Spatial Infrastructures - CRC for Spatial Information ¢â‚¬â€œ¢â‚¬“Seamless access to the right spatial knowledge

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  • Spatial Infrastructures Professor Geoff West

    Knowledge

    Data

    Information

    Wisdom

    Twitter: #crcsi

  • Vision and Outcomes • 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

    thetowerofbabel.net

    www.independent.co.uk

  • From Drivers to Utilisation

  • Virtual Spatial Infrastructure

    Already there

    Developing

  • Principles

    • Don’t necessarily want data, want information

    – Leave data where it is, access when needed

    – Use web services and online resources

    – Use standards (OGC, W3C etc.)

    – Run processes in situ with data

    – Build on top of available resources – no need to change

    underlying services!

    Want: Don’t want:

    http://www.sgio.com.au/wa/carcreationhttp://www.tflcar.com/2013/02/ is-the-2014-alfa-romeo-4c-the-next-lotus-elise-exige/

    http://ridesinfo.com/ land-rover-defender-white-wallpapers/

    or

    http://www.mkbergman.com/229/climbing-the- data-federation-pyramid/ [accessed 25th Oct 2014]

    http://www.tflcar.com/2013/02/ http://ridesinfo.com/ http://www.mkbergman.com/229/climbing-the-data-federation-pyramid/

  • The Semantic Web

    Semantic Web technology stack visualization (Benjamin Nowack 2011)

    http://bnode.org/blog/2009/07/08/the-semantic-web-not-a-piece-of-cake http://bnode.org/about

  • Artificial Intelligence • Highest levels of the Semantic Web

    • Application of rules derive knowledge from data through

    inference (first order logic):

    – Facts: Father(Fred, Bill), Father(Bill, John)

    – Rule: Grandfather(X,Y) if Father(X,Z) and Father(Z,Y)

    – Inference: Grandfather(Fred, John)

    • Description logics – querying and processing ontologies

    (derived from first order logic)

    – Find anomalies and errors in datasets

    – Infer new knowledge

    – Map different datasets to each other

  • Current Projects • Project 3.01 – Search and Discovery, Federated Models

    – Research Fellow – Curtin: David McMeekin

    – PhD students – Curtin (three)

    – NGIS, Amristar, PSMA, Landgate, DELWP, CSIRO, Omnilink

    • Project 3.02 – Supply chains, orchestration, crowd

    sourcing, licensing

    • Research Fellows –David McMeekin, Lesley Arnold

    • PhD students – Curtin (three), Canterbury (three)

    • Loc8 WA Board, DELWP, Canterbury City Council, LINZ, PSMA,

    NGIS, Omnilink

    • Project 4.17/3.03 – Querying big data – 3D/4D datasets

    – Joint P3/P4 project

    • Research Fellows – GA (one) and QUT (one)

    • GA, DELWP, QDERM et al

  • Participant Engagement • Evolving requirements requiring agility

    – Provenance research – eventuated from workshop on Geocoded

    Addressing (Project P3.10)

    – Conflation of data – eventuated from numerous stakeholder

    meetings (Landgate, DELWP)

    – Federation of data – eventuated from need to seamlessly

    reconcile data from agencies to the FSDF and each other

    – Accessing data through new processes – querying Google Map

    Engine API for Landgate

    – Searching with FIND for GA – build semantics on top

    – 43pl engagement– Omnilink and Omniscience software, AAM

    and Voyager software

  • • Why? – Addresses real world problems - identified by

    stakeholders

    – Seamlessly integrate into stakeholder systems

    • So what? – Logical evolution of information systems, increased

    automation

    • Who benefits? – Everyone from data suppliers through to customers