Dinner speech presentation for the SEMANTiCS 2015 conference

1. Slow-cooked data and APIs in
the world of Big Data:
the view from a city perspective
16/09/2015
Oscar Corcho
ocorcho@fi.upm.es
@ocorcho
https://www.slideshare.com/ocorcho

2. License
• This work is licensed under the license
CC BY-NC-SA 4.0 International
• http://purl.org/NET/rdflicense/cc-by-nc-sa4.0
• You are free:
• to Share — to copy, distribute and transmit the work
• to Remix — to adapt the work
• Under the following conditions
• Attribution — You must attribute the work by inserting
• “[source Oscar Corcho]” at the footer of each reused slide
• a credits slide stating: “These slides are partially based on
“Slow-cooked data and APIs in the world of Big Data:
the view from a city perspective” by O. Corcho”
• Non-commercial
• Share-Alike

3. Disclaimers
• I may be politically incorrect at
some points in time
• Don’t feel offended…, it’s a dinner speech
• Please, continue talking to me afterwards
• If you still feel offended, let me invite
you to a beer and discuss about it
• I have some questions for you
• Please respond to them…
• I explicitly asked not to serve tomatoes for dinner…
• Just in case that you are tempted to throw them at me…
• I hope that by the end of the talk, we all learn a bit
about data and slow food

4. Act 1
On Data and Food
Big Data, Open Data, fast food, slow food

5. What is Big Data?
Source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg

6. What is (Linked) Open Data?
Source: "Linking Open Data cloud diagram 2014, by Max Schmachtenberg, Christian Bizer, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/"

7. What is Big Data?
Source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg

8. What is (Linked) Open Data?
Source: "Linking Open Data cloud diagram 2014, by Max Schmachtenberg, Christian Bizer, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/"

9. Big Data, Linked Data and Food

10. An analogy between Big Data and Fast Food
• Too much data to
consume
• Too little time to
process it
• One is never sure
about the data
provenance
• No time for a good
espresso (or a nice
chat) afterwards

11. What about slow food?

12. Quiz 1 of the night
• Let’s see whether we agree on what slow food is…
• Hands up if you think that this is slow food
• Let’s now move into Spain, which I know a bit better

13. Slow food (and nouvelle cuisine) in Spain
• It’s everywhere, but most of it connected to two
regions with some of the best chefs
• Not sure how long they will be part of Spain anyway ;-)
Basque Country
Catalunya

14. That’s too experimental: other slow food in Spain
Nothing like a good pisto manchego

15. The origin for slow-cooked open data in Spain
• It comes from the region of Aragón…

16. Let’s meet the chef and her team of talented cooks
The Web team at Zaragoza, also responsible for the Open Data portal and API

17. And let’s see some of their slow-cooked data

18. Act 2
The five rules for
slow-cooked data
that this team is applying

19. Rule 1.
Chop your onions
appropriately

20. Rule 1. Chop your onions appropriately
• Take care about the number of datasets that you
produce
• There’s still a silly competition about
“my open data portal has more datasets than yours”
• This provokes, sometimes, over-segmentation of data
• Main question: What makes a dataset useful and
which datasets should I publish?

21. Rule 1. Chop your onions appropriately
• UNE 178301:2015
• Norm on Open Data for
Smart Cities
• Organised by
• AENOR CTN 178 group
• Government and Mobility
• Government
• Open Data
(led by Localidata)
• Formed by
• Several cities
• Private companies
• Nation-wide
organisations

22. Rule 1. Chop your onions appropriately
• 10 datasets selected
• Based on frequency of
requests from reusers
• Target for 2015
• And now working on extending it to 100 datasets
• With an additional group of people
Datasets
Cultural Agenda
Traffic
Population
Streets
Public Transport
Touristic Places and POIs
Budget
Shop Census
Air Quality
Contracts
Parkings

23. Rule 2.
Add some spices,
but not too many

24. Rule 2. Add some spices, but not too many
• Annotate (semantically) your data, so that others can
understand what you produce
• And produce examples for consumers to understand them
• Don’t wait until all schema.org properties are settled
• Generate SKOS thesauri for your own classifications
• e.g., for groups of citizens (young, elderly, etc.), for types of
events (cultural, children, music, etc.)

25. Rule 3. Try different ways of plating up your food

26. Rule 3. Try different ways of plating up your food
• Produce your data in different formats
• Agreed-upon JSONs
• JSON-LD
• RDF
• Agreed-upon CSVs
• With the upcoming CSV on the Web
• But don’t get crazy at offering all options
• The ones that get finally used are more than enough

27. Rule 4.
Let children appreciate (and cook) slow food

28. Rule 4. Let children appreciate (and cook) slow food
Let children understand the
benefits of open data
(and Citizen Science)
and how they can contribute to
improving the data of their city

29. Rule 5…. Eat your own…
Well, this is not a proper thing
to say for a dinner speech…

30. Let’s better say…
Rule 5.
Try it out yourself first…
… Before giving your
food to your customers

31. Rule 5. Try it out yourself first…
• Open data by default
• So that your applications are also based on open data
Source: Los Datos Abiertos como Eje Central del desarrollo de la Plataforma de Gobierno Abierto. M.J. Fernández-Ruiz, V. Morlán

32. Act 3 (final act)
But whom of you haven’t
ever eaten a burger in his/her life?
(tofu ones as well)

33. Rule 6.
Fast food has its value
as well, why not…
You go anywhere in the world and know how
McDonald’s burgers are…
So let’s only learn this from fast food..

34. Rule 6. Fast food has its value as well, why not…
• When we open our data, let’s use at least the same
data structures
Publish
Extract
Publish
Extract
Publish
Extract
I want to publish
my data
I am using GTFS I am using my own CSV
structure
I provide it as a Web
service
Write an app and deploy everywhere

35. Rule 6. Fast food has its value as well, why not…

36. So, are we ready to start cooking our open data better?

37. Slow-cooked data and APIs in
the world of Big Data:
the view from a city perspective
16/09/2015
Oscar Corcho
ocorcho@fi.upm.es
@ocorcho
https://www.slideshare.com/ocorcho