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Co-funded by the European Commission
Horizon 2020 - Grant # 780792
Digital Transformation in Aeronautics
through the ICARUS Aviation Data and
Intelligence Marketplace
Fenareti Lampathaki, Michele Sesana, Dimitris Alexandrou
Aviation-driven Data Value Chain for Diversified Global and Local Operations
ICT-14-2017: Big Data PPP: cross-sectorial and cross-lingual data integration and experimentation
9th EASN Conference, Athens, September 3rd, 2019
http://www.icarus2020.aero
Digital Transformation & Data Science Wave…
Digital technologies can boost A&D companies’ revenue
by 5 to 15% and lower their costs by 5 to 10%.
Source: https://www.ey.com/en_gl/aerospace-defense/how-digital-
technologies-are-transforming-aerospace-and-defense
5 major forces fueling digital transformation in A&D:
• Enhancing customer experience and involvement.
• Redefining products and services.
• Digitalizing business operations.
• Developing workforce of the future.
• Building a value chain ecosystem.
Source: https://www.mckinsey.com/industries/aerospace-and-defense/our-
insights/five-keys-to-digitizing-aerospace-and-defense-companies
The aviation ecosystem is almost unanimously invested in
some way into a digital transformation strategy, with 58%
of airlines and 35% of airports already having a strategy in
place
Source: SITA Air Transport IT Trends Insights 2018
Aviation-driven Data Value Chain for Diversified Global and Local Operations
Topic: ICT-14-2016-2017 Big Data PPP: cross-sectorial and cross-lingual data integration
and experimentation
Start Date
01/01/2018
36 Months
Innovation Action
Summary ICARUS Facts
11 Partners
4Demonstrators
5Countries
Key Challenges within and beyond the
scope of ICARUS
Who owns and controls the data?
How to trust and enforce data sharing contracts on a blockchain instead of traditional
bilateral agreements?
How to address the cold-start problem?
How to promote data standardization among highly fragmented data and actors?
How to facilitate data integration in a flexible manner for business users who lack the
required expertise and resources?
How to generalize algorithms?
How to avoid any bias imposed by the training data?
How to ensure interpretability of the AI results?
How to lower the barrier entry for small companies in the aviation value chain?
I. Trusted and Fair Data Sharing
II. Affordable and Cost-efficient Data Linking
III. Insightful and Understandable Data Analytics
Aviation Data Value Chain
UAV Operators
Ground Handlers
OEM Manufacturers
Helicopters Operators
Cargo Handling Agents
Air Traffic Control
Aircraft Leasing Companies
Aviation Authorities
UAV Operators
Aviation Data Aggregators
Air Navigation Services
Maintenance, Repair &
Overhaul Providers
Ground Service Equipment
Providers
Concessionaires
& Contractors
Parking Operators
Catering Suppliers
OEM Suppliers
Aviation Data Sharing at Ecosystem Level
1stTier:CoreAviation
Stakeholders
Airports
Airlines
Global Distribution Service
Providers
2ndTier:Extra-Aviation
Stakeholders
AviationDataSharingatGlobalLevel
3rd Tier: Aviation-
related Stakeholders
TravelAgencies&Operators
HealthOrganizations
EnvironmentalInstitutions
Transportation
Organizations
CarRentalCompanies
PrivateSecurity
Companies
PublicAuthorities
What is our approach about?
