An Architecture for Real Time Data Acquisition and Online Signal Processing for High Throughput Tandem Mass Spectrometry.
DOI: 10.1109/e-Science.2009.21 Conference: Fifth International Conference on e-Science, e-Science 2009, 9-11 December 2009, Oxford, UK
Independent, greedy collection of data events using simple heuristics results in massive over-sampling of the prominent data features in large-scale studies over what should be achievable through ¿intelligent", online acquisition of such data. As a result, data generated are more aptly described as a collection of a large number of small experiments rather than a true large-scale experiment. Nevertheless, achieving ¿intelligent¿, online control requires tight interplay between state-of-the-art, data-intensive computing infrastructure developments and analytical algorithms. In this paper, we propose a Software Architecture for Mass spectrometry-based Proteomics coupled with Liquid chromatography Experiments (SAMPLE) to develop an ¿intelligent¿ online control and analysis system to significantly enhance the information content from each sensor (in this case, a mass spectrometer). Using online analysis of data events as they are collected and decision theory to optimize the collection of events during an experiment, we aim to maximize the information content generated during an experiment by the use of pre-existing knowledge to optimize the dynamic collection of events.
Article: Components in the Pipeline[Show abstract] [Hide abstract]
ABSTRACT: State-of-the-art scientific instruments and simulations routinely produce massive datasets requiring intensive processing to disclose key features of the artifact or model under study. Scientists commonly call these data-processing pipelines, which are structured according to the pipe and-filter architecture pattern.<sup>1</sup> Different stages typically communicate using files; each stage is an executable program that performs the processing needed at that point in the pipeline.The MeDICi (Middleware for Data-Intensive Computing) Integration Framework supports constructing complex software pipelines from distributed heterogeneous components and controlling qualities of service to meet performance, reliability and communication requirements.IEEE Software 07/2011; 28(3-28):34 - 40. DOI:10.1109/MS.2011.23 · 1.05 Impact Factor
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