Innaxis feels an affinity with the Drucker Institute’s scientific approach to human interaction. Several of the projects in which Innaxis is involved, such as the ComplexWorld Network project which studies the complexities of the ATM system under the WP-E theme, ¨Mastering Complex Systems Safely¨, examine, among other things, actions and disturbances across the ATM system as a result of human behaviour.

And so it is of particular interest for us to see that this year the Drucker Forum, entering its 5th year, will look specifically at an area very much close to our interests; Managing Complexity.

The Drucker Forum will gather some of the most prominent management thinkers and CEOs from around the world to discuss their views, experiences and studies on the topic of Managing Complexity.

While the forum’s approach is in the context of business organisations, it specifically looks at what can be learnt from disciplines such as natural or social sciences or aesthetic fields, asking how organizations can transform themselves to deal with increasing complexity and issues such as uncertainty. Hence for our colleagues experienced in the study of organisational systems such as air traffic management, resilient systems designed to cope with uncertainty or complex networks, this forum may be an opportunity for a promising cross-disciplinary exchange.

Registrations for the conference, to be held on November 14 and 15 in Vienna are already open (details can be found here) and the call for submissions is open until July 1.

Innaxis is a firm believer in the potential of complex networks analysis and the power of Data Science techniques. We will present the paper Synchronization Likelihood in Aircraft Trajectories in the next USA/Europe Air Traffic Management R&D Seminar, held from June 10 to 13, 2013 in Chicago. We strongly believe these techniques will set the foundation for new ways of analysing safety levels in different contexts; from providing new techniques and correctly evaluating the safety levels of large airspace blocks to the actual development of predictive analytics that would assist in the implementation of new automation technologies.

Please attend Massimiliano Zanin’s presentation of his paper if you are attending the Seminar and do not hesitate to contact him if this area is of interest to you. Massimiliano is reachable on mz@innaxis.org.

The Federal Aviation Administration and the EUROCONTROL Organization will host the Tenth USA/Europe Seminar on ATM R&D June 10-13, 2013 in Chicago, IL, USA.

Data storing

Hard drive capacity is not increasing fast enough to keep up with the explosion of digital data world wide. A 50-fold increase in global data is forecast by 2020, but hard drives are likely to grow only by a factor of 15, even considering all the latest advances in data systems. No storage technology has been developed that can scale up to the pegabyte and beyond. Today piling conventional hard drive upon hard drive is the common procedure to handle data sets of this size.

However, there are a couple of research lines addressing this problem, both in their infancy:

Diamonds and quantum computers

Diamonds are not just for jewellery. Researchers at the Max Planck Institute of Quantum Optics and Caltech (Harvard), were able to store a quantum state in a diamond crystal for more than second, at room temperature. Doesn’t sound like much, but in quantum physics, that’s a lifetime, and a big step toward building a quantum computer with magnificent storage capacity.

Bacteria

The Chinese University of Hong Kong has recently discovered how to store encrypted data in the DNA of E. coli bacteria. Such “biostorage” could be used for future Big Data storages, especially considering a single gram of the bacteria could hold as much as 450 conventional 2-terabyte hard drives.

Big Data benefits

As explained in the first Big Data post, living in a world where economies, political freedom, social welfare and cultural growth increasingly depend on our technological capabilities, big data management and, most importantly, the knowledge that can be obtained from it, has enormous potential to benefit individual organizations. While in production teams it is increasingly asked to “do more with less”, in relation to data we are asked to “do more with more”.

There remains much unexplored terrain in Big Data and traditional databases and analytical platforms are not able to meet the challenges required of it. Capturing, filtering, storing and analysing Big Data flows has huge potential outcomes: Innovative new products, services and business models, better decision making, better productivity and higher revenues.

The emerging challenge of Big Data

Over the first years of the third millennium, worldwide digital data experienced huge growth, from scarce to super-abundant. Produced either by high-tech, scientific experiments or simply compiled from the now ubiquitous sources of automatic data collection through ordinary, every day transactions, this new reality of “Big data” -or being visually precise: “BIG DATA”- has resulted in the need for large-scale management and storage of data which cannot be handled with conventional tools.

Data management tools and hard drive capacity is not increasing fast enough to keep up with with this explosion in digital data world wide. While in economic production we are increasingly asked to “do more with less”, in contrast, in relation to data we are increasingly asked to “do more with more”.

What is the impact of this new reality and its potential benefits for the world of scientific research? Living in a world where economies, political freedom, social welfare and cultural growth increasingly depend on our technological capabilities, Big Data management, and most importantly, the knowledge that can be obtained from it, has enormous potential to benefit individual organizations.

