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ENTRY LEVEL/JUNIOR DATA ENGINEER

Innaxis is currently seeking for a Data Engineer (Entry Level/Junior ) to join its deployment team, Tadorea. We are based in Madrid, Spain. We look for talented and highly motivated data engineers who want to pursue and lead a career outside of the more mainstream, conventional alternatives. Individuals with a great dose of imagination, problem solving skills, flexibility and passion are encouraged to apply.

As a Data Engineer, you will help the team to design and integrate complete solutions for Big Data architectures; from data extract, load and transform processes until data storage, life cycle, management and delivery for analysis. Always making use of the latest technologies and solutions for the ultimate performance.

Skills wanted
Data Engineers at the Innaxis spin off, work very closely with the rest of the Data Science team, so a broader knowledge and a varied skillset will be very much appreciated.Candidates would be evaluated according to the following items (fulfilling the complete list is not a mandatory requirement)

  • University degree on Computer Science
  • MSc or PhD not required but positively evaluated
  • Professional experience is not a must, although it might be positively evaluated.
  • Proficient in a variety of programming languages, for instance: Python, Scala, Java, R or C++ and up to date on the newest software libraries and APIs, e.g. Tensorflow, Theano.
  • Experience with acquisition, preparation, storage and delivery of data, including concepts ranging from ETL to Data Lakes.
  • Knowledge of the most commonly used software stacks such as LAMP, LAPP, LEAP, OpenStack, SMACK and similar.
  • Familiar with some of the IaaS, PaaS and SaaS platforms currently available such as Amazon Web Services, Microsoft Azure, Google Cloud and similar.
  • Understanding of the most popular knowledge discovery and data mining problems and algorithms; predictive analytics, classification, map reduce, deep learning, random forest, support vector machines and such.
  • Continuous interest for the latest technologies and developments, e.g. blockchain, Terraform.
  • Excellent English communication skills (written and oral). It is the working language at Innaxis.
  • And of course, great doses of imagination, problem solving skills, flexibility and passion.
Benefits
The successful candidate will be offered a position as a Data Engineer, including a unique set of benefits:

  • Being part of a young, dynamic, highly qualified, collaborative and heterogeneous international team.
  • Flexible working environment, schedule and location.
  • A horizontal hierarchy, all researchers’ opinions matter.
  • Long term and stable position. Innaxis is steadily growing since its foundation ten years ago.
  • Salary adjusted to skills, experience and education.
  • The possibility to develop a unique career outside of mainstream: academics, private companies and consulting.
  • No outsourcing whatsoever, all tasks will be performed at Innaxis offices.
  • An agile working methodology; Innaxis recently implemented JIRA/Scrum and all the research is done on a collaborative wiki/Confluence.
Applying
IMPORTANT: Interested candidates should send their CV, together with a interest letter (around 400 words) and any other relevant information supporting their application to recruitment@innaxis.org .You will be contacted further and a personal selection process will start. We deal personally with all candidates.

Mobility metrics and indicators rethought

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Performance is about comparing some output of a system with some level of expectations. The issue of setting the right level of expectations is certainly a major issue by itself, but choosing the right metrics to measure is probably even more difficult.

This difficulty comes from the fact that Key Performance Areas (KPAs) live in a different world than Key Performance Indicators (KPIs). KPAs live in a qualitative world, where general ideas are thought to be important for human beings. For instance, ‘safety’. KPIs on the other hand belong to a quantitative world of ‘cold values’ — floats, integers — observed on the real world. Matching these two worlds is like getting into Mordor: first you think that it will be obvious, then you think that it will be impossible, and you finally pick a way because it is pretty much the only one available.

Indeed, the potential KPIs that one could imagine are fortunately severely restrained by reality and what we can observe in the system. For instance, in DATASET2050 we were trying to define an indicator for the ‘seamlessness’ of a trip, something which is important for all travelers without a doubt. Important, ok, but what is it exactly?

