1. WP 5 Data management & analysis Michel Bohms and Philomena M. Bluyssen – TNO Isabella Annesi-Maesano - UMPC Paris 06 Aileen Yang and Alena Bartonova – NILU Objectives 1. To build a common database on IAQ and other environmental parameters and related health effects 2. To facilitate the coordination of the different groups that will use the experimental data obtained on both school environmental characteristics and health

2. WP 5 Data management & analysis Task 1 Specification Database structures and functionalities (months: 3-6) Task 2 Implementation Database structures, content creation and functionalities (months: 7-14) (old task 2 and 3) Task 3 Analysis (old task 4 Source analysis and 5 Epidemiological Analysis?) (months: 14-20) Task 6. Recommendation-design tool (with WP7) (months: 18-22) transfer to WP7???

3. - building and HVAC characteristics of the investigated schools (including inventory of possible sources, design criteria, maintenance and occupancy); - outdoor situation and outdoor air conditions; - indoor air conditions (including content) and ventilation situations; - health and comfort of pupils and teachers; - potential confounders of the relationships between school environment and health. Type of data in database

4. - Analysis of the data resulting in possible links between exposure concentrations of air pollutants and sources and activities (old task 4) -Analysis of the data resulting in possible links between health in children and teachers and sources and activities (old task 5) Type of analysis

5. Task 1 Specification Database structures and functionalities (months: 3-6) Data/parameters for structure (input): WP2, 3, 4 Data/parameters for analysis (output): WP5 and WP6 Functionalities for analysis: WP2, WP5, WP6 Functionalities for other purposes: WP2, 8? What do we put in and what do we want to get out? How do we prefer to put it in and get it out?

6. Co-leaders: TNO, UMPC Paris 06 and NILU? Task 1: to provide data and functionalities for structure from WP leaders or directly from partners? Task 2 Implementation database structures and functionalities – TNO? Task 3 Analysis – TNO, NILU, and ???? WP 5 Who will do what? Person-months?

7. WP2 will be enhanced by an information web-based system providing Internet access as well as a search function to: -Data as available both from the reviewed studies, publications & reports as well as current study later on as they become available. WP 3.2 Task 5: Database on IAQ in European schools The collected data in the main and the case studies will be reported in a database, which will be easily accessible for the project partners, in order to facilitate the application of the data in the next WPs. WP 3.3 European wide dataset for input in WP5 (cross-analyses) WP8 web-site on IAQ Relation with other WP’s? Will there be different databases?

8. Perhaps two databases? A database for the public and a database for SINPHONIE to make analysis.

9. Possible approaches data management 1.Semantic web approach and open source implementation Standard way of saving and retrieving data on Internet via a Web server, open to every one. User control is possible. 2.Closed database: assessed via login etc. (i.e. oracle, SQL server) 3.Or something even more innovative: Google..

10. Next steps Partners: Who is involved? Task distribution: Who will supply which data? Who will do which analysis? Database: Which type of data and functionalities? What type of database(s)? WP5 Meeting – Medio January – in Delft?

11. Semantic web approach and open source implementation W3C Semantic Web technologies Web Ontology Language (OWL) On top of RDF/RDFS SPARQL Quering OSS Joseki Semantic Web (RDF) Server With Triple DataBase (TDB) back-end

12. Benefits of proposed approach 100% ‘Open Standards’-based For Data Storage & Access Independent of any particular platform, software vendor or user group 100% Open Source Software (OSS) implementation (of those open standards) 100% free of charge Semantic (model-driven) Flexible Generic International Web-based (distributed, universal access, …) Already successfully applied in other EU initiatives like IntUBE

13. An Example Informal Sinphonie Ontology, small part as example: Sites (150x, 5 per country) Environment Outdoor Air Quality Measurements (18x) CO2 in ppm RelativeHumidity in % School ClassRooms (3x) RoomType Indoor Air Quality Measurments (18x) CO2 in ppm RelativeHumidity in % (Health/Well-being measurements)

14. Sinphonie BIM/SIM/PIM Ontology Sinphonie BIM/SIM/PIM Workbook OWLXLS * Joseki RDF Server/TDB (Excel-2-OWL Converter) Semantic Content * OWL Configurator An Example Formalisation

15. Example: Excel workbook /1

16. Example: Excel workbook /2

17. Example: Excel workbook /3

18. Example: Excel workbook /4

19. Example: Excel workbook /5

20. Example: Excel-2-owl mapping

21. Example: Manual Instantiation with PMO Configurator saved as: Instance_School_1.owl at: www.bimtoolset.org/ontologies

22. Example: Put in on the Joseki RDF Server (in TDB data store) SPARQL Update query: LOAD http://www.bimtoolset.org/ontologies/Instance_School_1.owl

23. Example: Query the data /1 #################################################### # Get all triples #################################################### SELECT * { GRAPH { ?x ?y ?z } } http://www.bimtoolset.org/ontologies/IntUBE-EnergyBIM-Data

24. Example: Query the data /2

25. Example: Query the data /3 ########################################################## ## Get all triples of the bim_data ########################################################## PREFIX rdf: PREFIX sin: SELECT * { GRAPH {?x a sin:School} }

26. Example: Sensor data Query the data /4 SPARQLer Query Results x

27. Example: Query the data /5 ################################################ # Get the amount of rooms ################################################ PREFIX rdf: PREFIX sin: SELECT count (?y) WHERE { GRAPH {?y a sin:Room} }

28. Example: Query the data /6 SPARQLer Query Results.1 "4" ^^ The four non-optional rooms for the one school instantiated (without site context).

29. Final example ################################################ # Get all relative humidities per room, per school, per site ################################################ PREFIX rdf: PREFIX sin: SELECT ?x ?y ?z ?z2 WHERE { GRAPH { OPTIONAL {?x a sin:Site}. OPTIONAL {?y a sin:School}. OPTIONAL {?z a sin:Room}. OPTIONAL {?x sin:School ?y}. OPTIONAL {?y sin:Room ?z}. {?z sin:RelativeHumidity-Measurement ?z1}. {?z1 sin:RelativeHumidity-value ?z2}. }}

30. Final example Query Result, now in JSON syntax output { "head": { "vars": [ "x", "y", "z", "z2" ] }, "results": { "bindings": [ { "y": { "type": "uri", "value": "http://www.bimtoolset.org/ontologies/Instance_School_1.owl#Instance_School_1" }, "z": { "type": "uri", "value": "http://www.bimtoolset.org/ontologies/Instance_School_1.owl#Instance_Room_4" }, "z2": { "datatype": "xsd:float", "type": "typed-literal", "value": "90." } } ] }}

31. Example: More Much more beyond Sparql Select: Construct/Describe No “XML Result” results but RDF/XML code Sparql Update (beyond “load”) Insert new triples Jena ARQ beyond standard Sparql LET () Various functionalities / “magic properties” Like Membership of SET Very powerfull ! Especially in combination with SPIN > OO Ontologies