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

Kuru, Kaya orcid iconORCID: 0000-0002-4279-4166 (2021) Management of geo-distributed intelligence: Deep Insight as a Service (DINSaaS) on Forged Cloud Platforms (FCP). Journal of Parallel and Distributed Computing, 149 . pp. 103-118. ISSN 0743-7315

[thumbnail of Author Accepted Manuscript]
PDF (Author Accepted Manuscript) - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.


Official URL:


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. 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 revolutionising the industry and businesses in the transition from digital to the intelligent way of doing business with globally generated distributed intelligence. 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 geo-distributed architectural framework is proposed to 1) guide the national/international leading organisations, governments, cloud service providers and leading companies in order to establish an environment in which exponentially increasing voluminous big data can be harvested effectively and efficiently, 2) inspire the transformation of big data into wiser abstract formats in Specialised Insight Domains (SIDs) in order to help make better decision making and near-real-time predictions, especially for applications requiring low-latency, and 3) 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 specialised domains within geo-distributed data-centres located in geo-distributed cloud platforms.

Repository Staff Only: item control page