End-to-end data security allowing to encrypt and check-in data through an on-
premise environment
Trusted data sharing for creating, signing and validating smart data contracts in an
immutable manner to acquire aviation-related data,
Advanced access control to regulate access to the privately owned data assets
through declarative authorization policies,
Secure and private analytics space for designing and executing analytics and
“applications” in private sandbox environments, spawn on demand,
Intuitive data exploration in order to find, understand and explore aviation-related
data,
Effortless data linking that aims at curating, mapping and linking the privately
owned data assets with external data based on a common data model
October 2, 2019 Periodic Review Meeting, Luxembourg 6
Architecture
October 2, 2019 9th EASN Conference, Athens, 2019 7
“Data”-driven Methods, Models and Workflows
Data Collection Data Provenance Data Access
Control
Data Encryption
Data
Anonymization
Data Curation Data Mapping &
Linking
Data Analytics
Data Policy & IPR Data Sharing ICARUS Metadata
Schema
ICARUS Aviation
Data Model
Data Sharing Background
October 2, 2019 9th EASN Conference, Athens, 2019 9
❑ Open Data
❑ Sharing Motivation
❑ Data Sharing Agreement
Attributes:
– Licenses, IPR
– Privacy & Protection
– Access
– Responsibility
– Regulatory Compliance
– Pricing
– Quality
– Accuracy
– …
❑ Data & DaaS
❑ Buyer-Seller Matching:
– One-One
– One-Many
– Many-One
– Many-Many
❑ Automation Levels & Usability
❑ Contract Engines
❑ Model Complexity vs
Expressivity
❑ Distributed Ledger Technologies
– Transparency
– Single point of failure
elimination
– Security
– Traceability
❑ Smart Contracts
❑ FDX – Flight Data Exchange
❑ ASIAS - Aviation Safety
Information Analysis and Sharing
❑ GADM - IATA Global Aviation
Data Management Program
❑ STEADES - IATA’s aviation safety
incident data management and
analysis program
❑ SKYbrary - safety knowledge
related to flight operations, ATM
and aviation safety
❑ Data4Safety
❑ A-CDM - Airport Collaborative
Decision Making
❑ SkyFusion – IATA & HARRIS
partnership
❑ Skywise
❑ …
General Overview Data Marketplaces Aviation Initiatives
Data Asset Sharing
October 2, 2019 9th EASN Conference, Athens, 2019 10
High-Level Data Asset Sharing Model
Attribute Description & exemplary values
asset id hash
unique identification of the asset in ICARUS – hashed to avoid being in any
any way exposed
asset filters
any evaluatable filter on the asset. e.g. In the case of data assets, this could
include spatiotemporal coverage based on specific asset columns/fields
asset fields
applicable in data assets only, includes the fields of the ICARUS common
data model that should be present in the dataset
validation date timestamp when the contract was validated
duration contract duration exprssed as dates range
provider the id of the asset provider (ethereum address)
consumer the id of the asset consumer(ethereum address)
free terms hash hash of the contract part written in natural language
Policy Category Terms Scope Exemplary values
Pricing
cost calculation scheme fixed per row | fixed per asset | request dependent
amount amount in euro | available upon request
payment method
credit/debit card| bank transfer| online payment services |
other
Responsibility
copyright ownership owner of asset
addressed to individual | group | legal entity
liability & indemnification
custom clauses (included in the natural language textual part of
the contract if needed)
Rights & Usage
license custom | CC | CDLA | Open Data Commons | ...
derivation modify (Y|N) | excerpt (Y|N) | annotate (Y|N) | aggregate (Y|N)
attribution required | not required
reproduction allowed | prohibited
distribution allowed | prohibited
target purpose business | academic | scientific | personal | non-profit
target industry limited to Aviation | excluding Aviation | all
online storage allowed | prohibited
re-context allowed | prohibited
Privacy &
Protection
privacy & sensitivity
compliance
custom clauses (included in the natural language textual part of
the contract if needed)
liability
custom clauses (included in the natural language textual part of
the contract if needed)
applicable law
custom clauses (included in the natural language textual part of
the contract if needed)
Data Analytics
No Algorithm Name Algorithmic Family/Type
Axes I: Basic Analytics
1 Summary Statistics (mean, std, etc.) Statistical Analysis
2 Hypothesis Testing Statistical Analysis
3 Sampling Statistical Analysis
4 Pearson’s and Spearman’s Correlation Feature Correlation
5 Linear Regression methods Feature Correlation, Regression Analysis
