Sesar-03-2015 - Information Management in ATM
Specific Challenge: The development and introduction of operational concepts that require close collaboration between diverse actors and therefore require a more intense system wide information sharing, e.g. trajectory based operations, raise a number of new challenges when compared to traditional peer-to-peer architectures. There is strong commitment to system-wide information management (SWIM) in SESAR and a number of important developments are near implementation, but it is a dynamic field which provides regular opportunities for innovative approaches.
Research shall at a minimum address the management and distribution of all types of information in ATM between all stakeholders, with particular attention to the challenges of scalability, stability and error promulgation that relate to the inherent conflict between consistency, availability and partition tolerance in a distributed computer system like global ATM (CAP Theorem). It may also include new paradigms for the analysis of unstructured data, data streaming and non-concurrent data integration. This research will be expected to address the challenges of the distribution and management of all types of information in ATM between stakeholders.
Cyber-security is a major concern with such large quantities of information circulating in an aviation environment that, by using the internet, is connected to the external world. Innovative strategies may be explored to cope with security breaches designed to destroy, interrupt or corrupt information.
The quantity of data and information in ATM (meteo, aeronautical, flight, and trajectory) is increasing by orders of magnitude and studies are needed to see how elements of the system should cope with this continually increasing stream of information and, indeed, whether it is desirable in some areas. In this respect data science is emerging as a multi-disciplinary field, blending skills from scientific domains such as statistical physics, network theory or complexity theory, with techniques from computer science such as data mining, data indexing and visualization. This may be applied to data using new techniques and processes to extract and filter knowledge from raw, heterogeneous and incomplete data sources and hence dramatically improve accessibility and relevance to the end user.
The potential of using Ontology engineering models in a networked environment, and this in relation to the ATM Information Reference Model (AIRM) with various semantic resources (e.g. models) co-existing to form a semantic web, is one of those possible new developments. In the context of ATM, a better understanding is required of the benefits and disadvantages of using more modular, well separated and smaller models, as opposed to large monolithic models. Governance of these models and their corresponding information exchange services (evolution in a multi-stakeholder environment) is an important aspect of that.
There is also work to be done on understanding and presenting the impact of uncertainty of integrated information into the ATM decision-making process. For example meteorological forecasts will improve over time with better models and increased computer power but will always have a degree of uncertainty. Improving management of the potential impact of uncertainty in a multi-stakeholder environment would allow for better integration.
Expected impact: Improved data and information management is an important foundation for emerging SESAR concepts. High quality information that is correctly managed and presented to its clients in the ATM system will result in improvements in safety and efficiency of operations. These are key objectives in SESAR, which is seeking to substantially improve efficiency, with consequent positive impacts on environment, and higher levels of safety.
Type of action: SESAR2020 Research and Innovation Action (RIA)
Further conditions related to this topic are provided in the Technical Specification of the Call.