SIBILLA project

Industry 4.0, Big Data Analytics, Machine Learning and IOT

Summary of the project

 

Created by a team of 4 companies, including FabricaLab, with the aim of researching, designing and developing a Business Intelligence Systema for Industry 4.0 Companies, with collaboration, automatic interaction, Big Data Analytics and machine learning functions to extract knowledge carrying out predictive analysis integrating Big Data acquired from the Web and from Internet of Things architectures.

The management and enhancement of Big Data is one of the most important technological challenges that companies face in the context of industry 4.0 to make their processes more effective and efficient.
The current systems of Business Intelligence (BI) and Corporate Performance Management (CPM) do not fully meet the need to adapt the scope of BI by innovating it towards integration with solutions and technologies typical of Industry 4.0, how to use advanced features of data mining and machine learning to extract knowledge from large amounts of data, whether they are held in company information systems, collected from the Web or acquired through IoT architectures.
The consortium of companies established for the execution of the project, involves FabricaLab S.r.L. as leader in addition to the companies BSD, TomorrowData and University of Pisa. SIBILLA aims to overcome the critical issues of current CPM / BI systems by creating the prototype of a BI system that implements typical technologies of industry 4.0 and is able to extract, from Big Data coming from heterogeneous sources, even if exogenous , information useful for carrying out predictive analyzes and optimizing the management of business processes. Within the project will be developed software modules with web crawling functionality, data pre-processing, text analysis and Big Data mining, able to realize sentiment analysis and opinion mining on data collected from the web. Furthermore, a solution will be implemented to exploit, in a predictive key, the measurements of physical quantities performed by IoT architectures.

Particular attention will be paid to collaboration and automatic interaction functions, through an innovative graphic interface and mobile app that simplify the interaction with the system.

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