SafeClouds presented in the EU-US workshop

Last January, a team of European and American entities organised a workshop on transatlantic research with the support of the European Commission. The event was hosted by the FAA in their facilities at the William J. Hughes Technical Center in Atlantic City. Those mostly in attendance were US and European companies interested in how the different research threads could be boosted through international cooperation.

Among the subjects discussed during the three day event, data analytics was mentioned several times as a interesting area with applicability to different areas in industrial research. Particularly, safety data analytics was covered in three presentations. First, the FAA presented their +10-year old programme ASIAS, which collects data from more than 40 carriers and has been leading the developments in this field for more than a decade. Second, EASA presented the Data4Safety programme, recently launched and in a proof-of-concept stage. Lastly, Innaxis presented the research programme SafeClouds.eu, including the latest technological developments and how they could complement the existing initiatives by providing and exploring new research avenues.

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.

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.

 

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)

DATASET2050 presentation at Data Science in Aviation Workshop (EASA, Cologne-Germany)

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The annual event exploring Data Science in Aviation (ComplexWorld funded; organized by Innaxis) has recently celebrated its fourth edition this past September 8th 2016. The event was hosted on the EASA premises in Cologne, Germany . This year it highlighted a presentation of the DATASET2050 project, “Data Science for Mobility”, by project coordinator Samuel Cristobal (Innaxis).

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On the Data Science In Aviation event:

Previous editions of the Data Science in Aviation event were hosted in Madrid 2013, Paris 2014 and Brussels 2015. The popular event usually draws attendance from more than 80 individuals from top European and worldwide aviation entities (including Airbus, Eurocontrol, Boeing, EASA, Airlines, Airports, ANSPs, SESAR , Universities, etc) along with ICT and data-related entities (including CERN researchers, Fraunhofer, Infrastructure-related, and various universities). Notable presenters from the 2016 edition included EASA, Innaxis, NATS, Eurocontrol, Boeing, ENAC and Fraunhofer.

In terms of the event agenda and content, the presentations has traditionally outlined how data science is understood as a useful set of fundamental principles that support and guide the principled extraction of information and knowledge from aviation data. Furthermore, the discipline leans on well-known data-mining techniques, and goes far beyond these techniques with successful data-science paradigms which provide specific applications in various air transport areas (safety, performance, mobility etc).

 

On DATASET2050 Samuel’s presentation:

The event also highlighted a key presentation from Innaxis project coordinator, Samuel Cristobal. Samuel presented five different points on data science in aviation.

  • First, he explained how some of the data science tools, techniques and concepts have been used in the mobility context, specifically using the DATASET2050 project as a case example.
  • Second, Samuel explained the different door to door phases under analysis (door-kerb; kerb-gate; gate-gate; kerb-door), which helps to delve deeper in the different data science components within aviation phases.
  • Third, Samuel outlined the different links between project objectives and overall Flightpath2050 goals.
  • The fourth point explored mobility data in Europe, and the value of the DATASET approach in this context.
  • The presentation concluded with a fifth and final point announcing the next communication actions. The full presentation can be accessed here: https://www.dropbox.com/s/91julyl8gsij2k9/DATASET_SC_v1.pdf?dl=0

 

In sum, the fourth edition of the Data Science in Aviation event was an excellent opportunity for dissemination of DATASET2050. This was in conjunction with a fruitful exchange of ideas with other aviation data scientists, some of whom working with similar tools in other sub-areas far from mobility. We hope to continue this momentum of knowledge exchange and look forward to a potential fifth edition of the popular event.

 

You can watch DATASET2050 Samuel’s presentation here, and the rest of the event videos at Innaxis’ Vimeo channel.

Mobility presentation at Data Science In Aviation workshop (EASA, 2016)

The annual event exploring Data Science in Aviation (ComplexWorld funded; organized by Innaxis) has recently celebrated its fourth edition this past September 8th 2016. The event was hosted on the EASA premises in Cologne, Germany . This year it highlighted a presentation of the DATASET2050 project, “Data Science for Mobility”, by project coordinator Samuel Cristobal (Innaxis).

 

 

On the Data Science In Aviation event:

Previous editions of the Data Science in Aviation event were hosted in Madrid 2013, Paris 2014 and Brussels 2015. The popular event usually draws attendance from more than 80 individuals from top European and worldwide aviation entities (including Airbus, Eurocontrol, Boeing, EASA, Airlines, Airports, ANSPs, SESAR , Universities, etc) along with ICT and data-related entities (including CERN researchers, Fraunhofer, Infrastructure-related, and various universities). Notable presenters from the 2016 edition included EASA, Innaxis, NATS, Eurocontrol, Boeing, ENAC and Fraunhofer.