Seamlessness is about the perception of travellers. As a consequence, it is highly subjective, which by definition cannot be part of an indicator, because an indicator is meant to be objective. So instead of a top-down approach where we use the question ‘What would be the best metrics to measure in order to represent seamlessness?’, we are left with a bottom-up approach consisting in ‘Among the ones I can measure, what are the metrics which would be related somehow to seamlessness?’.

So, what can we measure? For many years now, sociologists and psychologists use the ‘cognitive load’ to have a measure of the effort needed by a brain to accomplish a given task. Seamlessness is about being able to forget the trip itself and not actively be forced to take decisions or looking for information for the continuation of the journey. We thus defined a first indicator, which is the total cognitive load of a given trip for the passenger as a measure of seamlessness. Ok, but how do you measure cognitive load in reality?

Well, you don’t, as least not on a large scale. And here comes the second step of the search for a good indicator: can we find something easily measurable which is an approximation for what would be a perfect indicator?

In the case of seamlessness, we have to go back to how the travel unfolds. For instance, what is the difference between:

1) depart from home, take a taxi, take a train, take a taxi, arrive at destination.vs:

2) depart from home, take a taxi, take a train, take another train, take a taxi, arrive at destination.

Easy: there is one train more. Ok, but what makes you choose the first option over the second if both have the same travel time, price, etc.? Well, the first is easier, right? You do not have to think about getting off the train, find the next one, wait, get in train, possibly struggling to find a spot to seat, etc. So the idea that the first one is easier than the second one comes ultimately from the ‘continuation’ property of the actions you are taking, which is associated with a low cognitive load dedicated to the journey. In other words, taking different actions during a trip is more annoying that taking only one action.

Following this idea, DATASET2050 defined the journey as a series of ‘phases’ and ‘transitions’. ‘Phases’ are typically long with a low cognitive load dedicated to the journey, whereas ‘transitions’ are short and require the active participation of the passenger in order to continue the journey. A simple indicator can then be defined as the number of transitions taken in a single journey, which is trivial to compute for nearly any journey, with very little data input.

A slightly more advanced indicator is to consider the time spent within the transitions — for instance, queuing times — compared to the total travel time. For instance, a small 45 minutes trip where one has to take three buses is quite tiring compared to a single-bus journey. This indicator requires more data, as the specific times in each of the segments are required. However, it is largely feasible to compute it with modern methods of data collection (e.g. GPS tracking). Giving a good balance between the measuralibity and its concetpual proximity with the initial KPA, this indicator is the one which has been selected as key performance indicator for seamlessness in DATASET2050.

In DATASET2050, we have gone through the exercise of finding the right indicator for all of the KPAs defined by ICAO, including safety, flexilibity, efficiency, etc. These concepts are sometimes too vast and need to be broken down into sub-KPAs, called “Mobility Focus Areas”. For all of them, several indicators have been defined, but we selected only one final KPI in the end per KPA. For instance, the KPA “flexibility” has been subdivided into “diversity of destinations”, “multimodality”, and “resilience”. Only on key indicator has been selected in the end, weighting the travel options by the distance between the potential destinations. All this work can be found in the public deliverable 5.1 of DATASET2050, soon available here

To conclude, the choice of a good indicator is thus dictated by the balance between the measurability of the metrics and its relationship with the overall concept. This is an important issue, as the indicators are then used by the policy makers to drive the system is a certain direction. And the quality of the indicator decides whether it is the right one or not.

Author: Gérald Gurtner (University of Westminster) as part of DATASET2050 post series

Blockchain and other data science applications for aviation digitalization

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 “European transport greener, safer, more efficient and innovative“. 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.

10 years later… and so much to come!

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This year marks Innaxis’ 10th Anniversary. A most remarkable date that we are very happy to celebrate and share with you. This decade -and the 30 projects developed so far- have provided us the opportunity of creating solid relationships with trusted partners and strengthening those links through successful collaborative stories. We consider you as part of this trusted network of partners, colleagues and friends and we feel very grateful for it.

