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In the Age of Open InformationDo-It-Yourself Analytical Mashups on Schema-optional DataKatrin BraunschweigJulian EberiusMa...
The Roots ofOpen Data<br />The open society is a concept originally developed by philosopher Karl Popper<br />In open soci...
1984 - Freedom of Information Campaign starts up<br />
Why Data Shouldbe Open<br />Many scientific data can be deemed to belong to the commons (“the human race”), e.g. the human...
Open Data – Examples<br />
data.gov<br />
data.gov.uk<br />
data.worldbank.org<br />
unData<br />
OpenStreetMap<br />
CivicApplicationsbased on Open Data<br />
Explore How U.S. Budget Proposal<br />
Mapnificient<br />
Schooloscope<br />
Fluglärmkarte (taz.de)<br />Database<br />Journalism<br />
Open Data – Challenges an  <br />Challenges<br />Lots ofcontributors / maintainers<br />Small informationpieces distribut...
The Big Picture<br />
Do-It-Yourself Schema Augmentation<br />Application<br />ReferenceNode<br />AttributeTypes<br />EntityTypes<br />NoType<br...
Do-It-Yourself Analytical Mashups<br />Process Query<br />„number of cafes vs. age distribution per district of Dresden“<b...
Do-It-Yourself Analytical Mashups (2)<br />Process Query<br />„number of cafes vs. age distribution per district of Dresde...
Do-It-Yourself Analytical Mashups (3)<br />Process Query<br />„number of cafes vs. age distribution per district of Dresde...
Do-It-Yourself Analytical Mashups (4)<br />Process Query<br />„number of cafes vs. age distribution per district of Dresde...
Do-It-Yourself Analytical Mashups (5)<br />Process Query<br />„number of cafes vs. age distribution per district of Dresde...
Demo<br />
Map-centricweb<br />application<br />Mobile <br />application<br />3rd-party <br />applications<br />#<br />#<br />#<br />...
Open CivicPlatformfor Dresden (2)<br />
Open CivicPlatformforDresden (3)<br />
New York – Example<br />
New York – Example (2)<br />
In the Age of Open InformationDo-It-Yourself Analytical Mashups on Schema-optional DataKatrin BraunschweigJulian EberiusMa...
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In the Age of Open Information - Do-It-Yourself Analytical Mashups on Schema-optional Data

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The increasing amount and variety of open and crowdsourced data available in the web leads to new challenges in end-user focused data analysis. This data is characterized by a great structural diversity which causes serious problems regarding their integration. On the other site there is a lack of end-user friendly tools to make productive use of the data available on the web. We want to address the first problem by developing a schema-optional graph-based data model that enables incremental schema augmentation and evolution. The second problem should be adressed by a multi-layered domain-specific language for data mashup construction on schema-optional data.

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In the Age of Open Information - Do-It-Yourself Analytical Mashups on Schema-optional Data

