For the 5th consecutive year, Innaxis organized the Data Science in Aviation Workshop with much positive feedback. This 2017 edition took place last September at EASA HQ in Cologne, Germany, sponsored by the SafeClouds.eu project.
This series of annual workshops was created in 2013 to promote data science techniques applied to the aviation field. Initially, this was a breakthrough idea as data analytic initiatives in the sector were very scarce. On the other hand, the potential benefit of applying these techniques to aviation, with relatively limited investment, greatly supported the effort of pushing this paradigm shift. Now, only 5 years later, the number of ongoing initiatives of data science applications in the aviation sector has continuously increased; demonstrating that the effort was really worth it.
Data has become the key driver of change all across aviation: from maintenance to training, from fuel efficiency to safety. There are on-going examples, with different levels of maturity, in nearly every layer of the aviation sector. This ranges from manufacturing to operations, both from the industry as well as the academia. The last DSIAW brought together this wide variety. Knowledge discovery and Data Mining (KDD) will be, is currently being, a key enabler of the digitalization of our industry.
The entire Horizon2020 transport research programme is driven by the overall objective of making ““. These challenges were precisely the 4 pillars of the 2017 DSIAW, showing how data can play a key role in achieving them through the application of data science (DS) techniques. The presentations were distributed among these 4 sessions: DS4Environment, DS4Safety, DS4Predictability and innovative DS techniques and supporting tools, illustrating the audience with these initiatives:
DS4Environment: While the development of greener technologies (engines, aerostructures, components, etc) require several coordinated initiatives, data science offers cost-effective solutions based on real figures of fuel burnt and noise pollution. Applying data analytics techniques to these datasets enhances our knowledge of fuel consumption and noise emission patterns, which supports efficient resource use, thus resulting in a emissions reduction to minimize environmental impact. For this theme, Boeing Global Services – Fuel Dashboard solution and the Technical University of Madrid initiatives related to environmental and noise emissions studies.
DS4Safety: The aviation sector’s requirement for high safety levels has always been the main reason to avoid ‘radical’ changes in this industry or, at least, follow a very slow adoption path. Nevertheless, aviation safety has recently become a pioneering area in data science applications. We can’t neglect to mention the significant challenges in this line of research, such as data protection, data merging, pattern detection in rare events, secure data infrastructures, etc, but nonetheless there are very promising initiatives such as: the SafeClouds project coordinated by Innaxis, the EASA Data4Safety programme, or the activities from SafetyData in NLP applied to Occurrence Reports. All projects were presented at the workshop.
DS4Predictability: In air transportation, efficiency is very linked to predictability, and predictability in turn, is highly dependent on data. Improving predictability reduces uncertainty which avoids losses and enables a more efficient aviation system from reducing delays to predicting systems failures. Ongoing studies, such as those presented by the University of Westminster or Atos, are good examples on how data can provoke a deep transformation of common airline procedures, like disruption management or maintenance scheduling.
DS techniques and supporting tools: Different KDD application techniques require appropriate infrastructures as well as supporting techniques that ensure various requirements are met. This includes: data protection, security, computation efficiency, flexibility, scability, etc. During this last workshop, we learned from the Eurocontrol experience in using cloud-based infrastructures. We also learned about the Innaxis spin-off, TADOREA, which shared knowledge on crypto-economics as a potential solution for enabling secure data analytics, while maintaining data privacy.
Still not convinced? Wanting to learn more? Visit the event page to watch the presentations and videos.