As you surely know, Innaxis was founded with the objective of finding applications of Complexity Science to address problems of real socio-technical systems. From that (quite abstract) idea, we have done our (exciting and challenging) way to become a reference research organization at the confluence of Complexity Theory, Data Science and Societal Challenges, mainly in the Aviation and Mobility sector. This rapid evolution has been possible, and even more stimulating, thanks to people like you and organizations like yours, who have accompanied us in this journey.

Addressing real-life problems through breakthrough innovation requires a clear focus on applied research and a close collaboration with end-users to ensure the solutions meet users´ expectations and help in solving their needs. To effectively apply some of the research results obtained, we launched some time ago a new venture called Tadorea as a spin-off of Innaxis. Tadorea focuses on applying Knowledge Discovery and Machine Learning solutions to the aviation sector, leveraging on massive data analytics. We strongly believe on the potential of this promising area, and so David Pérez has been appointed as General Manager of Tadorea to take the lead of our spin-off efforts. David will nevertheless stay very well connected to Innaxis by being nominated to its Board of Trustees.

And it is also time to give new responsibilities to people who have been with us for a long time and have shown an outstanding capacity and performance, combined with personal styles which are quite unique. Both Paula López-Catalá as Programme Director and Samuel Cristóbal as Science & Technology Director, are newly appointed to these most relevant functions. Together with them, David, Arantxa Villar as Finance Director and Carlos Álvarez Pereira as President, will integrate the Management Team to pursue our -even more- ambitious goals in this new era which we are much willing to share, explore and enjoy with you.

Engaging with a new generation of change makers

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Considering the intricate challenges humanity has to face such as climate change, social inequalities, migration, and technological disruption (just to name a few), we are in desperate need for a new generation of changemakers who are able to grasp the systemic and interconnected nature of the issues in order to design innovative approaches that overcome today’s barriers to change.

INX4PS is actively seeking to promote systems literacy and to engage with a new generation of changemakers to raise awareness as to how complex issues can be embraced.

In this context, INX4PS has participated at the first Club of Rome (CoR) Summer Academy, which took place 7th-13th 2017 September in Florence, Italy.

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The Club of Rome is an eminent international think tank that launched in 2016 with the “Reclaim Economics” project, designed to transform the way our economic system is perceived and understood. It promotes new economic thinking that puts human well-being and the planet at the centre. The Reclaim Economics flagship event was the first Club of Rome Summer Academy in Florence.

The CoR Summer Academy has been attended by students and academics, young professionals, aspiring entrepreneurs, young journalists, artists and activists. The participants joined with some of the world’s leading social and systems thinkers to inspire economic, ecological, and political movements towards action.

Carlos Alvarez Pereira (President of Innaxis and member of the Club of Rome) gave insights on the issue of  “TECH FOR HUMANITY – REFLECTIONS ON THE TECHNOLOGY REVOLUTION”.

During the interactive dialogue with the Summer School participants, many items were discussed including the role of science and technology and its impact on social evolution, the consequences on sustainable development, and furthermore the meaning of “technological disruption”.

The core theme of the debate was the role of current mindsets which largely influences technological innovation outcomes, including how well society adapts and integrates these new technologies. Additionally, questions of technology’s overall purpose, and how to design technology while ensuring humanity is the beneficiary, spurred a dynamic discussion among the participants.

The event was attended by 120 participants from 25 different countries. Among the speakers included: Kate Pickett, Kate Raworth, Anders Wijkman, Ernst Ulrich von Weizsäcker, Mathis Wackernagel, Ugo Bardi, Jorgen Randers, Tim Jackson and many more who discussed challenges and proposals for addressing systemic challenges.

INX4PS is looking forward to further engaging with the new generation of changemakers, to introduce systems thinking, and to co-create the paradigm shift of the 21st century.

