Overview of a FoodIntegrity project deliverable on IT architectures which enable data sharing in the food system. Talk given at the ASETT2018 conference, Belfast, 30 May 2018

1. IT ARCHITECTURES FOR DATA
SHARING IN AGRI-FOOD
Christopher Brewster, Robert Seepers, Niels Lucas Luijckx
ASSET Conference, 29 May 2018

2. OUTLINE
Objectives
Technologies for data capture and sharing
The Social and Business Environment
Commercial Systems
Academic Approaches
Conclusions and Future Directions
2 | IT ARCHITECTURES FOR DATA SHARING IN AGRI-FOOD
Jan Davidszoon de Heem, Still Life with Fruit and Ham, 1648-49
Talk based on the Food Integrity project
Deliverable 17.2

3. OBJECTIVES AND CONTEXT
Overall Work Package is concerned with the role information systems
can play in food integrity prevention and warning.
Assumptions:
That there is information (i.e. data) that can identify emerging
risks for food integrity
That this data could be shared along the food chain and
analysed
That there are ICT systems in place capturing data  This is
what this talk is about
Note: We distinguish “on chain” data concerning products, from “off
chain” data such as economic data or social media reports
3 | IT ARCHITECTURES FOR DATA SHARING IN AGRI-FOOD
Giuseppe Arcimboldo, Fruit Basket, c. 1590

4. TECHNOLOGIES FOR DATA CAPTURE
Paper and pencil
Web-based or App-based data entry
Barcodes/QR codes
RFID tags
… potentially a lot more sensors
4 | IT ARCHITECTURES FOR DATA SHARING IN AGRI-FOOD

5. TECHNOLOGIES FOR DATA SHARING
Centralised Data Bases
Electronic data interchange (EDI)
EDIFACT (ISO standard 9735)
GS1-EDI
Linked Data
Blockchains/ Distributed Ledger
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6. DATA STANDARDS
Data sharing depends on systems being interoperable
Data standards enable interoperability
Data standards include:
Messaging standards
e.g. EDIFACT, GS1 EPCIS, EFSA’s FOODEX2
Vocabularies or ontologies
e.g. AGROVOC, FOODON
Look at https://vest.agrisemantics.org/ over 300
ontologies
6 | IT ARCHITECTURES FOR DATA SHARING IN AGRI-FOOD

7. ARCHITECTURES 1: PEER TO PEER
Copy data from actor A to actor B
Decentralised system with owners having control of
access and use of data
Does not oblige common data models/standards
Examples include:
Current paper/pdf based systems
Dutch InfoBroker (JoinData) system
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8. ARCHITECTURES 2: CENTRALISED HUB/CLOUD
One or mode central cloud based locations
Common data model
Capability of regulators to connect easily
Third party control inhibits data sharing (loss of
control and ownership)
Majority of existing systems follow this model
Examples include:
Chainpoint,
Agriplace and MuddyBoots
DKE data hub
GS1 (only for “Master Data”)
…. Many more
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9. ARCHITECTURE 3 BLOCKCHAIN
New technology, very overhyped
A distributed, decentralised, shared database (ledger)
Distributed across the network – every participant has a
complete copy
Every copy is the same almost instantly
No transaction can be deleted
Usually open and public – everyone can add transactions
Most current uses do not put readable data on the blockchain
but only a hash value (i.e. a kind of numerical fingerprint)
Under current state of technology neither useful nor effective
(despite the hype)
Examples include: Provenance.org, Origen-Trail.com, Arc-net.io
(here in Belfast), and may others including IBM
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10. ARCHITECTURES – 4 HYBRID
Linked Pedigrees – a distributed peer-to-
peer architectures
Based on integrating:
GS1 EPCIS (formalised as a set of
ontologies)
Linked Data/semantic architecture
using triple stores and web based URIs
Granular access control (data
ownership and control)
(Potentially) a Blockchain for metadata
concerning links in the chain
Entirely standards based with no single
point of failure.
Conceptually very attractive with an unlikely
future!
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11. SOCIAL AND BUSINESS CONTEXT
Food and agriculture operate in multiple silos, both
vertically and horizontally
Social environment contradictory:
Farmers are naturally conservative
Push for transparency from NGOs and much of the
media
Growing emphasis on privacy (GDPR)
Much data is mixture of impersonal and personal
Business environment mitigates against data sharing:
May limit or restrict your business model
Fear of sharing data with competitors
Cost of ICT for small actors too high
11 | IT ARCHITECTURES FOR DATA SHARING IN AGRI-FOOD
Juan Sanchez Cortan, Still Life With Quince, Cabbage, Melon
and Cucumber, 1602-1603

12. CONCLUSIONS AND FUTURE WORK
Majority of commercial systems link 1-2 segments in the value chain
e.g. farmer to supplier/farm to certification
Occasional narrow vertical exceptions
Academic systems are more ambitious
But acknowledge reality of resistance to data sharing systems
Societal and Business context contradictory
More privacy vs. greater transparency
Greater ICT sophistication may increase the cost barrier to entry
Future Work:
Integration at least conceptually of “on chain” with “off chain” data (economic, media, inspections
etc.)
Potential of advanced computational approaches (PPA and MPC, etc.)
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13. THANK YOU FOR YOUR
ATTENTION
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