<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Atkinson, Malcolm P.</style></author><author><style face="normal" font="default" size="100%">Chee Sun Liew</style></author><author><style face="normal" font="default" size="100%">Michelle Galea</style></author><author><style face="normal" font="default" size="100%">Paul Martin</style></author><author><style face="normal" font="default" size="100%">Krause, Amrey</style></author><author><style face="normal" font="default" size="100%">Adrian Mouat</style></author><author><style face="normal" font="default" size="100%">Oscar Corcho</style></author><author><style face="normal" font="default" size="100%">Snelling, David</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data-Intensive Architecture for Scientific Knowledge Discovery</style></title><secondary-title><style face="normal" font="default" size="100%">Distributed and Parallel Databases</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Knowledge discovery, workflow management system</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/s10619-012-7105-3</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">30</style></volume><pages><style face="normal" font="default" size="100%">307-324</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue></record></records></xml>