Workshop: Digital for Sustainability – In Need of a Disruptive Research Agenda

"Digital Transformation" is the buzz phrase of the day. Since the 1980s an explosive growth has happened in Information and Communication Technologies (ICT), and its become pervasive, bringing a perception of tremendous acceleration in technological innovation. There are also high expectations for the role of ICT in sustainable development. Concepts such as disruption, dematerialization and zero marginal costs contribute to the (up to now) false belief that becoming increasingly digital will lead to low resource consumption. However, research shows that the ICT sector itself is not environmentally friendly; it is the fastest growing contributor to emissions, it consumes large amounts of energy, water and critical resources, and produces equally vast amounts of harmful waste with minimal recycling.

To address the generic claim of ICT as contributing to a better and “green” world, there should be mutual recognition and cooperation between digital tech and sustainable development, especially to understand the significant effort needed to harness the power of ICT for human advancement. Digital technologies and sustainability have rarely been analysed together in a rigorous manner. The scientific literature about the nexus of these topics is, up to now worryingly thin, and in many aspects not yet addressing the right questions, much less the responses.

This issue demands a rigorous inquiry of issues at stake and the foundation of a research agenda that builds strong synergies aimed to act beyond current hyped assumptions.

Considering this, Innaxis would like to invite you to the “Digital for Sustainability – In Need of a Disruptive Research Agenda” workshop. This event will be organised during the World Resources Forum on Tuesday 24th October 2017 in Geneva.

The goal of this workshop is to ignite a community of interested parties, who work on interdisciplinary research and action agendas, and to enable the alignment of digital technologies with the goals of sustainable development.

Speakers: Carlos Alvarez Pereira, Ladeja Godina Košir 
Workshop Organisers: Innaxis Research Institute and Texelia AG
Workshop Co-Organiser: Circular Change
Workshop Chairs: Soumaya El Kadiri (Texelia AG) and Joséphine von Mitschke-Collande (Innaxis Research Institute)

Date and time: Tuesday 24th October 2017, 16h30 – 18h30

Venue:
Centre International de Conferences (CICG)
Rue de Varembé 17
1211 Genève - Switzerland

 

Vista tactical model – Mercury: because passengers matter

Over the next decades, EU mobility is expected to progressively evolve from the gate-to-gate focus currently prevalent in the aviation and ATM industry towards a seamless and efficient door-to-door-orientated vision.  The paradigm shift from gate-to-gate (hence aircraft centered) to door-to-door (passenger-oriented) is present at virtually all strategic research documents and agendas. The paradigm shift is here to stay. From a passenger perspective, which of the following scenarios create more impact?:

  • Scenario A): a 8 minute delay in an aircraft arrival time with no connecting passengers
  • Scenario B): a 5 minute one that prevents a significant number of passengers doing a connection in that airport and subsequently expand their door-to-door trip in more than 10 hours

How can that impact be predicted in terms of time and cost? One of the very first research exercises was the POEM project (SESAR 1- WPE) etc. This project was the original seed of Mercury. Mercury has been afterwards improved, validated and completed in other reseach initiatives for SESAR and European Commission, reaching its current door-to-door status.

What is mercury?

Mercury is a modelling and simulator tool - a framework capable of measuring the performance of the air transport network. It provides a wide range of performance and mobility metrics, capable of describing in detail different air transport scenarios.

Mercury draws on extensive data, drawn from a wide range of industry sources, including airlines, airports and air navigation service providers. Mercury's data models have been demonstrated through over 5 years of research and development, plus industry consultation.

How passenger matter in mercury?

Mercury is the first air transportation network simulator that puts passengers in the centre. Each day of simulation the itineraries of more than 3 million passengers are reproduced. Each passenger has its individual profile, ticket and decisions to make. According to EU regulation 261/2004 passengers are compensated by delay and cancellations. Extended delays, aborted journeys, overnight stays there are all part of the Mercury simulator.

