Data-intensive architecture for scientific knowledge discovery

Malcolm Atkinson, Chee Sun Liew, Michelle Galea, Paul Martin, Amrey Krause, Adrian Mouat, Oscar Corcho, David Snelling

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

This paper presents a data-intensive architecture that demonstrates the ability to support applications from a wide range of application domains, and support the different types of users involved in defining, designing and executing data-intensive processing tasks. The prototype architecture is introduced, and the pivotal role of DISPEL as a canonical language is explained. The architecture promotes the exploration and exploitation of distributed and heterogeneous data and spans the complete knowledge discovery process, from data preparation, to analysis, to evaluation and reiteration. The architecture evaluation included large-scale applications from astronomy, cosmology, hydrology, functional genetics, imaging processing and seismology.
Original languageEnglish
Pages (from-to)307-324
JournalDistributed and Parallel Databases
Issue number5-6
Publication statusPublished - 1 Oct 2012


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