Dealing with Data Diversity in a Smart City Data Hub
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This document discusses the issue of data diversity in smart city data hubs. It argues that data can be diverse in many ways, not just content but also in attributes like licensing and policies. This diversity needs to be managed carefully. The document presents an example of WiFi sensors generating diverse data for a smart city project in Milton Keynes. It proposes that semantic representations of data flows and relationships can help manage diversity at the metadata level and enable reasoning about how attributes propagate across linked data. Formal ontologies are needed to clearly describe diverse data artifacts and their relationships in smart city applications.
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...
- 35. Discussion/future Master of Datasets
- 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