Of course airlines play a major role as well, Mercury incorporates costs models for canonical airline categories. Each of the airline decision of waiting for certain passengers, cancel a flight or even board the passengers and send a ready message even when a ATFCM slot was assigned is taken according to each airline rational cost model.

The secret ingedient: a spice of randomness

There is no way one could develop a simulator like Mercury taking into account every detail in the air transportation system. Some process are just too complex or simply put we do not understand yet. Whilst others are just exogenous factors far beyond the reach of the air transportation system. 

But what if we could use a different approach. In Mercury each day of operations is repeated, introducing small variations representing everyday uncertainty and exogenous factors.

Ultimately, small changes lead to completely different day of operations, delays and cancellations. Just similarly to what happens with some chaotic systems, the sensitivity to the initial conditions allow to explore overall trends and stable status, in some cases called emergence.

Interested in reading further info about Mercury? Click here to visit the website.

Author: Samuel Cristóbal (Innaxis)

Entry level/Junior Data Scientist or Data Engineer

Innaxis is currently seeking for Data Scientists and Engineers to join its research and development team based in Madrid, Spain. Talented and highly motivated individuals who want to pursue and lead a career outside of the more mainstream, conventional alternatives. Individuals with a great dose of imagination, problem solving skills, flexibility and passion are encouraged to apply.

  • As a Data Engineer, you will help the team to design and integrate complete solutions for Big Data architectures; from data acquisition and ETL processes until storage and delivery for analysis, using the latest technologies and solutions for the ultimate performance.
  • As a Data Scientist, you will mainly assist the team to understand, analyse and mine data, but also to prepare and assess the quality of such. You will also develop methods for data fusion and anonymization. Ultimately your goal will be to extract the best knowledge and insights from data, despite technical limitations and committing with regulatory requirements.

About Innaxis

If not unique, Innaxis is at most not conventional: it is a private independent non-profit research institute focused on Data Science and its applications: most notoriously in aviation, air traffic management and mobility, among other areas.

As an independent entity, Innaxis determines its own research agenda and has now a decade of experience in European research programs with more than 30 successfully executed ones. New projects and initiatives are evaluated continuously and open to new opportunities and ideas proposed within the team.

The Innaxis team consist on a very interdisciplinary group of scientists, developers, engineers and program managers, together with an extensive network of external partners and collaborators, from private companies to universities, public entities and other research institutes.

Skills wanted

Our team work very closely on a daily basis, so a broader knowledge means a much better coordination. Therefore, there is a unique list of skills ideally wanted for both positions. Those skills would be then weighted/assessed as requirements or “bonus points” according to the candidate’s position of interest, i.e. Data Scientist or Data Engineer.

  • University degree, MSc or PhD on Data Science or Computer Science, or related field provided all other requirements are met.
  • No professional experience required, although it might be positively evaluated.
  • Proficient in a variety of programming languages, for instance: Python, Scala, Java, R or  C++ and up to date on the newest software libraries and APIs, e.g. Tensorflow, Theano.
  • Experience with acquisition, preparation, storage and delivery of data,  including concepts ranging from ETL to Data Lakes.
  • Knowledge of the most commonly used software stacks such as LAMP, LAPP, LEAP, OpenStack, SMACK or similar.
  • Familiar with some of the IaaS, PaaS and SaaS platforms currently available such as Amazon Web Services, Microsoft Azure, Google Cloud and similar.
  • Understanding of the most popular knowledge discovery and data mining problems and algorithms; predictive analytics, classification, map reduce, deep learning, random forest, support vector machines and such.
  • Continuous interest for the latest technologies and developments, e.g. blockchain, Terraform,
  • Excellent English communication skills. It is the working language at Innaxis.
  • And of course, great doses of imagination, problem solving skills, flexibility and passion.

