The ICARUS Aviation Data Sharing and Intelligence framework was presented by Mr. Nikolaos Papagiannopoulos from Athens International Airport (AIA) at the 27th ACRIS Meeting, which was held in London on February 25th-27th, 2020.
The ICARUS aviation ontology was presented at the 10th international Conference on Web Intelligence, Mining and Semantics (WIMS'20) that was held virtually during June 30th – 3rd July in 2020.
A quick project overview provided by the ICARUS Coordinator, Dr. Dimitris Alexandrou (UBITECH) in the BDVA MeetUp that was held on May 15th, 2018 in Sofia.
Airline and Airport Big Data: Impact and EfficienciesJoshua Marks
Keynote presentation at Routes 2014 in Chicago - how big data changes aviation efficiencies, and what airlines and airports need to know about cloud data warehouses, real-time integration and predictive analytics.
The ICARUS aviation ontology was presented at the 10th international Conference on Web Intelligence, Mining and Semantics (WIMS'20) that was held virtually during June 30th – 3rd July in 2020.
A quick project overview provided by the ICARUS Coordinator, Dr. Dimitris Alexandrou (UBITECH) in the BDVA MeetUp that was held on May 15th, 2018 in Sofia.
Airline and Airport Big Data: Impact and EfficienciesJoshua Marks
Keynote presentation at Routes 2014 in Chicago - how big data changes aviation efficiencies, and what airlines and airports need to know about cloud data warehouses, real-time integration and predictive analytics.
BDE-SC1 Webinar: OpenPHACTS Re-engineered with Big Data EuropeBigData_Europe
Watch this webinar on YouTube: https://youtu.be/MwG0yhrctDs
Slides for the latest update on our Big Data Europe pilot in Societal Challenge 1: Health, Demographic Change and Wellbeing.
Last year we successfully completed the first phase of this pilot, replicating the functionality of the Open PHACTS Discovery Platform on the BDE infrastructure. The Open PHACTS Discovery Platform brings together pharmacological data resources in an integrated, interoperable infrastructure, and has been developed to reduce barriers to drug discovery for industry, academia, and small businesses.
Learn more about the progress we’ve made, and what’s coming next.
1. General overview of the Big Data Europe project and Societal Challenges it addresses (Ronald Siebes, VU Amsterdam)
2. The Big Data Europe infrastructure, generic components that are being developed, and their flexibility for different applications (Hajira Jabeen, University of Bonn)
3. Latest details of the current state of the Open PHACTS architecture in BDE, and ongoing work (Nick Lynch, CTO, Open PHACTS Foundation)
Innovations in London's Transport: Big Data for a Better Customer ServiceGovnet Events
Presentation on Innovations in London's Transport: Big Data for a Better Customer Service by Andrew Hyman, TFL at HPC and Big Data 2016 in Central London
Artificial Intelligence in Service SystemsNiklas Kühl
While there are impactful insights on how AI can solve isolated business problems, there is a gap of insights for AI applications within systems and business networks. To our current understanding, this is mostly due to IP preservation and technical issues (data volume, robustness, distributed sources). In this talk, I will highlight a few possibilities on how to tackle these barriers.
A glimpse at the ICARUS policy perspectives provided during the European Big Data Value Forum 2018, BDVA Workshop 2.3 "Policy issues, opportunities and barriers in big data-driven transport", on November 14th, 2018, in Vienna
ICARUS @EASN 2019 - Industry 4.0 in Aeronautics Session (September 2019, Athens)ICARUS2020.aero
A glimpse at the ICARUS aviation data and intelligent marketplace provided during the 9th EASN International Conference on Innovation in Aviation & Space, "Industry 4.0 in Aeronautics" Session, on September 3rd, 2019, in Athens
An overview of the ICARUS project and its trusted data brokerage framework, provided during the PRO-VE, Parallel Session C1: Collaborative Knowledge Management, on September 23rd, 2019, in Turin.
An overview of the ICARUS project provided during the European Big Data Value Forum, Parallel Session 1.3 “Transforming Transport”, on November 12th, 2018, in Vienna.