In terms of the event agenda and content, the presentations has traditionally outlined how data science is understood as a useful set of fundamental principles that support and guide the principled extraction of information and knowledge from aviation data. Furthermore, the discipline leans on well-known data-mining techniques, and goes far beyond these techniques with successful data-science paradigms which provide specific applications in various air transport areas (safety, performance, mobility etc).

 

On DATASET2050 Samuel’s presentation:

The event also highlighted a key presentation from Innaxis project coordinator, Samuel Cristobal. Samuel presented five different points on data science in aviation.

  • First, he explained how some of the data science tools, techniques and concepts have been used in the mobility context, specifically using the DATASET2050 project as a case example.
  • Second, Samuel explained the different door to door phases under analysis (door-kerb; kerb-gate; gate-gate; kerb-door), which helps to delve deeper in the different data science components within aviation phases.
  • Third, Samuel outlined the different links between project objectives and overall Flightpath2050 goals.
  • The fourth point explored mobility data in Europe, and the value of the DATASET approach in this context.
  • The presentation concluded with a fifth and final point announcing the next communication actions. The full presentation can be accessed here: https://www.dropbox.com/s/91julyl8gsij2k9/DATASET_SC_v1.pdf?dl=0

 

In sum, the fourth edition of the Data Science in Aviation event was an excellent opportunity for dissemination of DATASET2050. This was in conjunction with a fruitful exchange of ideas with other aviation data scientists, some of whom working with similar tools in other sub-areas far from mobility. We hope to continue this momentum of knowledge exchange and look forward to a potential fifth edition of the popular event.

Information, time, knowledge

We live in a world that gathers exponentially increasing amounts of information/data coming from endless sources, and a limited time to analyse it.

What is the current speed of “creating” information/data? What about knowledge/wisdom? What is the role of Data Science and Big Data in this context?

Food for thought for your -deserved- summer break! Enjoy, charge your batteries and get ready for a 2016/2017 year full of cutting-edge research, innovation (and Innaxis blogposts!)

 

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ComplexWorld: Linking Complexity and Data Science in ATM

 

This year we celebrated the 5th anniversary of ComplexWorld. As we reflect over the years to 2011, when Innaxis first launched the network, it’s easy to find many reasons to feel proud and grateful of our partners and participants of the network. ComplexWorld emerged from the idea of applying complexity science techniques to better understand the Air Traffic Management behaviour and the relationships among its different agents. At the network’s inception, it was considered a new and unfamiliar concept, but promising nonetheless. Now the concept has become a reality, after fruitful 5 years of ComplexWorld network development, along with 8 PhDs and 10 projects. The number of references in the field has increased significantly since 2011. To illustrate, the first graph below shows the growth of the number of papers published including the text “air transport” and “complex networks” since 2005. The second graph represents the number of those papers corresponding citations. (Data source: Thomson Reuters’ Web of Science)

 

 

In order to provide some direction to the purpose of the ComplexWorld network and specifically, the analysis of the air transport network as a complex system, the ComplexWorld partners Innaxis, University of Seville, University of Westminster, University of Palermo, along with NLR and DLR, identified 5 research challenges in which complexity science could provide a completely new perspective and deeper understanding of system performance. Those challenges include: resilience, metrics, emergent behaviour, data science and uncertainty, which have been our research pillars for the ComplexWorld network; enabling significant progress in those fields, previously insignificantly addressed by traditional and classical models. This the nexus of complexity science and air traffic management has garnered so much attention that soon a book will be released for the public, published under the title, “Complexity Science in air traffic management”. If you cannot wait to have it on your hands, you are in luck! The book is now available on Amazon.

Through the evolution of research within these five pillars, a key insight emerged that drove a conviction: data science was not merely one of the five pillars, but rather the key pillar that would foster the most significant and efficient progress within the other four areas. However, the aviation sector was not fully prepared to move forward quickly with the application of data science techniques as challenges related to data confidentiality, data sharing, and lack of appropriate data management infrastructures presented barriers for advancement. Therefore, with the objective of eliminating or reducing these barriers, in 2013 ComplexWorld organized the first Data Science in Aviation Workshop (DSIAW). The aim was to bring together aviation stakeholders willing to extract knowledge from their available data, with data scientists and experts from other sectors assisting by demonstrating the potential of data science with real examples of ongoing initiatives and recent work. The event was a complete success in terms of invited speaker expertise, but more importantly, the event was outstanding in terms of audience engagement, so much so that DSIAW has became an annual workshop thanks to the support of Eurocontrol and the SJU.

