This document discusses maximizing the reuse of open transport data through improving interoperability. It outlines several key steps to improving interoperability, including using open licenses, sharing data on the web using open standards like JSON and XML, identifying data using URIs instead of local identifiers, and exposing reusable building blocks of datasets to facilitate federated querying of multiple datasets. The goal is to make it as easy as possible to combine multiple transport datasets as if they were one for applications like route planning algorithms.

1. Maximizing the reuse of
Open Transport Data
Pieter Colpaert
Ghent University - iMinds/imec

2. How far
do you live from work?

3. km or min?

4. Imagine a program calculating distance in minutes
What data would you need?

5. Transport has become a
data sharing problem
How can we fix it?

6. Sharing data between 2 systems
Your system
Third party
system
Agree on a protocol
Will determine which questions can
be answered in a timely fashion
Can ask questions to your system
as previously agreed

7. Sharing data on the Web
Your system
?
?
?
?
?
?
Maximizing reuse → need to raise the interoperability

8. ↓
Querying
syntactic
semantic
technical
legal
When I have got 2 datasets, how easy is it to use
them as if they were 1?

9. OpenDefinition.org
↓
Querying
syntactic
semantic
technical
legal

10. reuse is allowed
Documents on the web
reuse in a gray zone unauthorised reuse

11. A story of raising interoperability
↓
Querying
syntactic
semantic
technical
legal
When I have 2 datasets, how easy is it to turn them into 1 dataset?
→ Open Definition & open licenses

12. A story of raising interoperability
↓
Querying
syntactic
semantic
technical
legal
When I have 2 datasets, how easy is it to turn them into 1 dataset?
→ Open Definition & open licenses
→ The Internet: exchanging data world-wide

13. A story of raising interoperability
↓
Querying
syntactic
semantic
technical
legal
When I have 2 datasets, how easy is it to turn them into 1 dataset?
→ Open Definition & open licenses
→ The Internet: exchanging data world-wide
→ JSON, XML, CSV, … Open Standards

14. name type same as location
iMinds company IBBT Gaston
Crommenlaan 8
{
“iMinds” : {
“type” : “company”,
“same as” : “IBBT,
“location” : “Gaston
Crommenlaan 8”
}
}
<iMinds>
<type>company</type>
<sameas>IBBT</sameas>
<location>
Gaston Crommenlaan 8
</location>
</iMinds>
Table / CSV / Spreadsheet
JSON XML
Serialisations

15. name type same as location
iMinds company IBBT Gaston
Crommenlaan 8
<iMinds> <type> <company> .
<iMinds> <sameas> <IBBT> .
<iMinds> <vestiging> “Gaston Crommenlaan 8” .
Table / CSV / Spreadsheet
triples
Triple structure
{
“iMinds” : {
“type” : “company”,
“same as” : “IBBT,
“location” : “Gaston
Crommenlaan 8”
}
}
<iMinds>
<type>company</type>
<sameas>IBBT</sameas>
<location>
Gaston Crommenlaan 8
</location>
</iMinds>
JSON XML

16. World Wide Web
iMinds
same as
IBBT
iMinds
is a
company
IBBT
located at
Gaston Crommenlaan 8
Machine 1 Machine 2 Machine 3
Linked data

17. Problem
The word company is ambiguous. How can we
make sure that machines understand each other?
semantic interoperability
What about “is a”?
and what about “iMinds”?

18. Solution
iMinds → http://data.kbodata.be/organisation/0866_386_380#id
is a → http://www.w3.org/1999/02/22-rdf-syntax-ns#type
Company → http://www.w3.org/ns/regorg#RegisteredOrganization
Uniform Resource Identifiers (URIs)

19. E.g., Linked Datex and Linked GTFS
Vocabularies at
http://vocab.datex.org/terms
http://vocab.gtfs.org/terms
E.g.,
Searching for Parking Facilities
with Linked Data thanks to “rich snippets”
But is that it?

20. A story of raising interoperability
↓
Querying
syntactic
semantic
technical
legal
When I have 2 datasets, how easy is it to turn them into 1 dataset?
→ Open Definition & open licenses
→ The Internet: exchanging data world-wide
→ JSON, XML, CSV, … Open Standards
→ using URIs instead of local identifiers

21. Where can you get in what amount of time?
under specific conditions:
taking into account: multimodality, criminality, your subscriptions,
what you’re carrying, disabilities, etc

22. data dump
Route planning
algorithms as a service

23. A long tail for transport data services
...
Hard to guess which kind of
queries will be needed

24. Can we find a way to publish
for example public transport data
while minimizing federated reuse cost?

25. Data needed for algorithm
a connection
departureTime
+
departureStop
arrivalTime
+
arrivalStop
another connection
departureTime
+
departureStop
arrivalTime
+
arrivalStop

26. time
* The Connection Scan Algorithm (CSA)
And this is the algorithm*
~ creating a minimum spanning tree through a sorted directed acyclic graph
Squares are connections

27. Resource XResource ...Resource 2Resource 1
time
nextPage
nextPage
When published in pages on the Web,
route planning will need X requests
instead of 1

28. Try it yourself at
http://LinkedConnections.org

29. Striking the golden mean?
Data dumps
Smart servers
Data publishing
(cheap/reliable)
Data services
(rather expensive/unreliable)
Entire query
languages over
HTTP
Dataset split in
fragments
Smart agents
algorithms
as a service

30. Global interoperability for Route Planners?
↓
Querying
syntactic
semantic
technical
legal → Open Definition & open licenses
→ The Internet: exchanging data world-wide
→ JSON, XML, CSV, … Open Standards
→ Work in progress linkedconnections.org
→ using URIs instead of local identifiers

31. Checklist Open (Transport) Data
Do you have an open license on your data?
Is it shared publicly on the Web in an open format (html/css/xml/json…)?
Do you identify things in a globally interoperable way?
How easy is it to include your dataset in a federated query?
Are you exposing basic reusable building blocks for your dataset?

32. A world where knowledge creates power
for the many, not the few
Questions?
@pietercolpaert
http://pieter.pm