CarStream: An Industrial System of Big Data Processing for Internet of Vehiclesijtsrd
As the Internet-of-Vehicles (IoV) technology becomes an increasingly important trend for future transportation, de-signing large-scale IoV systems has become a critical task that aims to process big data uploaded by fleet vehicles and to provide data-driven services. The IoV data, especially high-frequency vehicle statuses (e.g., location, engine parameters), are characterized as large volume with a low density of value and low data quality. Such characteristics pose challenges for developing real-time applications based on such data. In this paper, we address the challenges in de-signing a scalable IoV system by describing CarStream, an industrial system of big data processing for chauffeured car services. Photon is deployed within Google Advertising System to join data streams such as web search queries and user clicks on advertisements. It produces joined logs that are used to derive key business metrics, including billing for advertisers. Our production deployment processes millions of events per minute at peak with an average end-to-end latency of less than 10 seconds. We also present challenges and solutions in maintaining large persistent state across geographically distant locations, and highlight the design principles that emerged from our experience. Rakshitha K. S | Radhika K. R"CarStream: An Industrial System of Big Data Processing for Internet of Vehicles" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14408.pdf http://www.ijtsrd.com/computer-science/database/14408/carstream-an-industrial-system-of-big-data-processing-for-internet-of-vehicles/rakshitha-k-s
BDE-SC1 Webinar: OpenPHACTS Re-engineered with Big Data EuropeBigData_Europe
Watch this webinar on YouTube: https://youtu.be/MwG0yhrctDs
Slides for the latest update on our Big Data Europe pilot in Societal Challenge 1: Health, Demographic Change and Wellbeing.
Last year we successfully completed the first phase of this pilot, replicating the functionality of the Open PHACTS Discovery Platform on the BDE infrastructure. The Open PHACTS Discovery Platform brings together pharmacological data resources in an integrated, interoperable infrastructure, and has been developed to reduce barriers to drug discovery for industry, academia, and small businesses.
Learn more about the progress we’ve made, and what’s coming next.
1. General overview of the Big Data Europe project and Societal Challenges it addresses (Ronald Siebes, VU Amsterdam)
2. The Big Data Europe infrastructure, generic components that are being developed, and their flexibility for different applications (Hajira Jabeen, University of Bonn)
3. Latest details of the current state of the Open PHACTS architecture in BDE, and ongoing work (Nick Lynch, CTO, Open PHACTS Foundation)
Innovations in London's Transport: Big Data for a Better Customer ServiceGovnet Events
Presentation on Innovations in London's Transport: Big Data for a Better Customer Service by Andrew Hyman, TFL at HPC and Big Data 2016 in Central London
Artificial Intelligence in Service SystemsNiklas Kühl
While there are impactful insights on how AI can solve isolated business problems, there is a gap of insights for AI applications within systems and business networks. To our current understanding, this is mostly due to IP preservation and technical issues (data volume, robustness, distributed sources). In this talk, I will highlight a few possibilities on how to tackle these barriers.
A glimpse at the ICARUS policy perspectives provided during the European Big Data Value Forum 2018, BDVA Workshop 2.3 "Policy issues, opportunities and barriers in big data-driven transport", on November 14th, 2018, in Vienna
ICARUS @EASN 2019 - Industry 4.0 in Aeronautics Session (September 2019, Athens)ICARUS2020.aero
A glimpse at the ICARUS aviation data and intelligent marketplace provided during the 9th EASN International Conference on Innovation in Aviation & Space, "Industry 4.0 in Aeronautics" Session, on September 3rd, 2019, in Athens
An overview of the ICARUS project and its trusted data brokerage framework, provided during the PRO-VE, Parallel Session C1: Collaborative Knowledge Management, on September 23rd, 2019, in Turin.
An overview of the ICARUS project provided during the European Big Data Value Forum, Parallel Session 1.3 “Transforming Transport”, on November 12th, 2018, in Vienna.
CarStream: An Industrial System of Big Data Processing for Internet of Vehiclesijtsrd
As the Internet-of-Vehicles (IoV) technology becomes an increasingly important trend for future transportation, de-signing large-scale IoV systems has become a critical task that aims to process big data uploaded by fleet vehicles and to provide data-driven services. The IoV data, especially high-frequency vehicle statuses (e.g., location, engine parameters), are characterized as large volume with a low density of value and low data quality. Such characteristics pose challenges for developing real-time applications based on such data. In this paper, we address the challenges in de-signing a scalable IoV system by describing CarStream, an industrial system of big data processing for chauffeured car services. Photon is deployed within Google Advertising System to join data streams such as web search queries and user clicks on advertisements. It produces joined logs that are used to derive key business metrics, including billing for advertisers. Our production deployment processes millions of events per minute at peak with an average end-to-end latency of less than 10 seconds. We also present challenges and solutions in maintaining large persistent state across geographically distant locations, and highlight the design principles that emerged from our experience. Rakshitha K. S | Radhika K. R"CarStream: An Industrial System of Big Data Processing for Internet of Vehicles" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14408.pdf http://www.ijtsrd.com/computer-science/database/14408/carstream-an-industrial-system-of-big-data-processing-for-internet-of-vehicles/rakshitha-k-s
ACI ACRIS Semantic Model An Introduction v1.6Segun Alayande
Describes a cross-industry framework based on lexical semantics that enables knowledge sharing and data integration across the different industries in the Airport ecosystem.