6 Logistic Regression Feature Correlation, Regression Analysis, Classification
7 Principal component analysis (PCA) Dimensionality reduction, Feature extraction
8 Feature Selection Dimensionality reduction
9 Autoregressive Integrated Moving
Average (ARIMA)
Time series prediction
Axes II: Machine Learning Algorithms
1 Self-Organising Map (SOM) Clustering, Dimensionality Reduction
2 K-means Clustering, Anomaly Detection
3 Streaming K-means Clustering, Anomaly Detection
4 DBSCAN Clustering, Anomaly Detection
5 Gaussian Mixture models Clustering
6 Apriori Association Rules
7 Collaborative Filtering (CF) Recommendation Systems
8 Content-based Filtering (CBF) Recommendation Systems
9 Support Vector Machines (SVM) Classification, Regression, Outlier detection
10 Classification and Regression Tree (CART) Classification, Regression
11 Random Forest (RF) Classification, Regression, Outlier detection
12 Gradient Boosting Machines (GBM) Classification, Regression
13 K-NN Classification, Regression, Outlier detection
14 Naïve Bayes (NB) Classification
15 Multi-Layer Perceptron (MLP) Classification, Regression, Time Series Prediction
16 Adaptive Neuro-Fuzzy Inference System
(ANFIS)
Classification, Regression,
Time Series Prediction
17 Genetic Algorithms (GA) Optimisation
Axes III: Deep Learning
1 Deep Feedforward Networks (DFFN) Classification, Regression, Deep Learning
2 Convolutional Neural Networks (CNN) Classification, Regression, Deep Learning
3 Recurrent Neural Networks (RNN) Classification, Regression, Time Series Prediction, Deep
Learning
4 Deep Autoencoders Dimensionality Reduction, Clustering, Data visualisation,
Feature Learning, Deep Learning
5 Deep Q-Networks (DQN) Reinforcement Learning, Deep Learning
Axes III: Visual Analytics
October 2, 2019 9th EASN Conference, Athens, 2019 12
State-of-art in Data Analytics in Aviation
Data Analytics Approach
Data Analytics Algorithms prioritized
ICARUS restricted beta release online!
(https://platform.icarus2020.aero)
October 2, 2019 Periodic Review Meeting, Luxembourg 13
Conclusions
Technology Innovation through Data Platforms is accelerating digital
transformation…
Digital transformation is all about asking new questions and leveraging the
“data” goldmine!
How to apply Digital Transformation?
October 2, 2019 9th EASN Conference, Athens, 2019 14
Swim in the lake with caution !
Access to the ICARUS beta platform to become
available to interested users in October 2019!
Are you interested to join?
Co-funded by the European Commission
Horizon 2020 - Grant # 780792
Thanks for your attention !
Aviation-driven Data Value Chain for Diversified Global and Local Operations
ICT-14-2017: Big Data PPP: cross-sectorial and cross-lingual data integration and experimentation
Dr. Fenareti Lampathaki (Suite5)
fenareti@suite5.eu
9th EASN Conference, Athens, September 3rd, 2019
http://www.icarus2020.aero

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ICARUS @EASN 2019 - Industry 4.0 in Aeronautics Session (September 2019, Athens)

  • 1. Co-funded by the European Commission Horizon 2020 - Grant # 780792 Digital Transformation in Aeronautics through the ICARUS Aviation Data and Intelligence Marketplace Fenareti Lampathaki, Michele Sesana, Dimitris Alexandrou Aviation-driven Data Value Chain for Diversified Global and Local Operations ICT-14-2017: Big Data PPP: cross-sectorial and cross-lingual data integration and experimentation 9th EASN Conference, Athens, September 3rd, 2019 http://www.icarus2020.aero
  • 2. Digital Transformation & Data Science Wave… Digital technologies can boost A&D companies’ revenue by 5 to 15% and lower their costs by 5 to 10%. Source: https://www.ey.com/en_gl/aerospace-defense/how-digital- technologies-are-transforming-aerospace-and-defense 5 major forces fueling digital transformation in A&D: • Enhancing customer experience and involvement. • Redefining products and services. • Digitalizing business operations. • Developing workforce of the future. • Building a value chain ecosystem. Source: https://www.mckinsey.com/industries/aerospace-and-defense/our- insights/five-keys-to-digitizing-aerospace-and-defense-companies The aviation ecosystem is almost unanimously invested in some way into a digital transformation strategy, with 58% of airlines and 35% of airports already having a strategy in place Source: SITA Air Transport IT Trends Insights 2018
  • 3. Aviation-driven Data Value Chain for Diversified Global and Local Operations Topic: ICT-14-2016-2017 Big Data PPP: cross-sectorial and cross-lingual data integration and experimentation Start Date 01/01/2018 36 Months Innovation Action Summary ICARUS Facts 11 Partners 4Demonstrators 5Countries
