Skip to main content
placeholder image

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.

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.

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