World Routes 2014 Keynote Presentation – How Big Date Changes Aviation Effici...pmccann1984
Big data is a hot topic for any industry, aviation is no different. Josh Marks joins us at World Routes 2014 to speak on how the industry should be equipping itself for the future by leveraging the power of data.
Keynote Presentation – How Big Date Changes Aviation Efficiency (Josh Marks, ...Routesonline
Big data is a hot topic across all industries, Josh Marks, CEO, masFlight talks to the decision makers of the air service development community at World Routes 2014 about how large corporations can work agilely and harness the power of data.
IDS CRONOS is a Dynamic AIS/AIM system capable of storing and providing, under the responsibility of an aeronautical information management system, quality-assured, traditional, digital, aeronautical information/data in a timely manner, meeting the information needs of the diverse end-users with confidence and improve the overall safety of air navigation
Every day, 50,000 flights take off, transit and land safely within US airspace. NASA Aeronautics is behind many of the technology concepts that make this possible. With drones proliferating and traffic volume rising rapidly, NASA needs a way to stay ahead of the curve. In this session, you will learn how IBM Bluemix quickens NASA's pace in air traffic management research, and hear three lessons learned from a recent NASA project using Bluemix Mobile and Bluemix Data Analytics.
Shceduling iot application on cloud computingEman Ahmed
Resource scheduling considers the execution time of every distinct workload, but most importantly, the overall performance is also based on type of workload i.e. with different QoS requirements (heterogeneous workloads) and with similar QoS requirements (homogenous workloads).
Application of Big Data Systems to Airline ManagementIJLT EMAS
The business world is in the midst of the next
revolution following the IT revolution – the Big Data revolution.
The sheer volume of data produced is a major reason for the big
data revolution. Aviation and aerospace are typical areas that
can apply big data systems due to the scale of data produced, not
only by the plane sensors and passengers, but also by the
prospective passengers. Data that need to be considered include,
but are not limited to, aircraft sensor data, passenger data,
weather data, aircraft maintenance data and air traffic data.
This paper aims at identifying areas in aviation where big data
systems can be utilized to enhance operational performances
improve customer relations and thereby aiding the ultimate goal
of increased profits at reduced costs. An improved management
model built on a strong big data infrastructure will reduce
operation costs, improve safety, bring down the cost and time
spent on maintenance and drastically improve customer
relations.
Passenger Analytics: A Better Way to Manage AirportsICF
Through passenger analytics, airports can improve their terminal efficiency for all users, at all levels, for everyone's benefit.
This infographic overviews the three steps to performance optimization through passenger analytics. Also included, are real world examples of how these steps have been applied in airports.
For more information, click here: http://bit.ly/2bfZDPc
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
1. Co-funded by the European Commission
Horizon 2020 - Grant # 780792
Aviation Data Sharing and Intelligence: How
ACRIS is leveraged in ICARUS?