There will be 2 parts covering this interesting reality: the first one including the current introduction and main big data sources, the second part will explain the Big Data challenges and benefits

Sources of Big Data

There are two common provenances of Big Data: On one hand, scientific experiments and tools, which were the first origin of Big Data specific study, mostly from the physics field, involving either macro or micro spatial scales. In the natural science field there is also, latterly, some biology studies, in particular, the DNA research field, starting to make use of Big Data.

On the other hand, one of the other significant sources is simply “everyday” data, the vast quantity of information that is now collected everyday at a million points of citizen interactions, collected through billions of worldwide embedded sensors.

Prepare for some big numbers:

Physics: Large Hadron Collider (LHC):

The world’s largest and highest-energy particle accelerator and one of the greatest engineering milestones ever achieved, the LHC produces around 25 petabytes of raw data per year capturing information for the over 300 (3×10^14) trillion proton-proton collisions. The information management is not easy even making use of the world largest computer grid (170 computing centres in 36 countries). The extraction of information and knowledge from these particularly huge datasets enabled the recent discovery of the Higgs Boson or “god particle” , a discovery that will probably result in the team behind the discovery being awarded the 2013 Physics Nobel prize.

Astronomy:

When the telescope from the Sloan Digital Sky Survey (SDSS) opened in 2000, it collected in one week more data than had been amassed in the entire history of astronomy. The new Large Synoptic Survey Telescope (LSST) commencing in 2020, will store in 5 days the same amount of data that SDSS will have collected over the 13 years since its inception. The storing and processing of these massive data sets from the gigapixel telescopes on the earth’s surface and in space, requires very specific tools that have been beyond the current state of the art. Consequently, astronomy, while trying to extract knowledge to create the most accurate “universe map”, is one of the leading protaganists in the field of Big Data.

Everyday data

This is the kind of data collected by countless automatic recording devices that collect data on what, how, and where we purchase, where we go and more. Its really outstanding how our lives have changed in the last couple of decades. All of these improvements and the inherent multiplication in consumption and goods, the resulting transactions, communications and more are being captured through hundreds of receptors.  In addition, user-generated content like digital media files, video, photos and blogs are being generated and stored on an unprecedented scale. Our locations (GPS-GLONASS-Galileo), money transactions (credit card, NFC payments etc), several different forms of communication and even what we think and we do in our free time (via social networks) is being collected by different corporate and government bodies.

One of the most accurate ever studies, published in the journal, ‘Science’ in 2007, revealed that humanity might store in that year around 295 exabytes (1 exabyte = 1,000,000terabytes) of data. The global data of 2009 was calculated to have reached 800 exabytes, meanwhile by the end of 2013 it is forecast to reach more than 3 zettabytes, (3*10^21 bytes, 3000000000000000000000 bytes). Impressive. Many challenges obviously arise with a, roughly, 60% yearly increase in data to be handled and issues abound in relation to how to process and extract useful information from what is 95% raw data.

In our next post we’ll look at how these challenges are being addressed.

Data Science, as a set of fundamental principles that support and guide the principled extraction of information and knowledge from data, leans on well-known data-mining techniques. However, it goes far beyond these techniques, with successful data-science paradigms that provide specific application guidelines. Data-driven decision making involves principles, processes and techniques for understanding phenomena via the automatic analysis of data.

A data-analytic thinking approach will help to envision opportunities for improving data-driven decision making in different contexts. There is strong evidence that aviation performance can be improved substantially via data-driven decision making and data-science techniques drawing on big data. Data-science will support data-driven decision making in the aviation field, where the underlying principles have yet to be established, in order to be able to realize its potential.

Innaxis participates in various research programmes and works on different applications in this field. We will be organizing a workshop on Data Science applied to Aviation in Madrid, Spain during October 2013. Please, write to us at innovation@innaxis.org if find this of interest and you would like to receive information on the workshop (please state “Data Science workshop” in the subject).

In some weeks I will reach my “semiversary” at Innaxis – a reference the team here make to the milestone of reaching a half year tenure. I am very proud to be working with the super-professional team here and felt it was time to contribute to the team’s blog and share my experience at the organisation so far. One of the things that attracted me to Innaxis, and that I have found very rewarding at the organisation, is our work in applying the concepts of resilience to ATM.

During my Aeronautical degree ‘resilience’ was described as the ability of a system to recover under abnormal conditions, generally in regards to material properties. The term usually appeared in subjects together with defining terms such as “Structures”, “Materials” or similar. Studying those subjects involved repetitive exercises that required the analysis and calculation of permanent and transient deformations after a given stress. We were required to repeat the same exercises again, this time with software, such as Catia, after some basic programming sizing the material, specifying its properties and the stress held. In short, its application and study was fairly limited.

