Semantic Technologies for Spatial Infrastructures€¢ Vision: – “Seamless access to the right...
Transcript of Semantic Technologies for Spatial Infrastructures€¢ Vision: – “Seamless access to the right...
Semantic Technologies for Spatial Infrastructures
An update on progress in Program 3 of the
CRCSI
Professor Geoff West
• Commenced January 2012 • Initial discussions and landscape/literature search • Consultations with 43plers, jurisdictions, agencies, CRCSI, VANZI, ANZSM • Joint ANZLIC/CRCSI meeting, April 2012 • CRCSI Conference, Brisbane, May 2012 • GSDI 2012, Quebec, 2012 • Further literature search of main journals/conferences • Generation of documents:
– Research Strategy – Addressing the milestones
• CRCSI Board – endorsement • Final approval from ANZLIC, PSMA, Program 3 Board (CRCSI), RIC (CRCSI), REC
(CRCSI) • Engagement Workshops – done • Project proposal generation – done • Projects running – now!
Research Strategy Consultation
• Vision: – “Seamless access to the right spatial knowledge at
the right time, in the right format, at the right price”
• Outcomes: – Intelligent search and discovery – Vertical and horizontal linking of data and
processes – Orchestration of web services – Crowd sourced integration – Fast and effective big data querying – Supply chain management and processing
Vision and Outcomes
User demands Core research Test beds Use cases Adoption
From Drivers to Utilisation
Artificial Intelligence
Supply Chains
Semantic Web Web-based
Search
Querying
Crowd Sourcing
Marketplaces
Orchestration
Data Integration
Licensing
PSMA
GA
GLOBES
FSDF
SLIP
LINZ
ASDI
FSDF
SLIP future
Health
Urban planning
Big data queries
Biomass business
Property models
Federal data integration
State data integration
Disaster management
Research activities
Landgate DSE LPI PSMA crowdsourcing
…
Users Catalogues
Ontologies Vocabularies
Metadata
Data & Metadata
High Performance Computing
ABS BoM GA Private Sector
…
Big Data co-located with orchestrated processing, modelling and prediction
Data Data
Data
Data
Modelling Local Orchestrated Processing Search
Querying
Visualisation Inference
Data Integration
Smarts - code
Search
Modelling
LINZ
… …
Existing open software standards and architectures e.g. SISS
Solution: Linking Data and Processes
• Don’t want data, want results – Leave data where it is, run processes in situ – Use web services – Use standards (OGC, W3C, OASIS etc.) – Utilise many online sources
• Want stability of sources – Cool URIs – Persistent URIs
• (www.handle.net, http://dx.doi.org/10.1000/182)
Principles
Semantic Web technology stack visualization (Benjamin Nowack 2011)
Elements of the Semantic Web
Spatial Information Projects
• Funding: $600k per year • Project 3.01 – Search and Discovery, Federated Models
• Research Fellow – Curtin: Dr David McMeekin • Three PhD students – Curtin • Curtin, NGIS, Amristar, PSMA, Landgate (WA), DEPI (VIC) • Co-supervision of students – CSIRO: Simon Cox, Paul Box
• Project 4.17/3.03 – Querying big data, orchestration – 3D/4D datasets • Joint P3/P4 project • Research Fellow – GA • Research Fellow – QUT • QUT, GA, DEPI (VIC), OEH (NSW), DSITIA (QLD)
• Project 3.02 – Supply chains, orchestration, crowd sourcing, licensing • Research Fellow – Curtin: Dr David McMeekin • Research Fellow – Curtin: Dr Lesley Arnold • Three PhD students – Curtin • Three PhD students – Canterbury • Curtin, Canterbury, Landgate (WA), DEPI (VIC), DNRM (QLD), LINZ, PSMA,
Omnilink, NGIS, AAM, OSP • CSIRO involvement
• Project 4.1X – Biomass Business 2: Farmer centric crowd sourcing • UNE, Canterbury
• Good queries: – “Who should I evacuate
from the flood zone in the next 24 hours?”
– “Am I at risk of death/injury by purchasing this house?”
– “Which houses are unoccupied after the earthquake?”
• Bad query: – Give me all the data you’ve
got, I’ll work it out!
• How about:
Search and Discovery – Tristan
From nbc.com
http://sensorweb.ncsa.uiuc.edu/api/event/tornados?intersects= http://sensorweb.ncsa.uiuc.edu/data/map/state/USGS/Illinois Using Linked Data in a heterogeneous Sensor Web: challenges, experiments and lessons learned Liang Yua & Yong Liub, IJDE, Sept. 2013
• “how does solid water melting influence stream flow in the Arctic Region over the summer?”
P3.01: Search and Discovery - Tristan
Semantic-based web service discovery and chaining for building an Arctic spatial data infrastructure, Li et al (2011), Computers and Geosciences, Vol 37.
event state change
place time
• Can Google find everything? – getcapabilities WMS soil Italy – Google Search Appliance – Voyager – Omniscient – Geonetwork – Swoogle – www.opensearch.org – www.ontology.com – www.govpond.org
• Generate rdf triple stores of derived metadata?
P3.01: Search and Discovery - Chet
P3.01: Federated Data - Jeremy
Given FSDF models • Jurisdictional models examined and
generated • Federate seamlessly from jurisdictions • Leave data where it is • Linked data including fonts, colours
etc.
• Research – models and tools: – Integrate disparate and complex
multiple supply chains – Integrate crowd sourcing into
supply chains and combine with authoritative data
– Model the various crowd sourcing data suppliers
– Model business rules, people – Deal with currency, provenance
and versioning of spatial data – Link data and processes vertically
and horizontally e.g. from LGAs up to the Commonwealth and across different datasets
P3.04: Supply Chains – General
Primary suppliers
Final customers
VIRTUOSO Universal Server: •Middleware and database engine hybrid. •Combines the functionality of a traditional RDBMS, ORDBMS, virtual databases, RDF, XML, free-text, web application server and file server functionality in a single system. •RDF data can either be stored directly in Virtuoso or can be created on the fly from non-RDF relational databases based on a mapping. •SPARQL query statements can be written to fetch the resulting dataset as XML files and then the resulting datasets will be used to analyse model appropriateness and update the source geo-databases.
P3.04: Supply Chains - Latha
Collaborative Ontology Generation- Reza
Collaborative Ontology Development for the Geosciences
Collaborative Ontology Development for the Geosciences, Reza Kalbashi et al, Trans. In GIS, 2013.
• Investigate landscape • Model processes, activities, data and links
– UML
• Utilise Semantic Web techniques – URIs – RDF, Ontologies, Rules, Description Logics – Traditional databases, data formats
• Build Tools – Web-based
• Evaluate
Research Methodology