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Bio-inspired cost-effective access to big data

Conference Paper


Download full-text (Open Access)

Abstract


  • With the rapid proliferation of

    services and cloud computing, Big Data has

    become a significant phenomenon across

    many scientific disciplines and sectors of

    society, wherever huge amounts of data are

    generated and processed daily. End users

    will always seek higher-quality data access at

    lower prices. This demand poses challenges

    to service composers, service providers and

    data providers, who should maintain their

    service and data provision as cost-effectively

    as possible. This paper will apply bio-inspired

    approaches to achieving equilibrium among

    the otherwise competitive stakeholders. In

    addition to novel models of cost for Big Data

    provision, bio-inspired algorithms will be

    developed and validated for dynamic optimisation.

    Furthermore, the optimised algorithms

    will also be applied in the data-mining research

    on the Alpha Magnetic Spectrometer

    (AMS) experiment, which is aiming to find

    dark matter in the universe. This experiment

    typically receives 200G and generates 700G

    data daily.

UOW Authors


  •   Wang, Lijuan (external author)
  •   Shen, Jun

Publication Date


  • 2014

Citation


  • Wang, L. & Shen, J. (2014). Bio-inspired cost-effective access to big data. In A. Campbell & P. Perez (Eds.), International Symposium for Next Generation Infrastructure (ISNGI 2013) (pp. 243-249). Australia: University of Wollongong.

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=5183&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/isngi2013/proceedings/1/42/

Start Page


  • 243

End Page


  • 249

Place Of Publication


  • Australia

Abstract


  • With the rapid proliferation of

    services and cloud computing, Big Data has

    become a significant phenomenon across

    many scientific disciplines and sectors of

    society, wherever huge amounts of data are

    generated and processed daily. End users

    will always seek higher-quality data access at

    lower prices. This demand poses challenges

    to service composers, service providers and

    data providers, who should maintain their

    service and data provision as cost-effectively

    as possible. This paper will apply bio-inspired

    approaches to achieving equilibrium among

    the otherwise competitive stakeholders. In

    addition to novel models of cost for Big Data

    provision, bio-inspired algorithms will be

    developed and validated for dynamic optimisation.

    Furthermore, the optimised algorithms

    will also be applied in the data-mining research

    on the Alpha Magnetic Spectrometer

    (AMS) experiment, which is aiming to find

    dark matter in the universe. This experiment

    typically receives 200G and generates 700G

    data daily.

UOW Authors


  •   Wang, Lijuan (external author)
  •   Shen, Jun

Publication Date


  • 2014

Citation


  • Wang, L. & Shen, J. (2014). Bio-inspired cost-effective access to big data. In A. Campbell & P. Perez (Eds.), International Symposium for Next Generation Infrastructure (ISNGI 2013) (pp. 243-249). Australia: University of Wollongong.

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=5183&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/isngi2013/proceedings/1/42/

Start Page


  • 243

End Page


  • 249

Place Of Publication


  • Australia