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ABIDI

ABIDI

ABIDI - Context-aware and Veracious Big Data Analytics for Industrial IoT

Industry’s transition to the digital world has already reaped benefits such as fully automated processes and cost savings and has paved the way for novel services and business models.  However, the challenges of data-driven autonomous and context-aware systems that enable the automated processes in industry have not been fully addressed. ABIDI project contributes to the automated industrial processes with the development of a framework for context-aware and veracious big data analytics with automated knowledge discovery and reasoning for industrial IoT. ABIDI aims building reliable industrial IoT networks, automating the processing of big variety, volume, and velocity industrial IoT data streams, and offering big data insights with centralized or distributed solutions. Therefore, ABIDI develops adaptive models-based decision support and recommendation tools that enable the automated control of the system with processing big industrial IoT data. To materialize the research results into innovations that industrial operators and service providers can utilize, we design four real-life use cases with respect to energy consumption prediction, including two academic use cases in CeDInt-UPM (Spain) and HES-SO (Switzerland), and two industry use cases. Energy efficiency is one of the KPIs for any industry regardless of line of business.

ABIDI addresses key challenges of reliable industrial IoT networks, big data analytics, edge computing, contextualization, veracity and knowledge discovery, which are fundamental issues for industrial IoT and big data analytics to be successful in industry. The project carries out fundamental research and the technical results will support a more principled transition towards fully automated processes realized by autonomous and context-aware devices. Moreover, ABIDI applies the technical results in the context of realistic industry use cases and performs systematic studies with professional experts about the usability and reliability of the produced technologies.