Air Traffic, or simply ATM when referring to Air Traffic Management, was challenging in a different way, but was studied under completely different, distinct, subjects. Despite the economic crisis, airspace demand in Europe continues to grow, which lead to a focus on efficiency as the main goal of resources usage and management.

It is nice to learn new things every once in a while…[especially] when that learning takes the form of adapting previous knowledge acquired in a completely new way to a different field

My collegue Alberto wrote in his post on this blog, “It is nice to learn new things every once in a while”. I would like to add my wholehearted agreement to that sentiment and that this holds particularly true when that learning takes the form of adapting previous knowledge acquired in a completely new way to a different field. While studying, I could never imagine myself applying the material Resilience concept to ATM. Today, I not only see the clear connection but also recognise a knowledge gap and opportunity for advancement in better developing this concept within ATM. Resilience, not just efficiency, offers great opportunities to improve ATM performance.

The Air Traffic system is of a complex nature, with huge amounts of technical systems and human operators involved. Operating close to 100% efficiency, in the view of the majority of my ATM Professors, makes the ATM system sensitive to disturbances and thus vulnerable to disturbances outside our control. The complexity of the system amplifies the problem, augmenting the impact of disturbances on their way through the different ATM layers. However, over sized system buffers would decrease efficiency to unacceptable levels. Hence, a balance between efficiency on the one hand and resilience on the other is required.

Working together as a team in search of ATM Resilience is a truly wonderful and unique experience

Innaxis is leading an ongoing FP7 collaborative project  “Resilience2050″ regarding these issues. The main aim is to achieve a deeper knowledge of the  resilience concept in ATM in pursuit of a more efficient and resilient future in ATM systems. Working directly with this project is challenging, interesting and has given me the opportunity to work with an exciting international consortium of world-wide  experts, not only in the Resilience concept applied to other, non-ATM, systems, but also in data mining. This team incorporates experts from academia, ATCos and top-class aviation research centers, from countries as diverse as Germany, The Netherlands, Turkey, England and Spain. Working together as a team in search of ATM Resilience is a truly wonderful and unique experience and I hope to share some of those experiences with you in posts over the coming months as we continue to address the challenges of the task and encounter both highs and lows.

I hope you have been resilient enough to reach this blog post end, Thank you all! Hector

Innaxis is the coordinator of the ComplexWorld Network – one of two research projects through which WP-E operates. Innaxis’s director, David Perez, forms part of the programme committee for the SESAR Innovation Days, which will not only include sessions of the WP-E Research Networks, but also keynote presentations and panel discussions.

The programme includes two very compelling keynote speaker; Wolfgang Müller-Pietralla, Head of Future Research and Trend Transfer at Volkswagen and Otto Geis, VP Head of New Business at EADS CTO’s ‘Innovation Nursery’.

Both speakers promise to deliver very interesting insights into the needs and mindset of the aerospace business community and their perspective on disruptive innovations in ATM.

To find out more about the Second SESAR Innovation days and register to attend, visit the event’s website: http://www.sesarinnovationdays.eu/

It’s nice to learn new things every once in a while. I’m not a mathematician but I am definitely intrigued by the way some things work and how they can be studied. Recently I have been investigating lately about agent-based modeling.

Agent-based modeling is a relatively new science that is being used to analyse systems that are composed of many elements. In the Cassiopeia project, these elements are the airplanes, airlines, airports, air navigation service providers, and the passengers.

The nice thing about the agent-based models (ABMs) is that we can assign some decision making attributes to each element and see what happens when we run the program. Another important aspect of ABMs is that we can design the strategy of some elements, since sometimes what’s best for a single element is not the best for the team.

To put this in context, we can look at Russell Crowe’s character ¨John Nash¨ in the movie “A Beautiful Mind.¨ In one scene, his character explains that a group strategy does not necessarily require each member to achieve best possible outcome individually (which was in their case, for no one to approach the blonde woman). Often times when studying aircraft, we need to approach problems in a similar way- figuring out the group strategy that suits everyone in a collective sense. An example of this would be the distribution of delay amongst all the aircraft rather than having a few aircraft support the entire delay. This becomes a bit challenging as it would be much more easier and convenient to have solutions be based on key individual factors (one man approaches the woman; few aircraft bear the burden of delay), however we learn and demonstrate in the Casseiopeia project that agent-based modeling is actually the best way to resolve situations. In the end more is benefited from a involving multiple elements rather than just a few.