Mindtooth will aim at establishing a new era within the Human-Machine Interaction domain, enabling a truly “intelligent” and “cooperative” interaction between humans and the devices surrounding them by using cerebral signals. Mindtooth system will rely on an EEG headset designed according to the principles of ergonomy, easiness-to-use and limited invasiveness, and able to provide in real time Neurometrics holistically assessing the mental state (i.e. workload, stress, fatigue, etc.) of a general user during its activity. In particular, Mindtooth will be tailored on three specific applications, including the users’ needs collection and experimental validation in each one of them: aircraft pilots training, car drivers and industrial workers. These three environments have been chosen because of (i) the great social impact our system would have within them by increasing safety and ensuring workers’ healthy, and (ii) the great economic impact these sectors are going to produce on society, thus acting as an incomparable driving force for Mindtooth success. Although a precautionary approach, Mindtooth is expected to generate an overall turnover of 80 M € in the first five years after the project conclusion, i.e. almost 15 times the total funding request. Mindtooth, already at a TRL6 stage and intended to reach the TRL9, and consequently the market, within two years, relies on the strong scientific background of BrainSigns, Project coordinator, pioneer and leading company in the field of neurophysiological measurements and related applications. Indeed, a very multidisciplinary consortium has been built by involving leading industrial companies such as Brain Products, IBM and ITCL: their effort will be focused on the establishment of solid and robust technological assets via synergy of all partners’ existing assets and skills, joined with the aim to generate a commercial product, the Mindtooth, that will have a significant market impact across multiple application domains.

Big data applications processing extreme amounts of complex data are nowadays being integrated with even more challenging requirements such as the need of continuously processing vast amount of information in real-time. Current data analytics systems are usually designed following two conflicting priorities to provide (i) a quick and reactive response (referred to as data-in-motion analysis), possibly in real-time based on continuous data flows; or (ii) a thorough and more computationally intensive feedback (referred to as data-at-rest analysis), which typically implies aggregating more information into larger models. Given the apparently incompatible requirements, these approaches have been tackled separately although they provide complementary capabilities. CLASS aims to develop a novel software architecture to help big data developers to combine data-in-motion and data-at-rest analysis by efficiently distributing data and process mining along the compute continuum (from edge to cloud) in a complete and transparent way, while providing sound real-time guarantees. CLASS aims at adopting (1) innovative distributed architectures from the high-performance domain; (2) timing analysis methods and energy-efficient parallel architectures from the embedded domain; and (3) data analytics platforms and programming models from the big-data domain. The capabilities of the CLASS framework will be demonstrated on a real smart-city use case, featuring a heavy sensor infrastructure to collect real-time data across a wide urban area, and prototype cars equipped with heterogeneous sensors/actuators, V2I connectivity, and cluster support to present the innovative capabilities to drivers. Representative applications for traffic management and advanced driving assistance domains have been selected to efficiently process very large heterogeneous data streams in real-time, providing innovative services while preparing the technological background for the advent of autonomous vehicles

The valuable insights that can be inferred from analytics of data generated and collected from a variety of devices and applications are transforming businesses and are therefore one of the key motivations for organisations to adopt such technologies. Nevertheless, the data being analysed and processed are highly sensitive and put the individuals’ rights to privacy at risk. With the imminent arrival of the European General Data Protection Regulation (GDPR), companies are coerced to adopt privacy enhancing technologies that, on the one hand, protect data to ensure their clients’ privacy and on the other hand, allow their processing while keeping them meaningful, useful, and protected at the same time. The PAPAYA project aims at addressing the privacy concerns when data analytics tasks are performed by untrusted third-party data processors. Since these tasks may be performed obliviously on protected data (i.e. encrypted data), the PAPAYA will design and develop dedicated privacy preserving data analytics primitives that will enable data owners to extract valuable information from this protected data, while being cost-effective and accurate. The PAPAYA project will consider compliance with the GDPR as a key enabler to provide solutions that minimize the privacy risks while increasing trust in third-party data processors by means of auditing and visualization modules (a dashboard). The PAPAYA primitives as well as the dashboard will be combined in an integrated platform that will be designed, implemented and validated through a set of use cases reflecting relevant real world applications (namely, healthcare analytics and web & mobile data analytics).