Benefits

The successful candidate will be offered a Innaxis’ position as a Data Scientist or Data Engineer, including a unique set of benefits:

  • Being part of a young, dynamic, highly qualified, collaborative and heterogeneous international team.
  • Great flexibility in many aspects -including working hours, compatibilities and location- and most excellent working conditions.
  • A horizontal hierarchy, all researchers’ opinions matter.
  • Long term and stable position. Innaxis is steadily growing since its foundation ten years ago.
  • A fair salary according to the nature of the institute and adjusted to skills, experience and education with continuous revision.
  • Independence, as a non-profit and research-focused nature of Innaxis, the institute is driven by different forces than in the private sector, free of commercial and profit interests.
  • The possibility to develop a unique career outside of mainstream: academics, private companies and consulting.
  • No outsourcing whatsoever, all tasks will be performed at Innaxis offices.
  • An agile working methodology; Innaxis recently implemented JIRA/Scrum and all the research is done on a collaborative wiki/Confluence.

Apply

Interested candidates should send their CV, a research interest letter (around 400 words) and any other relevant information supporting their application to recruitment@innaxis.org You will be contacted further and a personal selection process will start.

 

ISSS 2017

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“From Science to Systemic Solutions – Systems Thinking for Everyone”
ISSS2017 Vienna
The 61st ISSS World Conference

As systems sciences has been a heterodox scientific field, the International Society for the Systems Sciences (ISSS) aims to bring a community of researchers and practitioners together once a year, during the ISSS World Conference, to exchange and share ideas related to systems sciences.

The founding fathers of the society, Ludwig von Bertalanffy, Kenneth Boulding, Ralph Gerard, and Anatol Rapoport, conceived the organization to be devoted to interdisciplinary inquiry into the nature of complex systems. In recent years the organization has broadened its scope, particularly to include the practical application of systems methodologies to problem solving.

Considering this, the 2017 edition convened by the Ludwig von Bertalanffy Centre in Vienna invited an interdisciplinary community to share the latest insights of the interdependencies between social, ecological and technological systems, as well as practical design skills for sustainable living and technologies for a future-oriented humanity.

INX4PS participated in past conferences and was invited to participate in ISSS2017 through a panel discussion deliberating the theme “From Money to Value Creation to Reinventing Economy in the Energy Transition”. Joséphine vMitschke-Collande represented INX4PS and discussed with Dr. Olaf Brugman (Rabobank), Christoph Thun-Hohenstein (Museum für angewandte Kunst), Roland Kuras (Power Solution), Ladeja Godina Košir (Circular Change) the role of systems approaches regarding sustainable finance, the energy transition, the circular economy and the arts.

Issues such as the lack of systems approaches in the design of the German Energy Transition, does not foresee how to overcome current self-referential economic dynamics. The current system favors grand energy infrastructure investments, instead of a decentralized and low-cost prosumer approach capable of integrating new technological developments quicker. These are a obstacles in making the Energy Transition efficient.

Furthermore, emerging economic approaches such as the Circular Economy, and in particular the narrative in which they are embedded, were discussed. The panelist deliberated how the Circular Economy has to break-out of an engineering and material closed-loop approach, and instead integrate a whole systems perspective, such as integrating social innovation aspects in order to enable a Circular Economy system which will ensure the sustainability of initiative success.

Systems sciences have fallen into oblivion in the past decades and only recently have regained ground for being a powerful tool to navigate the complexity of current challenges. With that, the need of a reinvigorated systems sciences community is crucial to guarantee knowledge and idea exchanges regarding methodological and theoretical approaches, as well as practical applications. INX4P looks forward to future exchanges with the community.

FDM Raw Data: Why Binary Data and How to Decode It?

