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Dealing with Data Diversity in a 
Smart City Data Hub 
Mathieu d'Aquin - @mdaquin 
slideshare.net/mdaquin 
Knowledge Media Institute, The Open University
Diversity 
where a penguin is a dataset
Why should we care about 
diversity? 
Because diversity is good, and what 
makes data diverse is not the same as 
what makes it more or less relevant
Why should we care about 
diversity? 
Because it is hard to manage 
How many species of species of 
penguins/animals/things? 
How many biologist to classify them? 
and that's purely static... unlike species, new data 
appear all the time...
Why should we care about 
diversity? 
The 
Eskimo language 
has 255 different 
words for 
"visiting linguist" 
Because we might have a lot of it, or 
what we need to manage is very 
granular
Data diversity in a Smart City 
Example of the MK:Smart project in 
Milton Keynes, UK (mksmart.org)
Data diversity in a Smart City 
Partners in the MK:Smart project
Data diversity in a Smart City 
Areas of the MK:Smart project
Data diversity in a Smart City 
MK Data Hub - Where diversity is handled
A concrete example 
Wifi-based presence sensors
A concrete example 
Wifi-based presence sensors 
10-12 can covers an reasonably large enclosed area (here, the refectory 
of the Open University);
A concrete example 
Wifi-based presence sensors 
Use trianglation to find the location of wifi-enabled devices.
A concrete example 
Wifi-based presence sensors 
Basic statistical analysis to extract patterns of usage of the facility
A concrete example 
Wifi-based presence sensors 
Basic statistical analysis to extract patterns of usage of the facility
A concrete example: Diversity
A concrete example: Diversity
A concrete example: Diversity
A concrete example: Diversity
A concrete example: Diversity
A concrete example: Diversity
How do we usually deal with 
this 
data heterogenity 
for we use alignments, mappings, links, etc. 
Example: The LinkedUp Catalogue of datasets 
for education includes mappings between 
the vocanulaties of different datasets 
data.linkededucation.org/linkedup/catalogue/
What about diversity at the 
policy level?
What about diversity at the 
policy level?
What about diversity at the 
policy level?
What about diversity at the 
policy level?
More structured representation 
VoID and DC to represent datasets, PROV-O for basic provenance.
More structured representation 
ODRL for the structured representation of policies and rights
More structured representation 
With the tools to deal with it
More structured representation 
And the processes
Reasoning on the way policy-information 
propagates 
Requires an appropriate representation of dataflows
DataNode 
http://purl.org/datanode/ns/ 
An ontology of relationships between data artifacts (DataNodes).
DataNode 
Captures the essence of dataflows rather than the process, as a basis for 
meta-information propagation.
Propagating meta information 
accross dataflows 
Examples of rules: 
Duties such as attributions propagate over relations of derivation, but 
not necessraly others 
Permissions such as the right to redistribute however do not 
propagate over relations of derivation, except of specific cases (e.g. 
copies) 
Prohibitions such as preventing commercial exploitation propage over 
derivations
Discussion/future 
A lot of the semantics for Smart Cities work focus on data heterogeneity. 
There is a need to look at data diversity at the meta-information level 
(here we focus on policy related information). 
How to manage, catalogue, keep track of and manipulate a large 
number of datasets with diverse rights, access, validity, scope. 
How do we help users/developers in exploring and exploiting this 
diversity...
Discussion/future 
Master of Datasets
Discussion/future 
Need for a clear, semantic (i.e. ontological) foundation for describing 
and defining data artefacts. 
DataNode is a step towards defining their relationships. Vocabularies 
such as ODRL and VOID focus on specific aspects. 
More is needed to formally represent the foundamental descriptors of 
data (scope, validity, policy, ...)
Thanks! 
