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Easy SPARQLing for the Building Performance Professional


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Slides of Martin Kaltenböcks (SWC) presentation at SEMANTiCS2014 conference in Leipzig on 5th of September 2014 about the 'Tool for Building Energy Performance Scenarios' of GBPN (Global Buildings Performance Network, that provides a prediction tool for buildings performance worldwide by making use of Linked Open Data (LOD).

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Easy SPARQLing for the Building Performance Professional

  1. 1. Easy SPARQLing for the Building Performance Professional SEMANTiCS2014, 5.9.2014, Leipzig, Germany @semwebcompany @kalte2707 Martin Kaltenböck Semantic Web Company These slides are published under :
  2. 2. About Semantic Web Company • Semantic Web Company founded 2001 in Vienna, Austria • 20 experts in strategy, coding, consulting, research & PM • Main Product: PoolParty Semantic Suite (launched 2009) • Focus: Semantic Information Management & LOD Services and Information- & Data Portals / Tools • Serving global 500 companies, GOV & NGOs worldwide • EU- & US-based consulting services Partner network:
  3. 3. Problem Statement: Why use LOD for Sustainable Development
  4. 4. Data Analytics 2.0 - The islands are now open for the experts
  5. 5. 5 (Linked) Open Data for Sustainable Development What is important… What is important when thinking about OPEN DATA in use? • Interoperability to ensure broad & easy use of data • Human AND machine readable data plus: metadata (!!) • In common open formats & open standards • For smooth and cost efficient data integration (re-use) • To generate effects : local – regional – national –worldwide For several target groups with several interests! Politicians & decision makers | Public administration & project developers | Citizens (Citizen Analysts) | Economy & Industry | (Data) Journalists, media & publishers | Academia & Science
  6. 6. Misconceptions about Linked Open Data • All of us have to use ONE schema • Everything needs to be switched to RDF • We all have to learn SPARQL, there are no standard (web) APIs • LOD is a pure academic approach • LOD can only be used by Semantic Web experts • We have to change our data integration & -management approaches
  7. 7. Solution: LOD for Sustainable Development !
  8. 8. 8 Linked Open Data (LOD) for Sustainable Development Why Linked Open Data for sustainable development • Awareness building & education • Participation through information • Enable & boost innovation • Knowledge sharing & showcasing best practise • Enable better decision making
  9. 9. Customers I-netexrtorapcetrability & Standards Henry Maudslay (1771 – 1831) He also developed the first industrially practical screw-cutting lathe in 1800, allowing standardisation of screw thread sizes for the first time. This allowed the concept of interchangeability (a idea that was already taking hold) to be practically applied to nuts and bolts. Before this, all nuts and bolts had to be made as matching pairs only. This meant that when machines were disassembled, careful account had to be kept of the matching nuts and bolts ready for when reassembly took place. John Sheridan, OGD2011 Conference, Vienna, Austria:
  10. 10. • Less replication (offering same datasets in different places) • Encouragement to re-use existing datasets (open standards, open formats, reduce semantic & syntactic heterogeneity) • Clear which datasets are providing similar / same information • More innovation and creation of new knowledge by contextualisation • Creating network effects by linking • Enable scale out of the growing amounts of data The Benefits of Linked Open Data for Sustainable Development 10
  11. 11. Data Analytics 3.0 – Connected islands based on open standards 11
  12. 12. GBPN – Global Buildings Performance Network
  13. 13. Global Buildings Performance Network (GBPN)
  14. 14. GBPN: Need of Data & Information • Funded by Climate Works • Mission to prevent Climate Change • By support to Governments to adopt better policies • Adoption of international agreements • Philantrophie  Linked AND Open … fits with GBPN mission
  15. 15. GBPN: Need of Data & Information
  16. 16. GBPN: Need of Data & Information
  17. 17. GBPN - Tool for Building Energy Performance Scenarios
  18. 18. GBPN: Idea of a MRV Tool What is a Modeling Tool? • It is a platform to structure, organize and analyze data taking into account a certain analytical goal What is MRV? Measurable, Reportable & Verifiable – parameters under which the mitigation actions should be undertaken 2007 Bali Action Plan The main goal of a Modeling MRV Tool is To provide a way to • measure energy use and related GHG emissions from buildings & serve as the basis for assessing the potential for certain mitigation actions • report on the country’s/city’s GHG emissions and appropriate mitigation actions • verify the compliance with the mitigation commitments by feeding in the updated data
  19. 19. The Data: A Multidimensionale Cube 46 Years World, 11 or 4 Regions 3 Scenarios 3 Options for End-Uses 17 Climate Zones 5 Building Vintages 3 Building Types, 16 Categories The output is constituted from the selection of different dimensions  Type of the output: CHART(S) or TABLE
  20. 20. Tool for Building Energy Performance Scenarios
  21. 21. Tool for Building Energy Performance Scenarios
  22. 22. Tool for Building Energy Performance Scenarios
  23. 23. Tool for Building Energy Performance Scenarios
  24. 24. Facts & Figures – GBPN MRV Tool • Source System: MS Access DB • RDF: Data Cube Vocabulary • Cube with 17 dimensions • Output ~ 235 Million Triples • Linked to DBpedia & Geonames & GBPN Terminology • CC-BY-3.0 France License • Virtuoso Triple Store for data storage and SPARQL Endpoint: • ETL & other LOD Jobs: UnifiedViews • D2R RDF Conversion: • PoolParty Server for add. Linkting to GBPN Taxonomies:
  25. 25. Other GBPN (LO)Data Tools The GBPN Policy Comparative Tool: The GBPN Facts & Figures Tool: The GBPN News Aggregator Tool: The Linked Open Data (LOD) based GBPN Terminology:
  26. 26. Contact Martin Kaltenböck, Semantic Web Company GmbH Mariahilfer Strasse 70/8, A-1070 Vienna +43-1-4021235
  27. 27. The Power of Linked Open Data • Enables web-scale data publishing - distributed publication with web-based discovery mechanisms • Everything is a resource – follow your nose to discover more about properties, classes, or codes within a code list • Everything can be annotated - make comments about observations, data series, points on a map • Easy to extend - create new properties as required, no need to plan everything up-front • Easy to merge - slot together RDF graphs, no need to worry about name clashes • Easy use and re-use on top of common schemas AND schema mapping • Allows complex querying of several distributed data sources & systems