Upload
ruleml
View
220
Download
0
Tags:
Embed Size (px)
Citation preview
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
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
2
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
3
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
4
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
5
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
7
Technical challenges
8
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
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
9
Challenges in data sharing and data security
• Data security, which data to share and with whom • Personal identifiable information • Some special types of properties
10
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
11
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
12
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
13
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
14
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.
15
Result
16
• 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
Importance
• Reduce the scope of data collection
• Improve the data quality
• Makes data easily accessible and usable for analysis
17
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
18
Thank you! Contact: [email protected]
http://prodatamarket.eu
http://www.statsbygg.no
@prodatamarket