Mathieu d'Aquin Alessandro Adamou Enrico Daga 
Shuangyan Liu Keerthi Thomas Enrico Motta

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Dealing with Data Diversity in a Smart City Data Hub

  • 1. Dealing with Data Diversity in a Smart City Data Hub Mathieu d'Aquin - @mdaquin slideshare.net/mdaquin Knowledge Media Institute, The Open University
  • 2. Diversity where a penguin is a dataset
  • 3. Why should we care about diversity? Because diversity is good, and what makes data diverse is not the same as what makes it more or less relevant
  • 4. Why should we care about diversity? Because it is hard to manage How many species of species of penguins/animals/things? How many biologist to classify them? and that's purely static... unlike species, new data appear all the time...
  • 5. Why should we care about diversity? The Eskimo language has 255 different words for "visiting linguist" Because we might have a lot of it, or what we need to manage is very granular
  • 6. Data diversity in a Smart City Example of the MK:Smart project in Milton Keynes, UK (mksmart.org)
  • 7. Data diversity in a Smart City Partners in the MK:Smart project
  • 8. Data diversity in a Smart City Areas of the MK:Smart project
  • 9. Data diversity in a Smart City MK Data Hub - Where diversity is handled
  • 10. A concrete example Wifi-based presence sensors
  • 11. A concrete example Wifi-based presence sensors 10-12 can covers an reasonably large enclosed area (here, the refectory of the Open University);
  • 12. A concrete example Wifi-based presence sensors Use trianglation to find the location of wifi-enabled devices.
  • 13. A concrete example Wifi-based presence sensors Basic statistical analysis to extract patterns of usage of the facility
  • 14. A concrete example Wifi-based presence sensors Basic statistical analysis to extract patterns of usage of the facility
  • 15. A concrete example: Diversity
  • 16. A concrete example: Diversity
  • 17. A concrete example: Diversity
  • 18. A concrete example: Diversity
  • 19. A concrete example: Diversity
  • 20. A concrete example: Diversity
  • 21. How do we usually deal with this data heterogenity for we use alignments, mappings, links, etc. Example: The LinkedUp Catalogue of datasets for education includes mappings between the vocanulaties of different datasets data.linkededucation.org/linkedup/catalogue/
  • 22. What about diversity at the policy level?
  • 23. What about diversity at the policy level?
  • 24. What about diversity at the policy level?
  • 25. What about diversity at the policy level?
  • 26. More structured representation VoID and DC to represent datasets, PROV-O for basic provenance.
  • 27. More structured representation ODRL for the structured representation of policies and rights
  • 28. More structured representation With the tools to deal with it
  • 29. More structured representation And the processes
  • 30. Reasoning on the way policy-information propagates Requires an appropriate representation of dataflows
  • 31. DataNode http://purl.org/datanode/ns/ An ontology of relationships between data artifacts (DataNodes).
  • 32. DataNode Captures the essence of dataflows rather than the process, as a basis for meta-information propagation.
  • 33. Propagating meta information accross dataflows Examples of rules: Duties such as attributions propagate over relations of derivation, but not necessraly others Permissions such as the right to redistribute however do not propagate over relations of derivation, except of specific cases (e.g. copies) Prohibitions such as preventing commercial exploitation propage over derivations
  • 34. Discussion/future A lot of the semantics for Smart Cities work focus on data heterogeneity. There is a need to look at data diversity at the meta-information level (here we focus on policy related information). How to manage, catalogue, keep track of and manipulate a large number of datasets with diverse rights, access, validity, scope. How do we help users/developers in exploring and exploiting this diversity...
  • 36. Discussion/future Need for a clear, semantic (i.e. ontological) foundation for describing and defining data artefacts. DataNode is a step towards defining their relationships. Vocabularies such as ODRL and VOID focus on specific aspects. More is needed to formally represent the foundamental descriptors of data (scope, validity, policy, ...)
  • 37. Thanks! Mathieu d'Aquin Alessandro Adamou Enrico Daga Shuangyan Liu Keerthi Thomas Enrico Motta