  • 4. Key Challenges within and beyond the scope of ICARUS Who owns and controls the data? How to trust and enforce data sharing contracts on a blockchain instead of traditional bilateral agreements? How to address the cold-start problem? How to promote data standardization among highly fragmented data and actors? How to facilitate data integration in a flexible manner for business users who lack the required expertise and resources? How to generalize algorithms? How to avoid any bias imposed by the training data? How to ensure interpretability of the AI results? How to lower the barrier entry for small companies in the aviation value chain? I. Trusted and Fair Data Sharing II. Affordable and Cost-efficient Data Linking III. Insightful and Understandable Data Analytics
  • 5. Aviation Data Value Chain UAV Operators Ground Handlers OEM Manufacturers Helicopters Operators Cargo Handling Agents Air Traffic Control Aircraft Leasing Companies Aviation Authorities UAV Operators Aviation Data Aggregators Air Navigation Services Maintenance, Repair & Overhaul Providers Ground Service Equipment Providers Concessionaires & Contractors Parking Operators Catering Suppliers OEM Suppliers Aviation Data Sharing at Ecosystem Level 1stTier:CoreAviation Stakeholders Airports Airlines Global Distribution Service Providers 2ndTier:Extra-Aviation Stakeholders AviationDataSharingatGlobalLevel 3rd Tier: Aviation- related Stakeholders TravelAgencies&Operators HealthOrganizations EnvironmentalInstitutions Transportation Organizations CarRentalCompanies PrivateSecurity Companies PublicAuthorities
  • 6. What is our approach about? End-to-end data security allowing to encrypt and check-in data through an on- premise environment Trusted data sharing for creating, signing and validating smart data contracts in an immutable manner to acquire aviation-related data, Advanced access control to regulate access to the privately owned data assets through declarative authorization policies, Secure and private analytics space for designing and executing analytics and “applications” in private sandbox environments, spawn on demand, Intuitive data exploration in order to find, understand and explore aviation-related data, Effortless data linking that aims at curating, mapping and linking the privately owned data assets with external data based on a common data model October 2, 2019 Periodic Review Meeting, Luxembourg 6
  • 7. Architecture October 2, 2019 9th EASN Conference, Athens, 2019 7
  • 8. “Data”-driven Methods, Models and Workflows Data Collection Data Provenance Data Access Control Data Encryption Data Anonymization Data Curation Data Mapping & Linking Data Analytics Data Policy & IPR Data Sharing ICARUS Metadata Schema ICARUS Aviation Data Model
  • 9. Data Sharing Background October 2, 2019 9th EASN Conference, Athens, 2019 9 ❑ Open Data ❑ Sharing Motivation ❑ Data Sharing Agreement Attributes: – Licenses, IPR – Privacy & Protection – Access – Responsibility – Regulatory Compliance – Pricing – Quality – Accuracy – … ❑ Data & DaaS ❑ Buyer-Seller Matching: – One-One – One-Many – Many-One – Many-Many ❑ Automation Levels & Usability ❑ Contract Engines ❑ Model Complexity vs Expressivity ❑ Distributed Ledger Technologies – Transparency – Single point of failure elimination – Security – Traceability ❑ Smart Contracts ❑ FDX – Flight Data Exchange ❑ ASIAS - Aviation Safety Information Analysis and Sharing ❑ GADM - IATA Global Aviation Data Management Program ❑ STEADES - IATA’s aviation safety incident data management and analysis program ❑ SKYbrary - safety knowledge related to flight operations, ATM and aviation safety ❑ Data4Safety ❑ A-CDM - Airport Collaborative Decision Making ❑ SkyFusion – IATA & HARRIS partnership ❑ Skywise ❑ … General Overview Data Marketplaces Aviation Initiatives
  • 10. Data Asset Sharing October 2, 2019 9th EASN Conference, Athens, 2019 10
