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Industry@RuleML2015: Norwegian State of Estate A Reporting Service for the State-owned Properties in Norway

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Data distribution
•Public and private
•Data complexity
•Rich in attributes and location based
•Time dimension
•Example of data model from the Norwegian Mapping Authority

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Industry@RuleML2015: Norwegian State of Estate A Reporting Service for the State-owned Properties in Norway

  1. 1. Norwegian State of Estate A Reporting Service for the State-owned Properties in Norway Ling Shi, Bjørg E. Pettersen, Ivar Østhassel, Nikolay Nikolov, Arash Khorramhonarnama, Arne J. Berre and Dumitru Roman @RuleML 2015
  2. 2. Outline • Business Case • Technical challenges • Rule-based solutions • Results • Importance and impacts 2
  3. 3. The public sector owns a significant amount of property data. Re-use of public sector information is required by both the EU and the Norwegian government. EU’s DIRECTIVE 2013/37/EU OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 26 June 2013 amending Directive 2003/98/EC on the re-use of public sector information 3
  4. 4. About Statsbygg • A public sector administration company • Responsible to the Ministry of Local Government and Modernisation (KMD) • Norwegian government's key advisor in construction and property affairs • Building commissioner • Property manager • Property developer 4
  5. 5. Annual reports that assess the efficiency and sustainability of the property included in the government's civil estate https://www.gov.uk/government/collections/state-of-the- estate http://www.theguardian.com/news/datablog/2013/may/21 /downsizing-government-estate State of the estate UK 5
  6. 6. Reporting state-owned real estate properties in Norway • Statsbygg has been responsible for the task • A hard copy of 314 pages and as a PDF file • 6 Person-Months • Data collection with spreadsheets • Quality assurance through • e-mails • phone correspondence 6
  7. 7. The new SoE reporting service 7
  8. 8. Scenarios supported by SoE • Reporting the state-owned properties • Analysis of accessibility of office locations • Risk and vulnerability analysis • E.g. cultural heritage buildings affected by flooding • Analysis of leasing prices • 3rd party services 8
  9. 9. Technical challenges 9 https://kurs.matrikkel.no/matrikkel/docs/Domenemodell.html#analysemodellen_bygg • Data distribution • Public and private • Data complexity • Rich in attributes and location based • Time dimension • Example of data model from the Norwegian Mapping Authority
  10. 10. Challenges in data integration and data quality • Original data formats vs. alternative data formats • Different domain focus and scope • Missing unique identifier in some of the systems • Data updating and consistency 10
  11. 11. Challenges in data sharing and data security • Data security, which data to share and with whom • Personal identifiable information • Some special types of properties 11
  12. 12. Challenges in data analysis • Data analysis based on data • from different sources, both authorized and non-authorized • with different quality • Trustworthiness of the integrated data 12
  13. 13. Rule-based solutions • Rule technologies can be used to meet the challenges in • Data integration • Quality control • Data sharing • Data security • Data analysis • Business rules vs. machine readable rules 13
  14. 14. Rules for data integration and data quality • Definitions, vocabularies and ontology models describe how the data should be integrated • Rules describe under what conditions data can be integrated • Conditional integration • E.g. if the cadastral building number is missing, the building’s address shall be used as the alternative integration key • Flexible data quality validation supported by rules • E.g. addresses and areas registered in the cadastral system and the property management system shall be identical 14
  15. 15. Rules for data sharing and data security • Data ownership and data security: Laws, regulations and business restrictions • Rules on • What to share: e.g. The name, address, area, ownership of buildings shall be shared. • Whom to share with • E.g. Dataset X shall be open to the public. • How to share • E.g. Dataset X shall be shared as a downloadable tabular data and an online mapping service. • Access and security control 15
  16. 16. Rules for data analysis • Trustworthiness of integrated data • Integrated data from SoE service • E.g. Data from the norwegian mapping authority shall have trustworthiness scale 9 of 10. • 3rd party reuse of integrated data from SoE service • E.g. Data collected through crowdsourcing shall have trustworthiness scale 3 of 10. 16
  17. 17. Result 17 • An existing GIS software procedure • Integrate cadastral data and property management data • Results are shown as a map layer in the GIS portal • Example rule for integration: the address of a building in different source systems shall be identical or with known spell variations • Goal: extend the existing procedure into an interactive reporting service for properties data
  18. 18. Importance • Reduce the scope of data collection • Improve the data quality • Makes data easily accessible and usable for analysis 18
  19. 19. Impacts and outcomes • Sharing of Statsbygg’s internal property datasets in novel ways • Exploitation of cadastral data and other cross-sectorial data • A pilot SoE web service using Statsbygg’s data and the integrated cross- sectorial data • Data collection survey for ownership and, possibly, leasing of property data from government agencies • Sharing the survey data results in novel ways • An extended pilot to include survey result, i.e., the public sector’s owned and, possibly, leased properties • Reporting function based on SoE web service • Internationalization process 19
  20. 20. Thank you! Contact: ling.shi@statsbygg.no http://prodatamarket.eu http://www.statsbygg.no @prodatamarket

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