Abstract In our data-deluge era, massive chunks of information, perpetually collected by pervasive sensors, are communicated and processed by distributed computational architectures. To address emergent big-data computational issues, this project embarks on an ambitious multidisciplinary research effort that aims at advancing the state-of-the-art in-network/distributed big-data processing via a general algorithmic framework for data analytics over massively distributed data sets. The proposed algorithmic framework enables fully distributed and parallel big-data analytics, for a variety of heterogeneous data sets over a wide range of computational architectures. The developed research directions are beneficial also to domains far beyond big-data analytics, such as signal processing, machine learning, next-generation wireless communications, smart-city and smart-grid networks. Research results are distributed through archival publications, courses, undergraduate research opportunities, tutorials and conference presentations.

The developed scheme relies on a novel convexification/decomposition technique which accommodates a rich class of non-convex, unstructured and stochastic optimization tasks with non-separable objective functions. Algorithms are designed for settings where data are distributed across a large number of multi-core computational nodes, within a network of arbitrary topology with (possibly) time-varying and even random links. This new class of algorithms addresses shortcomings of current (non-parallel and non-distributed) convexification techniques via (i) full control of the degree of parallelism and distribution of the computation/signaling among processors/network nodes, and (ii) by offering a plethora of convex approximants, regularization terms, step-size rules, and communication protocols. Designed for time-varying or even random network topologies, the advocated framework demonstrates also another desirable attribute for distributed computations: resiliency to (random) network failures.