[DSC Europe 22] How to educate people about Data Science & AI by putting it all in ONE book! - Christos Tryfonopulous

ENIRISST: the first data infrastructure
for shipping and transport in Greece
Assoc. Prof. Christos Tryfonopoulos
Department of Informatics & Telecommunications
University of the Peloponnese, Greece

v Background on Research Infrastructures
v ENIRISST overview
v Pillars, Platforms & Services of ENIRISST
v ENIRISST Platform Architecture
v Current status and future prospects
Presentation overview
2
INTELLIGENT RESEARCH INFRASTRUCTURE FOR SHIPPING, SUPPLY CHAIN, TRANSPORT & LOGISTICS
(ENIRISST)

1,043 eligible research infrastructures with more than 3,000 services
and approximately 755 collaborating organizations (MERIL, April 2020)
Research infrastructures (RIs)
“Facilities, resources and
services that are used by the
research communities to
conduct research and foster
innovation in their fields.
Where relevant, they may be
used beyond research, e.g., for
education or public service.”
European Research Infrastructures
3
(ENIRISST)

Categorization of RIs
Social Science
and
Humanities
Biological and
Medical
Sciences
Environmental
and Earth
Sciences
Natural
Sciences and
Engineering
Material
Sciences and
Analytical
Facilities
Energy
Scientific
Areas
Based on their mode of operation and available facilities:
I. Distributed RIs consist of facilities geographically distributed,
with a central distributor and the interconnected national
nodes
II. Single Sited RIs are located to a geographical place and in
some cases additional specific locations designed to provide
access to the users (european or international governance)
III. Virtual RIs constitute a virtual infrastructure or research
environment in which services are provided electronically
Virtual RIs are essentially e-infrastructures that “address
the needs of European researchers for digital services in
terms of networking, computing and data management”
4
(ENIRISST)

Our vision
A scientific e-infrastructure that supports
seamless access, use, re-use, and trust of data
and services in shipping, logistics and transport
(ENIRISST) 5
Shipping Logistics Transport
Collect and integrate:
• third-party data
• open-source data
• primary (survey) data
Develop services based on:
• data analytics
• programming
• simulation/modelling
Digital platforms to provide end-
users with:
• Data as a Service (DaaS)
• Knowledge as a Service (KaaS)

ENIRISST
v Contribution to GR GDP : 7% (15.2 b€)
v 133 m€ income taxes
v Unique case of Greek entrepreneurship for
half a century
v Contribution to GR GDP: 9% à 15.7 b€
v 570,000 SMEs (annual turnover ~74 b€)
v 3.7% of working force
Transport
Shipping
Logistics
v Contribution to GR GDP : 7.5%
v Road safety: 10,848 accidents, 731 deaths, 13,271 injuries
v Energy: ~40% of total energy demand
ΕNIRISST… Why is it important?
6
(ENIRISST)

1. University of the Aegean (UAEGEAN), RI Coordinator
2. National Technical University of Athens (NTUA)
3. University of Piraeus (UNIPI),
4. Aristotle University of Thessaloniki,
5. University of Thessaly (UTH),
6. Democritus University of Thrace (DUTh)
7. University of West Attika (UWA)
8. University of the Peloponnese (UOP)
9. Centre of Planning and Economic Research (KEPE)
10. Hellenic Centre for Marine Research (HCMR)
11. National Center for Scientific Research “Demokritos” (NCSRD)
ENIRISST partners
7
(ENIRISST)

ENIRISST Platforms and Services
PLATFORMS
Maritime Ecology and
Environment ECOMARPOL
Shipping & Financial Markets
Logistics
Passenger Transport
Maritime Heritage
Infrastructure
• Support Decision Making in Shipping Entrepreneurship
• Financial Markets and Forecasting Services
• …
• Freight and Logistics Market Monitoring
• Optimization of Supply Chains
• …
• Business models for Autonomous Vehicles and Mobility-as-a-Service
• Platform of Accidents and Promotion of Safe Road User Behaviour
• …
• Thematic digital library
• National Observatory for Historical Research on Shipping
• …
• Crowdsourced information on infrastructure and financing
• Evaluation of infrastructure projects
• …
• Environmental Risk Analysis and Energy Efficiency
• Observatory for Management of Maritime Waste
• …
SERVICES
8
(ENIRISST)

Case study: Passenger transport
1. Simulation of urban and interurban passenger trips
2. Multi-modal transport
3. Sea tourism data observatory
4. Mobility-as-a-Service (MaaS) in Greece
5. Road safety observatory
6. Evaluation of transport prosperity indicators
7. Impact of air transport on climate change
8. Usage of autonomous vehicles in Greece
9. Quantitative assessment of social network trends on tourism, migration
and transport
9
(ENIRISST)

Expected Results
Basic performance
indicators
traffic volumes, passenger
kilometres, pollutant
emissions, demand trends
Data
Personalised travellers’ data
(trip/activity diaries, stated preference
experiments, attitudes/perceptions)
Network infrastructure data
Transport services supply
Analytical Tools
Transport Simulation Models
(e.g. Activity-based models)
Data related to innovative
transport modes & services
(stated preference experiments)
ANALYSIS &
SIMULATION OF
FUTURE SCENARIOS
Autonomous vehicles
Micromobility
Mobility-as-a-Service (MaaS)
Impact of innovative modes of
transport & services
Urban and interurban passenger trips
10
(ENIRISST)

