Tadorea focuses on Knowledge Discovery and machine learning solutions, based on massive data analytics for the aviation sector.
Tadorea aims to extend and apply the research results achieved by its founding entities to the real needs of aviation stakeholders.
We leverage the research results in the aviation field during the last few years, including a variety of simulation models built in the domains of mobility, GHG emission assessment, delay propagation, passenger connectivity and network resilience management. This is done through meticulous interfacing with end users and addressing issues of deployment infrastructure and industrialization.
Furthermore, we use and expand the data infrastructure built by Innaxis to efficiently process aviation information from disparate data sources.
Data comes in different formats. Whether it be structured or unstructured, the lack of explicit semantics and data silos of heterogeneous data sources without interoperability are common issues. At Tadorea, we ensure the quality of the data, data provenance and data lifecycle management processes.
We work on cloud computing architectures to manage heterogeneous data at-rest and data-in-motion, making sure the performance of our algorithms provides scalable, and when possible, real time analytics.
There are different analytics frameworks which provide business analytics and intelligence. Integrating those analytics with the business processes is key. We work with state-of-the-art predictive and prescriptive techniques, including complexity and graph analysis.
We work with privacy-aware technologies which allow machine learning on fused datasets while maintaining data privacy. We use secure multi-party computation when needed, as a much more powerful alternative to de-identification.
We use visual metaphors as a tool both for communication and data mining. We develop interactive interfaces that ensure visual data discovery and exploration for our users.
This constitutes a complete air transport simulation tool including a wide range of performance and mobility metrics, for a variety of uses for airlines, network managers and policy makers. This simulator is capable of modeling passenger connectivities and a wide range of flight and passenger prioritisation scenarios. This simulator has been built using airline and other industry inputs, and captures airline decision-making including their related costs, by fusing a variety of data sources.
Tadorea efficiently processes flight information from different data sources, including more than 20 million flights that have operated in Europe within the last three years. A production-ready infrastructure enables data mining for the development of accurate predictive analytics, which are ultimately available for use by airlines, airports and air navigation service providers for strategic decision-making or real-time oversight.
We can monitor runway performance, runway occupancy time, and aircraft operations in order to predict safety and performance issues. This is all possible by taking into account the aircraft type/operator, meteorological conditions, runway configurations, threshold times, touchdown points, rotation points, line-up times, exit/entry points, excessive runway occupancy, runway over run, missed exit points, and other similar factors.
We develop insightful performance metrics for air transport scenarios, including explicit passenger itineraries and estimations on delay cost. Our technology enables evaluation under novel flight and passenger prioritisation scenarios, exploring trade-offs, and characterising the delay through the network.
Through data mining tools, techniques and procedures we’re able to optimise fuel use in airline operations taking into account the complexity of the scenarios which presently characterise air transport operations. This is completed through observing the airspace capacity restrictions and flow control, airport congestion, meteorological conditions, fleet and equipment mix, personnel training, and other factors.
We are able to perform various computations of aviation emissions based on a requirements-based, quantitative approach. This also includes a validation of CO2 estimates even when the data is incomplete or of unknown quality. For this, we utilise emissions modeling, aircraft performance models, mathematical modeling, statistical analysis and operational expertise.
We are able to examine airspace performance by exploring the relationships between sector capacity, workload and complexity. We do this through machine learning techniques that are used to analyse large quantities of historical data, that are ultimately used to provide a set of predictive analytics that provide the air traffic controller teams with a deeper understanding of the most likely scenario to occur considering the information available.
This analysis is based on data-driven metrics for safety monitoring and safety intelligence. This leverages unique data in which we can develop predictive analytics for safety, including safety performance indicators for a variety of scenarios, such as: unstable approaches, real time approach congestion monitoring, airprox, lack of proper separation with terrain, or adherence to air traffic control procedures.
“How so much progress depends on the interplay of techniques, discoveries, and new ideas, probably in that order of decreasing importance.”
“Disruptive technologies typically enable new markets to emerge.”
“It will be an interesting match, but I think it will be impossible to defeat Lee Sedol this time.”