Nikos Papagiannopoulos
Senior Project Manager - IT&T Business Applications and Technology, Information Technology & Telecommunications,
Athens International Airport
Dr. Fenareti Lampathaki
ICARUS Technical Coordinator, Suite5 Technical Director
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
27th ACRIS Meeting, London, February 25th-27th, 2020
http://www.icarus2020.aero
2. Aviation-driven Data Value Chain for Diversified Global and Local Operations
Horizon 2020 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
3. Key Data 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 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 promote 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
4. 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
Target Users: Data Providers & Data Consumers; Business Users & Data Analysts (without coding)
5. ICARUS in a nutshell
Trusted data sharing for creating, signing and validating smart data contracts in
an immutable manner to acquire aviation-related data,
End-to-end data security allowing to encrypt and check-in data through an on-
premise environment
Advanced access control to regulate access to the private data assets through
declarative authorization policies,
Intuitive data exploration in order to find, understand & explore aviation-
related data,
Effortless data linking that aims at curating, mapping and linking the private
data assets with external data based on a common data model
Secure and private analytics spaces for designing and executing analytics and
“applications” in private sandbox environments, spawn on demand,
4
2 CORE WORKFLOWS1 NOVEL ICARUS ASPECTS
3 ICARUS ECOSYSTEM
CORE PLATFORM
ON-PREMISE
ENVIRONMENT
SECURE & PRIVATE
SPACES
DATA CHECKIN
DATA SEARCH &
ACQUISITION
DATA ANALYTICS
ICARUS DIFFERENTIATING POINTS
6. Aviation Data Standards Landscape
Different aviation-specific data standards are available from international
standardization organizations, e.g.:
– ACRIS (ACI Airport Community Recommended Information Services),
– A-CDM (Airport Collaborative Decision Making Manual),
– AIXM (Aeronautical Information Exchange Model),
– SSIM (Standard Schedules Information Manual),
New standards are often built on top of generic purpose standards, such as
the UN/CEFACT Core Components Technical Specification (also used by ACRIS)
that promotes reusability of generic data structures, Core Components, and
industry-specific data structures, Business Information Entities that build on
Core Components.
Solving the “data standards dilemma” and selecting which standard to follow
when there are data from multiple stakeholders that model different aspects
of the aviation industry (e.g. focusing on the airport perspective, the flight
perspective or the aircraft perspective) is not a straightforward process.
4 March, 2020 27th ACRIS Meeting, London 6
7. ICARUS Common Aviation Data
Model
Built taking into consideration the modelling performed in: ACRIS, A-CDM, SSIM, AIXM,
UN/ CEFACT CCTS
Bringing together different aviation data standards rather than adopting one due to the
diverse ICARUS stakeholders (from different domains within and beyond aviation) and
the need to capture complementary information (such as if there is a need for
encryption or anonymization in the specific column) to facilitate the integration efforts
Currently containing over 620 concepts (that extend to over 5000 concepts taking into
consideration the nested concepts)
13 main entities: Aircraft, Airport, Baggage, Booking, Carrier, Crew, Flight, FlightLeg,
Fuel, Location, Passenger, Product, Weather + 1 complementary entity Provenance
Data (to keep the history of all entities)
Defined in JSON for flexibility purposes in the ICARUS platform since it contains the
following metadata per concept: "definition“, "related_terms“,
"standards", "date_added“, "date_deprecated", "version",
"children“, "facet“ (including "encryption", "multiple",
"ordered", "sensitive“, “measurementType“,
“measurementUnit“, “timezone“)
4 March, 2020 27th ACRIS Meeting, London 7
8. Benefits of Data Mapping to the
ICARUS Aviation Data Model
To address the inherent data standard incompatibility problems
To ensure that datasets that come from diverse stakeholders in the
aviation data value chain are semantically enriched, harmonized and put
to the appropriate context prior to their storage (in an encrypted or
unencrypted format)
To facilitate dataset integration within ICARUS and between ICARUS
datasets & external datasets
To make the data easier discoverable and searchable prior to their
acquisition through the ICARUS data brokerage and sharing mechanisms
To be able to search for data that span over multiple datasets - Note:
feature not yet available in the ICARUS platform
To extract the data in the preferred aviation standard (among the ones
included in the data model) - Note: feature not yet available in the ICARUS
platform
4 March, 2020 27th ACRIS Meeting, London 8
9. How Data Mapping works with the
help of the ICARUS data model?
Based on the sample a data provider has uploaded in the platform, the semi-
automatic mapping (i.e. proposal of the mapping of each column to the ICARUS
aviation data model) is calculated based on fuzzy matching and machine learning
techniques.
The proposed mapping has a certain confidence level depending on whether
there was an exact match with a concept in the ICARUS data model.
The data provider must manually confirm the proposed data mapping (or add it, if
it was not possible to be automatically calculated) and provides the
supplementary information anticipated in the ICARUS data model, e.g. the
measurement unit that which the data currently have (in order for ICARUS to make
the appropriate transformations to the baseline measurement unit of the ICARUS
aviation data model).
