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Slow-cooked data and APIs in the world of Big Data: the view from a city perspective

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Dinner speech presentation for the SEMANTiCS 2015 conference

Published in: Science

Slow-cooked data and APIs in the world of Big Data: the view from a city perspective

  1. 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. 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. 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. 4. Act 1 On Data and Food Big Data, Open Data, fast food, slow food
  5. 5. What is Big Data? Source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg
  6. 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. 7. What is Big Data? Source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg
  8. 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. 9. Big Data, Linked Data and Food
  10. 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. 11. What about slow food?
  12. 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. 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. 14. That’s too experimental: other slow food in Spain Nothing like a good pisto manchego
  15. 15. The origin for slow-cooked open data in Spain • It comes from the region of Aragón…
  16. 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. 17. And let’s see some of their slow-cooked data
  18. 18. Act 2 The five rules for slow-cooked data that this team is applying
  19. 19. Rule 1. Chop your onions appropriately
  20. 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. 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. 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. 23. Rule 2. Add some spices, but not too many
  24. 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. 25. Rule 3. Try different ways of plating up your food
  26. 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. 27. Rule 4. Let children appreciate (and cook) slow food
  28. 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. 29. Rule 5…. Eat your own… Well, this is not a proper thing to say for a dinner speech…
  30. 30. Let’s better say… Rule 5. Try it out yourself first… … Before giving your food to your customers
  31. 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. 32. Act 3 (final act) But whom of you haven’t ever eaten a burger in his/her life? (tofu ones as well)
  33. 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. 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. 35. Rule 6. Fast food has its value as well, why not…
  36. 36. So, are we ready to start cooking our open data better?
  37. 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

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