Year after year we have enthusiastically worked to bring relevant experts together to present their work on the application of data science techniques to enable an improvement on their business performance. Furthermore, we feel this sector is moving in the right direction as we see the number of success cases in this sector grow significantly. This year, we are organizing the 4th edition of the DSIAW, which will be celebrated September 8th and 9th at EASA HQ in Cologne. The workshop will be opened by Mr. Luc Tytgat, EASA Strategy and Safety Management Director, and will include presentations about different data science applications, including:

  • Air navigation (UK NATS and Eurocontrol-MUAC),
  • Aviation safety (Innaxis and AESA – Spanish Aviation Safety Agency),
  • Mobility (Boeing and Innaxis)
  • Infrastructure and visualization (Fraunhofer ITWM and ENAC)

Registration is now open so you are invited to join us and participate in the debates. At DSIAW you will also have the opportunity of attending the presentation of the “Complexity Challenges” report, a report in which we have developed in the framework of SESAR Exploratory Research along with 18 external experts who have provided their assessment on how the complexity challenges have been addressed by the different ComplexWorld related activities within these last five years, and the existing gaps and opportunities for future research in the field. We highly encourage you to attend this 1.5 days event and to be an active part of the definition of future research lines in the field of complexity in air transport. If you cannot attend, videos of the event will be uploaded to our vimeo channel and wiki, where you will catch some of the unprecedented conversations and become an active participant in the dialogues.

 

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Complex networks, data mining, causality, and beyond

Over the last few weeks Innaxis has published two papers that may be of interest to air transport researchers, among others.

The first paper is an extensive review on the combined use of complex network theory and data mining. Not only do complex network analysis and data mining share the same goal in general- that of extracting information from complex systems to ultimately create a new compact quantifiable representation- but they also often address similar problems as well. Despite these commonalities, a surprisingly low number of researchers take advantage of methodologies, as many conclude that these two fields are either largely redundant or totally antithetic. In this review, we challenge this perception, show how this state of affairs should be relegated to contingent rather than conceptual differences, and that these two fields can in fact advantageously be used in a synergistic manner. The review starts by presenting an overview of both fields, and by illustrating some of their fundamental concepts. A variety of contexts in which complex network theory and data mining have been used in a synergistic manner are then presented. Finally, all discussed concepts are illustrated with worked examples through a series of hands-on sections, which we hope will help the reader to put these ideas in practice. If you ever wonder how a real-world problem can be tackled by these two techniques, you should definitively read this review!

 

 

The second paper addresses the common misinterpretation of correlation vs causality. Following this idea, many causality metrics have been proposed in the literature, all sharing a same drawback: they are defined for time series. In other words, the system (or systems) under analysis should display a time evolution. Associating causality to the temporal domain is intuitive, due to the way the human brain incorporates time into our perception of causality; nevertheless, such association results in some rather important problems.

For instance, suppose one is trying to detect if there is a causality relation between the workload of an ATC controller and the appearance of loss of separation events. These events are only defined at one point in time. To illustrate, one can detect an instance of a loss of separation and check the corresponding workload; afterwards, perform the same actions for another event; and so forth. In the end, the researcher would get two vectors of features, which do not encode any temporal evolutions – in other words, consecutive values are not correlated. So, in this situation, how can we detect if a true causality (and not just a correlation) is present?

In this paper we propose a novel metric able to detect causality within static data sets, by analysing how extreme events in one element correspond to the appearance of extreme events in a second element- refer to the picture above for a graphical representation. The metric is able to detect non-linear causalities, to analyse both cross-sectional and longitudinal data sets, and to discriminate between real causalities and correlations caused by confounding factors.

If you are interested in these ideas, feel free to have a look at these two papers:

M. Zanin et al., Combining complex networks and data mining: why and how. Physics Reports (2016), pp. 1-44. http://authors.elsevier.com/a/1T3yF_8QfbYE-k. Also available at: http://arxiv.org/abs/1604.08816
M. Zanin, On causality of extreme events. PeerJ. Also available at: http://arxiv.org/abs/1601.07054

If you have questions about them, please contact M. Zanin at mzanin@innaxis.org

Finally, Seddik Belkoura is going to present a paper at the forthcoming ICRAT 2016, Philadelphia, about the use of the static causality metric to study delay propagation. You can find the paper on the official website of the conference (http://www.icrat.org/), and also by contacting him at sb@innaxis.org.

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