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From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Dashboards

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Presented at the 14th Extended Semantic Web Conference (ESWC 2017) - In-Use Track

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From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Dashboards

  1. 1. FROM DATA TO CITY INDICATORS: A KNOWLEDGE GRAPH FOR SUPPORTING AUTOMATIC GENERATION OF DASHBOARDS Henrique Santos1, Victor Dantas1, Vasco Furtado1, Paulo Pinheiro2 and Deborah L. McGuinness2 1Universidade de Fortaleza, Fortaleza, CE, Brazil 2Rensselaer Polytechnic Institute, Troy, NY, USA 14th Extended Semantic Web Conference (ESWC 2017) Portorož, Slovenia, 31 May 2017
  2. 2. Henrique Santos et al. From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Agenda ■ Motivation ■ City indicators ■ City Knowledge Graph – Metadata ontologies – Domain and Indicator ontologies ■ Use-case on BICICLETAR (Fortaleza bicycle-sharing system) – Data pipeline – SBIG (Semantic BI Generator) ■ Conclusions and ongoing work 2
  3. 3. Henrique Santos et al. From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Assessing city performance ■ Why is it important to measure city performance? – Compare to other cities – Better decision making – Better budget allocation ■ ISO 37120:2014 – Sustainable development of communities – Indicators for city services and quality of life – Standardized set of indicators for measuring city performance ■ But how to calculate city indicators? – “Get the needed data… but what data do I need?” – “I think I have the data… but I can’t understand it” – High quality metadata is crucial when calculating trustworthy indicator values 3
  4. 4. Henrique Santos et al. From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of City indicator requirements Temporal coverage 01 Entities of interest 02 Provenance 03 Contextual knowledge 04 (Geo) Location 05 Support easy visualization 06 4
  5. 5. Henrique Santos et al. From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Metadata ontologies VSTO-I HAScO HACitO • Instruments and sensing devices • Activities performed over instruments • Scientific activities • Alignment of VSTO-I and PROV • Specialization of HAScO concepts for cities hasco:Study hasco:Data Acquisition vstoi: DeploymentisData AcquisitionOf hasDeployment prov:Activity xsd: dateTime xsd: dateTime prov:startedAtTime prov:endedAtTime 5
  6. 6. Henrique Santos et al. From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Domain and indicator ontologies QoE Domain QoE Indicators • Entities and relations of interest • Quantifications • Relations/procedures between/over domain quantifications 6
  7. 7. Henrique Santos et al. From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of BICICLETAR: The bicycle-sharing system of Fortaleza, Brazil Number of journeys performed Tons of CO2 saved As of May 2017 7 - Dataset containing list of bicycle stations - Dataset containing list of journeys performed during May 2016
  8. 8. Henrique Santos et al. From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Data pipeline steps Data annotation 1 Data loading / KG building 2 KG browsing 3 KG serialization 4 KG serialization (suitable indicators discovery) 5 Dashboard building 8
  9. 9. Henrique Santos et al. From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Data annotation 1 Data loading / KG building 2 KG browsing 3 KG serialization 4 KG serialization (suitable indicators discovery) 5 Dashboard building CCSV-Annotator CCSV (Contextualized CSV) CCSV-Loader 9
  10. 10. Henrique Santos et al. From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of 10 Data annotation 1 Data loading / KG building 2 KG browsing 3 KG serialization 4 KG serialization (suitable indicators discovery) 5 Dashboard building
  11. 11. Henrique Santos et al. From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of ■ Most existing data tools (R, Python, Gephi, Business Intelligence softwares) are not ready to deal with RDF model serialization formats like RDF/XML, JSON-LD or Turtle ■ Most of the times they expect tabular data ■ To foster automatic visualization, a set of possible calculations (indicators) over the data should be attached to the serialized data 11 Data annotation 1 Data loading / KG building 2 KG browsing 3 KG serialization 4 KG serialization (suitable indicators discovery) 5 Dashboard building
  12. 12. Henrique Santos et al. From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of For each domain class with valid instances, create a CSV file For every valid instance in the KG, create a CSV register in that file Each register is composed by every triple in which that instance is the subject Generate Turtle annotations to describe each column of the CSV file Based on all annotations of all files, discover suitable indicators in the indicator ontology Serialize the discovered indicators themselves in Turtle format 12 Data annotation 1 Data loading / KG building 2 KG browsing 3 KG serialization 4 KG serialization (suitable indicators discovery) 5 Dashboard building
  13. 13. Henrique Santos et al. From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Serialized KG entities in CCSV format 13
  14. 14. Henrique Santos et al. From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of 14 PROLOG implementation CCSV CCSV QoE Domain QoE Indicators Indicators (Turtle) Data annotation 1 Data loading / KG building 2 KG browsing 3 KG serialization 4 KG serialization (suitable indicators discovery) 5 Dashboard building
  15. 15. Henrique Santos et al. From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of 15 Data annotation 1 Data loading / KG building 2 KG browsing 3 KG serialization 4 KG serialization (suitable indicators discovery) 5 Dashboard building
  16. 16. Henrique Santos et al. From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Resultados 16
  17. 17. Henrique Santos et al. From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of 17
  18. 18. Henrique Santos et al. From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Conclusions and future work ■ City Knowledge Graph description in support of automatic generation of dashboards ■ Indicator and domain ontology ■ SBIG: Semantic BI Generator application ■ Extension of the approach to support more complex indicator values (for instance, network algorithms and their meanings for each network) ■ HADatAc: Human-Aware Data Acquisition Framework 18
  19. 19. Henrique Santos et al. From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Thank you for your attention From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Dashboards Henrique Santos, Victor Dantas, Vasco Furtado, Paulo Pinheiro and Deborah L. McGuinness hos@edu.unifor.br @hansidm http://henriquesantos.org 19

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