Expected Results
Transport requirements
(users & operators)
Demand for multimodal
transport
Information services to
users & operators
Barriers for seamless use
of multimodal transport
services
Data
Data related to multimodal
transport
(stated preference experiments)
Personalised traveller data
(household/individual surveys,
trip/activity diaries, stated preference
experiments, attitudes/perceptions)
Traffic volume
Network infrastructure data
Transport services supply
Analytical Tools
Data Analysis Recording
Current transport networks
Modes of transport
Econometric demand
models
Multi-modal transport
11
(ENIRISST)

Expected results
Coastal/Marine transport
data observatory
Maritime/Cruise
transport data
observatory
Shared use of sailing
boats/yachts
Data
Traffic data statistics
Stakeholders requirements
Personalised travel data
(trip data, social media data,
stated preferences results on
sea tourism services)
Infrastructure data
Analytical Tools
Analysis of current situation
(supply & demand)
Stakeholders requirements
Users preferences &
current trends
Econometric demand
models
Sea tourism data observatory
12
(ENIRISST)

Impact of air transport on climate change
Expected Results
Air transport analysis
(study area: Aegean sea)
Air pollutants emissions
estimation
Analysis of environmental
policies in aviation
Data
Aggregated air traffic data
(air services – flights, airports,
transfers, airline companies)
Air transport supply data
(air ticket prices, aircraft types,
airlines network)
Analytical Tools
Econometric demand
models
Game theoretic
competition modelling
Estimation of air pollutants
emissions
Data related to the
implementation of
environmental policies in
aviation
13
(ENIRISST)

Trends on tourism, migration and transport
Expected Results
Trends on tourism,
transport, and migration
Interaction with other
ENIRISST services for the
exchange of data and
support of tools
Data Analytical Tools
Statistical Analysis
Econometric models
Geospatial analysis of
people preferences
Data from social media
(Twitter, Facebook,
Instagram, Google Places,
Trip advisor, etc.)
14
(ENIRISST)

The architecture aims at designing a platform that follows several important
(and sometimes even conflicting) principles
v usability – how well a user can use it to achieve a defined goal
v efficiency – how fast a defined (processing) goal is achieved
v security – a set of policies, procedures, & technologies for protection
v expandability – how to easily accommodate additions to its capabilities
v reliability – to work as expected for a given amount of time
v maintainability – to be able to understand, repair, & enhance it easily
Architectural goals
15
(ENIRISST)

Platform philosophy
Completely based on open-source tools
Not meant to be a replica of existing sources!
Essentially a data lake
◦ able to combine, correlate & leverage the collected
data
Two primary entities: data providers & consumers
◦main task: matchmaking between entities
16
(ENIRISST)

Main platform tasks
real-time data movement
◦ retrieve data from source, process in memory,
avoid replicating data
on-premises data movement
◦ for vast datasets, for long-term storage & reuse
analysis
◦ using state-of-the-art machine learning suites to
extract insights
visualization
◦ dashboard-based accessibility of data & their
analysis
17
(ENIRISST)

ENIRISST data platform
Two primary entities
◦ data providers
◦ data consumers
Matchmaking between data
providers & consumers
◦ data cataloging
◦ metadata based-searching
◦ data virtualization
18
(ENIRISST)

Bird’s eye view of ENIRISST platform
storage
19
(ENIRISST)

cataloguing
20
(ENIRISST)

searching
21
(ENIRISST)

visualize
22
(ENIRISST)

ready-to-use
machine learning
algorithms
23
(ENIRISST)

custom analysis &
machine learning
tasks
24
(ENIRISST)

ENIRISST: An evolving ecosystem
Constantly adding/incorporating new components
v textual information management (Lucene/Solr, …)
v graph management (neo4j, …)
v maps (open street maps & google maps)
Implied functionality (not directly shown here) :
v authorization
v virtualization
v dockerization
25
(ENIRISST)

ENIRISST+: a recent extension
11 new services to extend the ENIRISST platforms, such as:
v install sensors on Greek islands – citizen science/STEM
v transport models based on land usage
v last mile delivery/pickup by drones
v augmented reality for ships in Greek coastline
v naval cultural heritage digital library
v vision picking in logistics
v …
26
(ENIRISST)

Collaborations and the ENIRISST ecosystem
Policy makers Research institutions
Companies
v Decision support
tools
v Policy making tools
v Innovative research
ideas/topics
v Support for new
researchers
v Collaboration
v Smart specialization
in Shipping, Logistics,
and Transport
v Support for
competitiveness
Citizens
v Platforms
v Accessibility
v Citizen science/
STEM education
27
(ENIRISST)
Data lovers/enthusiasts

Indicative MoUs
v 22 MoUs – new collaborations are always welcome!
v Harvard, MIT, ETH, EPFL, TU Delft, UoCyprus, UoBelgrade, …
v Naval museums
v Energy institutes
v Transportation/logistics centers
v Specialized networks (e.g., WISE-ACT)
v …
28
(ENIRISST)

Indicative research output/impact
vA successful story of a national research ecosystem
v over 80 publications in scientific venues
v over 300 senior & junior scientists working in it
v attracted over 6m euros in funding
vCurrently focusing efforts on
v creating a research organization
v becoming a European research infrastructure
v becoming a hybrid infrastructure (add physical presence)
29
(ENIRISST)

We are Changing the World through Data!
For more information, please contact us at: enirisst@aegean.gr
Assoc. Prof. Christos Tryfonopoulos
University of the Peloponnese

[DSC Europe 22] How to educate people about Data Science & AI by putting it all in ONE book! - Christos Tryfonopulous

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[DSC Europe 22] How to educate people about Data Science & AI by putting it all in ONE book! - Christos Tryfonopulous