Management of geo-distributed intelligence: Deep Insight as a Service (DINSaaS) on Forged Cloud Platforms (FCP)

The recent advances in the cyber-physical domains, cloud and edge platforms along with the advanced communication technologies play a crucial role in connecting the globe more than ever, which is creating large volumes of data at astonishing rates and a tsunami of computation within hyper-connectivity. Data analytic tools are evolving rapidly to harvest these explosive increasing data volumes. Deriving meaningful insights from voluminous geo-distributed data of all kinds as a strategic asset is fuelling the innovation, facilitating e-commerce and revolutionizing the industry and businesses in the transition from digital to the intelligent way of doing business. In this perspective, in this study, a philosophical industrial and technological direction involving Deep Insight-as-a-Service (DINSaaS) on Forged Cloud Platforms (FCP) along with Advanced Insight Analytics (AIA), primarily motivated by the global benefit is systematically analysed within sophisticated theoretical knowledge, and consequently, a conceptual geo-distributed framework is proposed to (1) guide the national/international leading organizations, governments, cloud service providers and leading companies in order to establish a scalable framework within the hyperscale geo-distributed infrastructure in which exponentially increasing voluminous Big Data (BD) can be harvested effectively and efficiently, (2) inspire the transformation of BD into wiser abstract formats in Specialized Insight Domains (SID), (3) provide fusion and networking of insights rather than BD in order to obtain globally generated distributed intelligence and help make better decisions and near-real-time predictions, in particular for time-critical latency-sensitive applications, and (4) direct all the stakeholders to rivet the high-quality products and services within Automation of Everything (AoE) by exploiting continuously created and updated insights in dedicated taxonomic SID within large-scale geo-distributed datacenters. (C) 2020 Elsevier Inc. All rights reserved.