The overall objective of DATAWiSE is to develop and test building and building portfolio management tools, leveraging cross-sectoral lifecycle data, on the basis of an open, secure, interoperable, and scalable framework, to maximize both energy efficiency and stakeholder value. Utilizing cutting-edge artificial intelligence and advanced analytics, the methodology integrates data from a variety of sources to provide a holistic understanding of building operations. Besides technological innovation, the project also incorporates a supportive market and policy framework designed to offer evidence-based pathways for widespread adoption and commercialization. DATAWiSE aims to provide a standardized approach for managing and exploiting building-related big data.

MKLab is the coordinator of DATAWise project. It is also responsible for the scientific and technical management of the project, leading the related WP, and has a critical role in the project by leading the WP on data analytics backend services. This WP will deal with data collection and development of backend services relevant to data processing and analysis. MKLab has significant experience in artificial intelligence and specifically in the fields of machine learning, deep learning, multimodal retrieval, and multimedia analysis, as well as in the processing and analysis of the multimodal data extracted from them, and thus will work on the development of the AI based fusion frameworks and explainable AI techniques that will be incorporated in the DATAWiSE toolkits.