<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</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%">Baxter, Robert M.</style></author><author><style face="normal" font="default" size="100%">Peter Brezany</style></author><author><style face="normal" font="default" size="100%">Oscar Corcho</style></author><author><style face="normal" font="default" size="100%">Michelle Galea</style></author><author><style face="normal" font="default" size="100%">Parsons, Mark</style></author><author><style face="normal" font="default" size="100%">Snelling, David</style></author><author><style face="normal" font="default" size="100%">van Hemert, Jano</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The DATA Bonanza: Improving Knowledge Discovery in Science, Engineering, and Business</style></title><secondary-title><style face="normal" font="default" size="100%">Wiley Series on Parallel and Distributed Computing (Editor: Albert Y. Zomaya)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Big Data</style></keyword><keyword><style  face="normal" font="default" size="100%">Data Intensive</style></keyword><keyword><style  face="normal" font="default" size="100%">data mining</style></keyword><keyword><style  face="normal" font="default" size="100%">Data Streaming</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases</style></keyword><keyword><style  face="normal" font="default" size="100%">Dispel</style></keyword><keyword><style  face="normal" font="default" size="100%">Distributed Computing</style></keyword><keyword><style  face="normal" font="default" size="100%">Knowledge Discovery</style></keyword><keyword><style  face="normal" font="default" size="100%">Workflows</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">John Wiley &amp; Sons Inc.</style></publisher><pages><style face="normal" font="default" size="100%">580</style></pages><isbn><style face="normal" font="default" size="100%">978-1-118-39864-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">With the digital revolution opening up tremendous opportunities in many fields, there is a growing need for skilled professionals who can develop data-intensive systems and extract information and knowledge from them. This book frames for the first time a new systematic approach for tackling the challenges of data-intensive computing, providing decision makers and technical experts alike with practical tools for dealing with our exploding data collections.  Emphasising data-intensive thinking and interdisciplinary collaboration,  The DATA Bonanza: Improving Knowledge Discovery in Science, Engineering, and Business examines the essential components of knowledge discovery, surveys many of the current research efforts worldwide, and points to new areas for innovation. Complete with a wealth of examples and DISPEL-based methods demonstrating how to gain more from data in real-world systems, the book:
* Outlines the concepts and rationale for implementing data-intensive computing in organisations
* Covers from the ground up problem-solving strategies for data analysis in a data-rich world
* Introduces techniques for data-intensive engineering using the Data-Intensive Systems Process Engineering Language DISPEL
* Features in-depth case studies in customer relations, environmental hazards, seismology, and more
* Showcases successful applications in areas ranging from astronomy and the humanities to transport engineering
* Includes sample program snippets throughout the text as well as additional materials on a companion website
The DATA Bonanza is a must-have guide for information strategists, data analysts, and engineers in business, research, and government, and for anyone wishing to be on the cutting edge of data mining, machine learning, databases, distributed systems, or large-scale computing.</style></abstract></record><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%">Fan Zhu</style></author><author><style face="normal" font="default" size="100%">Rodríguez, David</style></author><author><style face="normal" font="default" size="100%">Carpenter, Trevor K.</style></author><author><style face="normal" font="default" size="100%">Atkinson, Malcolm P.</style></author><author><style face="normal" font="default" size="100%">Wardlaw, Joanna M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Lesion Area Detection Using Source Image Correlation Coefficient for CT Perfusion Imaging</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Journal of Biomedical and Health Informatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CT , Pattern Recognition , Perfusion Source Images , Segmentation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2013</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">PP</style></volume><pages><style face="normal" font="default" size="100%">1-8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Computer tomography (CT) perfusion imaging is widely used to calculate brain hemodynamic quantities such as Cerebral Blood Flow (CBF), Cerebral Blood Volume (CBV) and Mean Transit Time (MTT) that aid the diagnosis of acute stroke. Since perfusion source images contain more information than hemodynamic maps, good utilisation of the source images can lead to better understanding than the hemodynamic maps alone. Correlation-coefficient tests are used in our approach to measure the similarity between healthy tissue time-concentration curves and unknown curves. This information is then used to differentiate penumbra and dead tissues from healthy tissues. The goal of the segmentation is to fully utilize information in the perfusion source images. Our method directly identifies suspected abnormal areas from perfusion source images and then delivers a suggested segmentation of healthy, penumbra and dead tissue. This approach is designed to handle CT perfusion images, but it can also be used to detect lesion areas in MR perfusion images.</style></abstract><issue><style face="normal" font="default" size="100%">99</style></issue></record><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><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%">Chee Sun Liew</style></author><author><style face="normal" font="default" size="100%">Atkinson, Malcolm P.</style></author><author><style face="normal" font="default" size="100%">Radoslaw Ostrowski</style></author><author><style face="normal" font="default" size="100%">Murray Cole</style></author><author><style face="normal" font="default" size="100%">van Hemert, Jano I.