  • 11. High-Level Data Asset Sharing Model Attribute Description & exemplary values asset id hash unique identification of the asset in ICARUS – hashed to avoid being in any any way exposed asset filters any evaluatable filter on the asset. e.g. In the case of data assets, this could include spatiotemporal coverage based on specific asset columns/fields asset fields applicable in data assets only, includes the fields of the ICARUS common data model that should be present in the dataset validation date timestamp when the contract was validated duration contract duration exprssed as dates range provider the id of the asset provider (ethereum address) consumer the id of the asset consumer(ethereum address) free terms hash hash of the contract part written in natural language Policy Category Terms Scope Exemplary values Pricing cost calculation scheme fixed per row | fixed per asset | request dependent amount amount in euro | available upon request payment method credit/debit card| bank transfer| online payment services | other Responsibility copyright ownership owner of asset addressed to individual | group | legal entity liability & indemnification custom clauses (included in the natural language textual part of the contract if needed) Rights & Usage license custom | CC | CDLA | Open Data Commons | ... derivation modify (Y|N) | excerpt (Y|N) | annotate (Y|N) | aggregate (Y|N) attribution required | not required reproduction allowed | prohibited distribution allowed | prohibited target purpose business | academic | scientific | personal | non-profit target industry limited to Aviation | excluding Aviation | all online storage allowed | prohibited re-context allowed | prohibited Privacy & Protection privacy & sensitivity compliance custom clauses (included in the natural language textual part of the contract if needed) liability custom clauses (included in the natural language textual part of the contract if needed) applicable law custom clauses (included in the natural language textual part of the contract if needed)
  • 12. Data Analytics No Algorithm Name Algorithmic Family/Type Axes I: Basic Analytics 1 Summary Statistics (mean, std, etc.) Statistical Analysis 2 Hypothesis Testing Statistical Analysis 3 Sampling Statistical Analysis 4 Pearson’s and Spearman’s Correlation Feature Correlation 5 Linear Regression methods Feature Correlation, Regression Analysis 6 Logistic Regression Feature Correlation, Regression Analysis, Classification 7 Principal component analysis (PCA) Dimensionality reduction, Feature extraction 8 Feature Selection Dimensionality reduction 9 Autoregressive Integrated Moving Average (ARIMA) Time series prediction Axes II: Machine Learning Algorithms 1 Self-Organising Map (SOM) Clustering, Dimensionality Reduction 2 K-means Clustering, Anomaly Detection 3 Streaming K-means Clustering, Anomaly Detection 4 DBSCAN Clustering, Anomaly Detection 5 Gaussian Mixture models Clustering 6 Apriori Association Rules 7 Collaborative Filtering (CF) Recommendation Systems 8 Content-based Filtering (CBF) Recommendation Systems 9 Support Vector Machines (SVM) Classification, Regression, Outlier detection 10 Classification and Regression Tree (CART) Classification, Regression 11 Random Forest (RF) Classification, Regression, Outlier detection 12 Gradient Boosting Machines (GBM) Classification, Regression 13 K-NN Classification, Regression, Outlier detection 14 Naïve Bayes (NB) Classification 15 Multi-Layer Perceptron (MLP) Classification, Regression, Time Series Prediction 16 Adaptive Neuro-Fuzzy Inference System (ANFIS) Classification, Regression, Time Series Prediction 17 Genetic Algorithms (GA) Optimisation Axes III: Deep Learning 1 Deep Feedforward Networks (DFFN) Classification, Regression, Deep Learning 2 Convolutional Neural Networks (CNN) Classification, Regression, Deep Learning 3 Recurrent Neural Networks (RNN) Classification, Regression, Time Series Prediction, Deep Learning 4 Deep Autoencoders Dimensionality Reduction, Clustering, Data visualisation, Feature Learning, Deep Learning 5 Deep Q-Networks (DQN) Reinforcement Learning, Deep Learning Axes III: Visual Analytics October 2, 2019 9th EASN Conference, Athens, 2019 12 State-of-art in Data Analytics in Aviation Data Analytics Approach Data Analytics Algorithms prioritized
  • 13. ICARUS restricted beta release online! (https://platform.icarus2020.aero) October 2, 2019 Periodic Review Meeting, Luxembourg 13
  • 14. Conclusions Technology Innovation through Data Platforms is accelerating digital transformation… Digital transformation is all about asking new questions and leveraging the “data” goldmine! How to apply Digital Transformation? October 2, 2019 9th EASN Conference, Athens, 2019 14 Swim in the lake with caution ! Access to the ICARUS beta platform to become available to interested users in October 2019! Are you interested to join?
  • 15. Co-funded by the European Commission Horizon 2020 - Grant # 780792 Thanks for your attention ! Aviation-driven Data Value Chain for Diversified Global and Local Operations ICT-14-2017: Big Data PPP: cross-sectorial and cross-lingual data integration and experimentation Dr. Fenareti Lampathaki (Suite5) fenareti@suite5.eu 9th EASN Conference, Athens, September 3rd, 2019 http://www.icarus2020.aero