The data provider can suggest new concepts to the ICARUS data model that are
moderated by an administrator, if he/she cannot find the concept that their data
column(s) represent.
The administrator takes appropriate action for the data model evolution to
ensure backwards compatibility, to the extent it’s possible.
9
Model Evolution actions: PROP | PROMPT | BLOCK
11. ICARUS restricted beta release online from
April 2020! (https://platform.icarus2020.aero)
4 March, 2020 Periodic Review Meeting, Luxembourg 11
12. ICARUS Demonstrators as the alpha
platform users
Airport Capacity Planning
Pollution Data Analysis and Massive Route
Network Analysis
Aviation Related Disease Spreading -
GLEAM
Enhancing Passenger Experience
I II
III IV
13. Athens International Airport (AIA)
4 March, 2020 27th ACRIS Meeting, London 13
• An Airport city (Aerotropolis) hosting more than 370 companies with more than 16.000
employees servicing approx. 100.000 passengers and 800 flights per day during peak
periods.
• Athens International Airport is the largest airport in Greece. Current traffic is about
25,573,993 an annual increase of 6% over 2018. Total ATMs for 2019, 225,628 an annual
increase of 3.9% over 2018.
• Athens International Airport hit its first capacity threshold in 2008. In addition, before
hitting the 25 million passenger volume trigger, the airport constructing an additional
terminal, new gates, as well as improvements to the baggage system.
14. What is the interest of AIA in ICARUS?
1/2
Business Perspective: To improve the Planned Airport’s airside capacity
Using Airport data sources to gather usage data from Runway, Apron, Aircraft
Stands and Gates to enable one to understand current airfield performance
and baseline capacity.
To enable capacity enhancement decisions that are targeted appropriately to
relevant airport resources.
Analysis will include data for: the runway, considering also taxiways, aprons,
gates, terminals & local airspace usage.
Delay (actual or predicted) can be used as a KPI performance indicator that
highlights an imbalance between demand and capacity.
AIA eventually expects to strengthen its win-win collaborations with relevant
aviation stakeholders (i.e. airlines and ground handlers) through the provision
of renovated, or even new data-driven intelligence services as emerging from
its demonstrator results.
4 March, 2020 27th ACRIS Meeting, London 14
15. What is the interest of AIA in ICARUS?
2/2
Technical Perspective: To increase its readiness in the Data era
Acquire know-how in data analytics, augmenting its baseline data analysis that
is already performed in its IT infrastructure with advanced machine learning
and deep learning analytics to improve its operational efficiency.
Further embrace data-related technologies and increase its overall data
readiness in terms of data sharing
increase its data reach in an effortless manner, by finding and exploring
additional data “on-the-go” while performing or trying to improve an analysis.
Reach bilateral data sharing agreements between the airport and its relevant
parties with less effort and in less time, in order to complement its data
availability with different data assets that have been identified and could be
leveraged by the airport in scenarios beyond the airport capacity planning.
Deeper knowledge of aviation data standards like ACRIS
4 March, 2020 27th ACRIS Meeting, London 15
16. Conclusions
The ACRIS initiative holds tremendous potential for airports as it provides
from semantic models to API specifications.
The ICARUS aviation data model has leveraged the ACRIS outcomes and
has created the mappings to its Basic-Associated-Aggregate Business
Information Entities. Such mappings are used under the hood in the
ICARUS platform.
Key learning from ICARUS and AIA so far:
– A data model needs to be always “designed for change” with the purpose of efficiently
managing its whole lifecycle and effectively anticipating its consistent evolution
– Data sharing requires hard efforts and synergies between all parties in the value chain as
they are typically reluctant to expose their data to third party platforms
– Strong Data sharing agreements need to be in place in order to provide assurance to
third parties to share their data
Next steps: to establish liaisons with ACRIS (to ensure that the latest ACRIS
standards are supported in ICARUS).
4 March, 2020 27th ACRIS Meeting, London 16
Access to the ICARUS beta platform to become available to interested users in April 2020!
Are you interested to join? Register to the platform or send us an email to grant you access!
17. Co-funded by the European Commission
Horizon 2020 - Grant # 780792
Thank you 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
Nikos Papagiannopoulos (AIA) - Papagiannopn@aia.gr
Dr. Fenareti Lampathaki (Suite5) - fenareti@suite5.eu
27th ACRIS Meeting, London, February 25th-27th, 2020
http://www.icarus2020.aero