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Authors: Lukas Höhndorf & Javensius Sembiring (TU Munich)

SafeClouds.eu gathers 16 partners for research collaboration with a wide and diverse group of users, including air navigation services providers, airlines and safety agencies. SafeClouds.eu encourages active involvement from users, as the project aims to apply data science techniques to improve aviation safety. SafeClouds.eu is unique as it involves data combination and collaboration from ANSPs, airlines and authorities in order to improve our knowledge on safety risks, all while maintaining the confidentiality of the data. This safety analysis requires comprehensive understanding of various data sources, and supports the use case analysis as selected by the users.
The basics of the FDM data, as one of the main data sources for the project, is outlined in this post.

Onboard Recording

A large amount of data is recorded during civil aircraft flights. Apart from the “Flight Data Recorder” that is mainly used for accident investigations (widely known as “Black Box”), there are also recorders for regular operations. These recorders are often called “Quick Access Recorders” (QAR). QAR data is analysed in terms of safety, efficiency and other aspects in Flight Data Monitoring activities for airlines and is furthermore an integral part of the research project SafeClouds.eu.
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Figure 1: Example for a QAR (Source: https://www.safran-electronics-defense.com/aerospace/commercial-aircraft/information-system/aircraft-condition-monitoring-system-acms)

Aircraft are very complex systems with a large number of sensors constantly recording measurements. Important parameters regarding the aircraft state, including position, altitude, speed, engine characteristics and many others are recorded by the QAR. Depending on the aircraft type and airline, the number of recorded parameters can reach several thousand.

As a digital device, the recording uses binary format. In other words, if we look at the QAR data we would only see a bit stream, i.e. a sequence of 0 and 1. In order to use the data and investigate, for example the aircraft position, two additional components are necessary. First, logic is needed to determine how the data is written into the bit stream. This is given by an ARINC standard and two versions are presently used: ARINC 717 standard is used for older aircraft types and the ARINC 767 is used for newer aircraft types. Second, a detailed description of the location of any considered parameter in the bit stream is needed. This is given by a “dataframe” which is a text document of up to several hundreds of pages.

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Figure 2: Overview (Source: “Flight Data Decoding used for Generating En-Route Information based on Binary Quick Access Recorder Data”, Master thesis, Nils Mohr, Technical University of Munich)

File Formats

One of the advantages of data stored in binary format is storage efficiency. The size of the same flight data file stored in binary format compared to being stored in engineering values (e.g. in a CSV file) might be ten times smaller. Considering the research project SafeClouds.eu or the shared framework for flight data such as ASIAS of the FAA, FDX of IATA or Data4Safety of EASA which collects millions of flight data, an efficient storage is obviously needed.

However, storing flight data in binary format then requires an efficient way to transfer the binary data into engineering values. Considering the bit stream logic, two parts are necessary. First, the bit stream logic (provided by the ARINC standard) needs to be represented in a decoding algorithm. Second, the dataframe information, i.e. which parameter can be found in which part of the bit stream needs to be accessible to the decoding algorithm.

Decoding

Recorded parameters have different characteristics. For example, they can be numeric, alphanumeric or characters. Depending on these characteristics, different decoding rules have to be applied. As an example, a temperature recording of 36.5 °C with a linear conversion rule is considered in the following figure.

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Figure 3: Simple Decoding Example (Source: “Flight Data Decoding used for Generating En-Route Information based on Binary Quick Access Recorder Data”, Master thesis, Nils Mohr, Technical University of Munich)

Starting from the bit stream, just specific binary values are relevant for the temperature recording. As mentioned above, this information can be found in the dataframe. The combination of all bits leads to a number in the binary system, which can then be transferred into the associated decimal value. Applying the conversion rule for linear parameters gives the result 36.5. Information about these rules as well as the unit, in this case degree Celsius, can be found in the dataframe.

Summary

The data that is recorded by civilian aircraft in their daily operation contains valuable information that can be used for airline safety analyses. Due to the nature of the recording, the data is generated in binary format. To make the data accessible and readable for the analysts, a decoding algorithm is applied. For the development of this algorithm, information about the recording logic and for all the considered parameters must be available.

Author: Lukas Höhndorf (TU Munich)

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