</style></author><author><style face="normal" font="default" size="100%">Liangxiu Han</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Performance database: capturing data for optimizing distributed streaming workflows</style></title><secondary-title><style face="normal" font="default" size="100%">Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">measurement framework</style></keyword><keyword><style  face="normal" font="default" size="100%">performance data</style></keyword><keyword><style  face="normal" font="default" size="100%">streaming workflows</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">369</style></volume><pages><style face="normal" font="default" size="100%">3268-3284</style></pages><abstract><style face="normal" font="default" size="100%">The performance database (PDB) stores performance-related data gathered during workflow enactment. We argue that by carefully understanding and manipulating this data, we can improve efficiency when enacting workflows. This paper describes the rationale behind the PDB, and proposes a systematic way to implement it. The prototype is built as part of the Advanced Data Mining and Integration Research for Europe project. We use workflows from real-world experiments to demonstrate the usage of PDB.</style></abstract><issue><style face="normal" font="default" size="100%">1949</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Chee Sun Liew</style></author><author><style face="normal" font="default" size="100%">Atkinson, Malcolm P.</style></author><author><style face="normal" font="default" size="100%">van Hemert, Jano</style></author><author><style face="normal" font="default" size="100%">Liangxiu Han</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Optimising Distributed Data Streaming Graphs using Parallel Streams</style></title><secondary-title><style face="normal" font="default" size="100%">Data Intensive Distributed Computing (DIDC'10), in conjunction with the 19th International Symposium on High Performance Distributed Computing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Data-intensive Computing</style></keyword><keyword><style  face="normal" font="default" size="100%">Distributed Computing</style></keyword><keyword><style  face="normal" font="default" size="100%">Optimisation</style></keyword><keyword><style  face="normal" font="default" size="100%">Parallel Stream</style></keyword><keyword><style  face="normal" font="default" size="100%">Scientific Workflows</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">20/06/2010</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.cct.lsu.edu/~kosar/didc10/index.php</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">Chicago, Illinois</style></pub-location><pages><style face="normal" font="default" size="100%">725-736</style></pages><abstract><style face="normal" font="default" size="100%">Modern scientific collaborations have opened up the opportunity of solving complex problems that involve multi- disciplinary expertise and large-scale computational experiments. These experiments usually involve large amounts of data that are located in distributed data repositories running various software systems, and managed by different organisations. A common strategy to make the experiments more manageable is executing the processing steps as a workflow. In this paper, we look into the implementation of fine-grained data-flow between computational elements in a scientific workflow as streams. We model the distributed computation as a directed acyclic graph where the nodes represent the processing elements that incrementally implement specific subtasks. The processing elements are connected in a pipelined streaming manner, which allows task executions to overlap. We further optimise the execution by splitting pipelines across processes and by introducing extra parallel streams. We identify performance metrics and design a measurement tool to evaluate each enactment. We conducted ex- periments to evaluate our optimisation strategies with a real world problem in the Life Sciences—EURExpress-II. The paper presents our distributed data-handling model, the optimisation and instrumentation strategies and the evaluation experiments. We demonstrate linear speed up and argue that this use of data-streaming to enable both overlapped pipeline and parallelised enactment is a generally applicable optimisation strategy.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Liangxiu Han</style></author><author><style face="normal" font="default" size="100%">van Hemert, Jano</style></author><author><style face="normal" font="default" size="100%">Richard Baldock</style></author><author><style face="normal" font="default" size="100%">Atkinson, Malcolm P.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Ronghuai Huang</style></author><author><style face="normal" font="default" size="100%">Qiang Yang</style></author><author><style face="normal" font="default" size="100%">Jian Pei</style></author><author><style face="normal" font="default" size="100%">et al</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Automating Gene Expression Annotation for Mouse Embryo</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (Advanced Data Mining and Applications, 5th International Conference)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">LNAI 5678</style></volume><pages><style face="normal" font="default" size="100%">469-478</style></pages></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</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%">van Hemert, Jano</style></author><author><style face="normal" font="default" size="100%">Liangxiu Han</style></author><author><style face="normal" font="default" size="100%">Ally Hume</style></author><author><style face="normal" font="default" size="100%">Chee Sun Liew</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Distributed Architecture for Data Mining and Integration</style></title><secondary-title><style face="normal" font="default" size="100%">Data-Aware Distributed Computing (DADC'09), in conjunction with the 18th International Symposium on High Performance Distributed Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pages><style face="normal" font="default" size="100%">11-20</style></pages><abstract><style face="normal" font="default" size="100%">This paper presents the rationale for a new architecture to support a signiﬁcant increase in the scale of data integration and data mining. It proposes the composition into one framework of (1) data mining and (2) data access and integration. We name the combined activity “DMI”. It supports enactment of DMI processes across heterogeneous and distributed data resources and data mining services. It posits that a useful division can be made between the facilities established to support the deﬁnition of DMI processes and the computational infrastructure provided to enact DMI processes. Communication between those two divisions is restricted to requests submitted to gateway services in a canonical DMI language. Larger-scale processes are enabled by incremental reﬁnement of DMI-process deﬁnitions often by recomposition of lower-level deﬁnitions. Autonomous types and descriptions which will support detection of inconsistencies and semi-automatic insertion of adaptations.These architectural ideas are being evaluated in a feasibility study that involves an application scenario and representatives of the community. 
