Visible to the public A Demo of a Software Platform for Ubiquitous Big Data Engineering, Visualization, and Analytics, via Reconfigurable Micro-Services, in Smart Factories

TitleA Demo of a Software Platform for Ubiquitous Big Data Engineering, Visualization, and Analytics, via Reconfigurable Micro-Services, in Smart Factories
Publication TypeConference Paper
Year of Publication2022
AuthorsSoderi, Mirco, Kamath, Vignesh, Breslin, John G.
Conference Name2022 IEEE International Conference on Smart Computing (SMARTCOMP)
KeywordsAPI, Big Data, big data security in the cloud, Biological system modeling, cloud, cloud computing, Computational modeling, data analytics, Data engineering, Data visualization, edge, fog, intelligent manufacturing, Metrics, Micro-service, Predictive models, pubcrawl, Reconfigurable Manufacturing, remote monitoring, resilience, Resiliency, Scalability, smart manufacturing, Software, software platform
AbstractIntelligent, smart, Cloud, reconfigurable manufac-turing, and remote monitoring, all intersect in modern industry and mark the path toward more efficient, effective, and sustain-able factories. Many obstacles are found along the path, including legacy machineries and technologies, security issues, and software that is often hard, slow, and expensive to adapt to face unforeseen challenges and needs in this fast-changing ecosystem. Light-weight, portable, loosely coupled, easily monitored, variegated software components, supporting Edge, Fog and Cloud computing, that can be (re)created, (re)configured and operated from remote through Web requests in a matter of milliseconds, and that rely on libraries of ready-to-use tasks also extendable from remote through sub-second Web requests, constitute a fertile technological ground on top of which fourth-generation industries can be built. In this demo it will be shown how starting from a completely virgin Docker Engine, it is possible to build, configure, destroy, rebuild, operate, exclusively from remote, exclusively via API calls, computation networks that are capable to (i) raise alerts based on configured thresholds or trained ML models, (ii) transform Big Data streams, (iii) produce and persist Big Datasets on the Cloud, (iv) train and persist ML models on the Cloud, (v) use trained models for one-shot or stream predictions, (vi) produce tabular visualizations, line plots, pie charts, histograms, at real-time, from Big Data streams. Also, it will be shown how easily such computation networks can be upgraded with new functionalities at real-time, from remote, via API calls.
NotesISSN: 2693-8340
DOI10.1109/SMARTCOMP55677.2022.00041
Citation Keysoderi_demo_2022