An original big data approach

The "Surfer way": riding on and controlling the wave of data

The generalized version of our SURFER solution consists in associating a non-intrusive architecture based on intelligent generic and hierarchically organized agents with the key equipment in a fleet. These agents observe the signals and establish a robust, contextualized health status of the equipment monitored. The hierarchical vision of the architecture allows us to process the entire information chain, from low level raw data to the maintenance centre. All "augmented" equipment, at whatever level, becomes active, able to cooperate with the human operator via innovative interfaces. The theoretical scientific background describing the "Surfer way" is available [here].

Our developments are based on several patents, and in particular, one filed by the three SurferLab partners developed as a result of the successful applications achieved during the parent project SURFER. This patent is considered as the cornerstone of SurferLab and defines the socalled "surfer way"!

Know-how

Even if SurferLab is an autonomous structure, it welcomes collaboration with other partners. If you are interested in our "Surfer way", it would be a pleasure to work with you. We propose various types of collaboration:

• Technical (feasibility study, benchmark) or scientific (analysis, specifications, state-of-the-art) assessments or advice pertaining to one of SurferLab's fields of expertise,
• Research projects in the field of embedded intelligence in transportation, in collaboration with the three SurferLab partners in the form of a national (ANR, FUI) or international (H2020) project for example,
• Technical services in the field of embedded intelligence in transportation implementing the joint Surfer patent and involving SurferLab's industrial partners.

Our engineers and researchers can work with you in the field of distributed intelligence, embedded systems, and artificial intelligence in road, rail, air or maritime transport:

• Industrial know-how: expertise in the non-intrusive collection of real-time data from automated equipment; use of data behaviour patterns characterize states and operational drifts, or for diagnosis by comparison with a nominal state; provision of behaviour data (Internet of Things, cloud ...); modelling and simulation of equipment for the development and validation of control systems.
• Academic know-how: functional analysis of complex systems; RAMS studies; design and simulation of embedded control and monitoring architecture; autonomous systems and distributed artificial intelligence; learning algorithms; optimization techniques; human-machine cooperation.

An original approach for the management of big data in transportation

Our field of expertise includes the ability to process and transform mass data into knowledge that can be exploited by industrialists. Our approach to solving the problem of "big data" is original in the sense that we prefer to work with local, contextualized, reliable aggregated knowledge depending on the decision-maker's desired level of granularity rather than huge quantities of raw data that are difficult to manage in real time. We define ourselves as architects of information systems whose mission is to translate raw data into exploitable knowledge.