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barker, Adam</style></author><author><style face="normal" font="default" size="100%">van Hemert, Jano I.</style></author><author><style face="normal" font="default" size="100%">Baldock, Richard A.</style></author><author><style face="normal" font="default" size="100%">Atkinson, Malcolm P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An e-Infrastructure for Collaborative Research in Human Embryo Development</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">01/2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/pdf/0901.2310v1</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">6</style></pages><abstract><style face="normal" font="default" size="100%">Within the context of the EU Design Study Developmental Gene Expression Map, we identify a set of challenges when facilitating collaborative research on early human embryo development. These challenges bring forth requirements, for which we have identified solutions and technology. We summarise our solutions and demonstrate how they integrate to form an e-infrastructure to support collaborative research in this area of developmental biology. </style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barker, Adam</style></author><author><style face="normal" font="default" size="100%">van Hemert, Jano I.</style></author><author><style face="normal" font="default" size="100%">Baldock, Richard A.</style></author><author><style face="normal" font="default" size="100%">Atkinson, Malcolm P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An E-infrastructure to Support Collaborative Embryo Research</style></title><secondary-title><style face="normal" font="default" size="100%">Cluster Computing and the Grid</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE Computer Society</style></publisher><pages><style face="normal" font="default" size="100%">520--525</style></pages><isbn><style face="normal" font="default" size="100%">978-0-7695-3622-4</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">inproceedings</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Grant, Alistair</style></author><author><style face="normal" font="default" size="100%">Antonioletti, Mario</style></author><author><style face="normal" font="default" size="100%">Hume, Alastair C.</style></author><author><style face="normal" font="default" size="100%">Krause, Amy</style></author><author><style face="normal" font="default" size="100%">Dobrzelecki, Bartosz</style></author><author><style face="normal" font="default" size="100%">Jackson, Michael J.</style></author><author><style face="normal" font="default" size="100%">Parsons, Mark</style></author><author><style face="normal" font="default" size="100%">Atkinson, Malcolm P.</style></author><author><style face="normal" font="default" size="100%">Theocharopoulos, Elias</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">OGSA-DAI: Middleware for Data Integration: Selected Applications</style></title><secondary-title><style face="normal" font="default" size="100%">ESCIENCE '08: Proceedings of the 2008 Fourth IEEE International Conference on eScience</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE Computer Society</style></publisher><pub-location><style face="normal" font="default" size="100%">Washington, DC, USA</style></pub-location><pages><style face="normal" font="default" size="100%">343</style></pages><isbn><style face="normal" font="default" size="100%">978-0-7695-3535-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">inproceedings</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Berlich, R{\&quot;u}diger</style></author><author><style face="normal" font="default" size="100%">Hardt, Marcus</style></author><author><style face="normal" font="default" size="100%">Kunze, Marcel</style></author><author><style face="normal" font="default" size="100%">Atkinson, Malcolm P.</style></author><author><style face="normal" font="default" size="100%">Fergusson, David</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">EGEE: building a pan-European grid training organisation</style></title><secondary-title><style face="normal" font="default" size="100%">ACSW Frontiers</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><pages><style face="normal" font="default" size="100%">105-111</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">inproceedings</style></work-type></record><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%">Antonioletti, Mario</style></author><author><style face="normal" font="default" size="100%">Atkinson, Malcolm P.</style></author><author><style face="normal" font="default" size="100%">Baxter, Robert M.</style></author><author><style face="normal" font="default" size="100%">Borley, Andrew</style></author><author><style face="normal" font="default" size="100%">Hong, Neil P. 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