  1. 1. In the Age of Open InformationDo-It-Yourself Analytical Mashups on Schema-optional DataKatrin BraunschweigJulian EberiusMaik ThieleWolfgang Lehner<br />OUTPUT 2011<br />
  2. 2. The Roots ofOpen Data<br />The open society is a concept originally developed by philosopher Karl Popper<br />In open societies, government is responsive and tolerant, and political mechanisms are transparent and flexible<br />The state keeps no secrets from itself in the<br /> public sense<br />It is a non-authoritarian society in which<br /> all are trusted with the knowledge of all<br />
  3. 3. 1984 - Freedom of Information Campaign starts up<br />
  4. 4. Why Data Shouldbe Open<br />Many scientific data can be deemed to belong to the commons (“the human race”), e.g. the human genome, medical science, environmental data<br />They have an infrastructural role essential for scientific endeavour (e.g. in Geographic Information Systems and maps)<br />Data published in scientific articles are factual and therefore not copyrightable<br />Public money was used to fund<br /> the work and so it should be<br /> universally available<br />It was created by or at a<br /> government institution <br />
  5. 5. Open Data – Examples<br />
  6. 6. data.gov<br />
  7. 7. data.gov.uk<br />
  8. 8. data.worldbank.org<br />
  9. 9. unData<br />
  10. 10. OpenStreetMap<br />
  11. 11. CivicApplicationsbased on Open Data<br />
  12. 12. Explore How U.S. Budget Proposal<br />
  13. 13. Mapnificient<br />
  14. 14. Schooloscope<br />
  15. 15. Fluglärmkarte (taz.de)<br />Database<br />Journalism<br />
  16. 16. Open Data – Challenges an <br />Challenges<br />Lots ofcontributors / maintainers<br />Small informationpieces distributed, decentralised<br /> and verylooselycoupled<br />Different degreeofschemainformationand metadata<br />Innovation / unexpectedreuse<br />Nostandardizeddevelopmentprocess<br />Contributions<br />Schema-optional datastore, collaborativeschemaaugmentation (basicoperators)<br />Measuredegreeofschemainformation<br />Non-destructiveschemachanges<br />Capture dataprovenance<br />Visualizationsand interactionpatterns<br />Iterative and guideddevelopment<br />Data and visualizationrecommendation<br />
  17. 17. The Big Picture<br />
  18. 18. Do-It-Yourself Schema Augmentation<br />Application<br />ReferenceNode<br />AttributeTypes<br />EntityTypes<br />NoType<br />NoType<br />ET1<br />AT1<br />ET4<br />AT2<br />AT4<br />ET2<br />ET3<br />AT3<br />Schema <br />Augmentation<br />Automated<br />Schema Extraction<br />AT1 : value<br />AT2 : value<br />AT3 : value<br />AT4 : value<br />E<br />V<br />E<br />V<br />AT<br />TT<br />AT<br />TT<br />ET<br />Relational Table<br />CSV File<br />
  19. 19. Do-It-Yourself Analytical Mashups<br />Process Query<br />„number of cafes vs. age distribution per district of Dresden“<br />Look upfittingdatasets<br />Computesuitablevisualization<br />Computeinteraction / explorationfeatures<br />
  20. 20. Do-It-Yourself Analytical Mashups (2)<br />Process Query<br />„number of cafes vs. age distribution per district of Dresden“<br />Look upfittingdatasets<br />Computesuitablevisualization<br />Computeinteraction / explorationfeatures<br />NLP techniques <br />+ Lookup services (e.g. GeoNames)<br />number of cafes vs.age distribution perdistrict of Dresden<br />natural geographic entity<br />value dimensions<br />relations/operations<br />
  21. 21. Do-It-Yourself Analytical Mashups (3)<br />Process Query<br />„number of cafes vs. age distribution per district of Dresden“<br />Look upfittingdatasets<br />Computesuitablevisualization<br />Computeinteraction / explorationfeatures<br />Ambiguity<br />userfeedback<br />OR<br />
  22. 22. Do-It-Yourself Analytical Mashups (4)<br />Process Query<br />„number of cafes vs. age distribution per district of Dresden“<br />Look upfittingdatasets<br />Computesuitablevisualization<br />Computeinteraction / explorationfeatures<br />
  23. 23. Do-It-Yourself Analytical Mashups (5)<br />Process Query<br />„number of cafes vs. age distribution per district of Dresden“<br />Look upfittingdatasets<br />Computesuitablevisualization<br />Computeinteraction / explorationfeatures<br />number of cafes <br />age distribution <br />Too much information for one visualization<br />enableexploration, e.g., clicking a district in themapopenshistogram<br />
  24. 24. Demo<br />
  25. 25. Map-centricweb<br />application<br />Mobile <br />application<br />3rd-party <br />applications<br />#<br />#<br />#<br />REST Interface<br />PersistenceLayer<br />Open CivicPlatformforDresden<br />Mobile Application <br />Add new requests by guiding the user through a wizard-style input form<br />Show (own) reports and there current rating and processing actualstate<br />Visualize all reports on a map<br />Subscribe to a set of urban district and notify the user about news<br />Web Application<br />Filter the requests by their category, their creation time (last 24 hours, last week, last month, all)<br />Change the requests state (open, closed, closed) for authorized users<br />Zoom in/out and adapt the type of visualization if the issue density gets very sparse<br />
  26. 26. Open CivicPlatformfor Dresden (2)<br />
  27. 27. Open CivicPlatformforDresden (3)<br />
  28. 28. New York – Example<br />
  29. 29. New York – Example (2)<br />
  30. 30. In the Age of Open InformationDo-It-Yourself Analytical Mashups on Schema-optional DataKatrin BraunschweigJulian EberiusMaik ThieleWolfgang Lehner<br />OUTPUT 2011<br />

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