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Contextual Data Collection for Smart Cities

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Presented at The 6th Workshop on Semantics for Smarter Cities (S4SC 2015) co-located with The 14th International Semantic Web Conference (ISWC 2015).

Full paper at: http://tw.rpi.edu/web/doc/santos-s4sc-2015

Published in: Data & Analytics
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Contextual Data Collection for Smart Cities

  1. 1. Contextual Data Collection for Smart Cities The 6th Workshop on Semantics for Smarter Cities (S4SC 2015) at The 14th International Semantic Web Conference (ISWC 2015) Bethlehem, Pennsylvania, U.S.A. Henrique O. Santos, LEC-UNIFOR and TW-RPI Vasco Furtado, LEC-UNIFOR and CITINOVA Paulo Pinheiro, TW-RPI Deborah L. McGuinness, TW-RPI
  2. 2. 2 Smart Cities • Smart City is a city that is aimed to the future, i.e., with public policies to foster its safeness, sustainability, creativeness, innovativeness and so forth • Two keys points are identified: access to and understanding of city data
  3. 3. 3 Smart Cities are cities that produce relevant data that can be understood, derive knowledge from this data and use that knowledge to empower the city aspects.
  4. 4. 4 Open Government Data (OGD) • City data encompasses not only datasets containing regular data from city agencies, but also datasets and streams containing monitored data from sensors deployed throughout the city • This monitored data is normally published as regular datasets (many times in CSV format) without further information about the sensor network that is behind the collection of the data • Monitored data is about data that is collected empirically by sensors deployed in the city. It talks about a measured value that is obtained while sensing a characteristic of an entity of interest
  5. 5. 5 Aspects of OGD today Aspect How it is addressed Data presentation to the stakeholders - Datasets Metadata information - Description text files - Annotations - Derived from the above Provenance - Dataset level: Description text files - Data level: (mostly not addressed) Context (Not addressed)
  6. 6. 6 Context timeline t Feb 12, 2015, 11:45PM Feb 11, 2015, 10:00AM Configuration Deployment Dec 28, 2015, 11:32AM Dec 16, 2015, 9:55AM Calibration Acquire Nov 4, 2014, 12:55PM t February 12, 2015, 9:30AM February 12, 2015, 11:45PM Auto calibration Oct , 2015, 10:33AM Feb 12, 2015, 9:30AM
  7. 7. 7 City sensor network Sensors • What is being monitored on the city? • Where are the sensors deployed? • What are the sensors capable of monitoring? Monitored data • Is this data coming from which sensor? • Can one compare two monitored values for scientific purposes?
  8. 8. 8 HASNetO ● The Human-Aware Sensor Network Ontology [1] VSTO-I OBOE Deployment Data Collection Dataset Measurement Entity Characteristic Unit Platform Instrument Detector 1. Pinheiro, P., McGuinness, D.L., Santos, H.: Human-Aware Sensor Network Ontology: Semantic Support for Empirical Data Collection. In: Proceedings of the 5th Workshop on Linked Science. Bethlehem, PA, USA (2015) 2. Fox, P., McGuinness, D.L., Cinquini, L., West, P., Garcia, J., Benedict, J.L., Middleton, D.: Ontology-supported scientific data frameworks: The Virtual Solar-Terrestrial Observatory experience. Computers & Geosciences 35(4), 724–738 (Apr 2009) 3. http://www.w3.org/TR/prov-o 4. Madin, J., Bowers, S., Schildhauer, M., Krivov, S., Pennington, D., Villa, F.: An ontology for describing and synthesizing ecological observation data. Ecological Informatics 2(3), 279–296 (Oct 2007) [2 ] [3 ] [4 ]
  9. 9. 9 HASNetO-SC • The Human-Aware Sensor Network Ontology for Smart Cities
  10. 10. 10 Contextualized CSV - CCSV TimeStamp,AirTemp_C_Avg,RH_Pct_Avg 2015-02-12T09:30:00Z,-4.5,66.58 2015-02-12T09:45:00Z,-4.372,66.45 2015-02-12T10:00:00Z,-4.146,65.98 2015-02-12T10:15:00Z,-4.084,66.22 2015-02-12T10:30:00Z,-4.251,67.48 2015-02-12T10:45:00Z,-4.185,69.85 2015-02-12T11:00:00Z,-4.133,72 2015-02-12T11:15:00Z,-3.959,70.84 … 2015-02-12T23:00:00Z,-9.63,77.88 2015-02-12T23:15:00Z,-10.48,80.8 2015-02-12T23:30:00Z,-10.96,82 2015-02-12T23:45:00Z,-10.1,80.7 t February 12, 2015, 9:30AM February 12, 2015, 11:45PM
  11. 11. 11
  12. 12. 12 senses senses SOLR CCSV-loader Ontologies (HASNetO, OBOE, PROV, VSTO)Data Metadata data (CCSV) data (CCSV) expanded CSV Sensor network description Data browser SPARQL / SOLR queries Data users Architecture
  13. 13. 13 Fortaleza is the 5th biggest capital in Brazil With more than 2.5 million residents
  14. 14. 14 Use case: Fortaleza bus transportation system • http://dados.fortaleza.ce.gov.br • Used datasets – Bus checkpoints – Bus companies – Bus fleet – GPS measurements for February 2015
  15. 15. 15 Domain ontology
  16. 16. 16 Fortaleza bus sensor network description
  17. 17. 17
  18. 18. 18
  19. 19. 19 Aspects of OGD with HASNetO-SC Aspect How it is addressed How we are addressing Data presentation to the stakeholders - Datasets - Data collections Metadata information - Description text files - Annotations - Derived from the above - HASNetO-SC sensor network - OBOE concepts Provenance - Dataset level: Description text files - Data level: (mostly not addressed) - PROV-O Context (Not addressed) - HASNetO activities
  20. 20. 20 Conclusion and next steps • A challenge exists in representing context in city sensor networks in a meaningful way, i.e., that can leverage the full potential the data it collects • Our work addresses that challenge by linking the monitored data to metadata (sensor network and activities) using CCSV and HASNetO-SC • We are approaching monitored data, but non-monitored data also plays a main role on smart cities. We are currently researching how to cope PROV with “told” data
  21. 21. 21 Thank you! Questions? Henrique O. Santos – oliveh@rpi.edu Vasco Furtado – vasco@unifor.br Paulo Pinheiro – pinhep@rpi.edu Deborah L. McGuinness – dlm@cs.rpi.edu

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