RIS ID
80660
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.
Publication Details
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.