robust statistical signal processing as well as large, distributed, fault-tolerant, and intelligent systems engineering. The goal is to selectively sample a diverse gamut of big data challenges and opportunities through surveys of methodological advances, as well as more focused- and application-oriented contributions chosen on the basis of timeliness, importance, and relevance to signal processing.