Project Description

  • Start date: 01/10/2018

  • End date: 30/04/2020

  • Duration (months): 30

  • Total budget: € 9.830.000

  • Lab Budget: € 1.300.000

  • Number of partners: 9

  • Partners: Università del Salento, Università di Palermo, CNR, CEFRIEL, TERA, EKA srl, CALEF, GE Avio srl, SACMI.

OK-INSAID proposes scientific, technological, and application innovation in Industrial Data Analytics to help in the redesign of actual manufacturing networks and processes by leveraging data and analytics to achieve a step change in value creation, by transforming existing manufacturing processes and business models. It will integrate and demonstrate the potential of Big Data technologies to deliver new digital services in the industrial sector. OK-INSAID proposes a novel approach to industrial analytics based on coordination, synchronization, and collaboration among analytics in cloud and at the edge. The approach will be supported by a reference architecture and a reference implementation to adopt in order to develop novel hybrid cloud-edge industrial analytics for Industry4.0.

  • Novel models and methods for industrial data ingestion and integration from many different heterogeneous sources to create enterprise-level industrial data spaces;
  • Novel algorithms and data science methods for generating value and operational knowledge from Industrial Big Data coming from the above-mentioned sources. The focus will be on (near) real-time analytics and stream processing in order to mainly support operations;
  • Novel industrial analytics services, by integrating the developed algorithms into applications that exploit the distributed data processing and analytics model and the potential and the information value of enterprise-level industrial data spaces;
  • Advanced methods for industrial data security, in order to assess possible vulnerabilities and implement proper protection measures and counter- measures on industrial data;
  • Advanced data visualization methods for providing the insights, value, and operational knowledge extracted from data available to relevant users and stakeholders, including novel user interfaces for wearable, mobile personal devices, augmented/virtual reality, etc.
  • Identification and assessment of dark data in asset-intensive industries;
  • New industrial and business scenarios based on industrial analytics;
  • Analysis of requirements and use case definition for industrial analytics;
  • Business models for Industry 4.0 based on industrial analytics;
  • Methodologies for Industrial analytics adoption;
  • Novel models and tools for industrial data ingestion and integration;
  • Big Data infrastructure for industrial edge-cloud analytics;
  • Define of descriptive, diagnostic, predictive, and prescriptive industrial analytics;
  • Define of Industry data privacy;
  • Define and implementations of chatbot for industrial applications and Augmented data discovery;
  • Applications of Big Data enabled PLM.

For more information please contact: Mariangela Lazoi (