The Development and Applications of Wireless Streaming Media Technology

Wang, Weijiang; Guo, Liang

doi:10.1109/iccsee.2012.419

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…Multidisciplinary science efforts, technologies like Hadoop [113], OpenStack [114], Elastic Cloud Computing [115], Pipeline workflow systems [32, 116] and super high-bandwidth networking [117, 118] will be critical for riding this storm and uncovering novel biomedical knowledge. Embracing the science interactome (the multidisciplinary interactions between biomedical, computational and basic scientific areas, which often lead to new discoveries) will also be essential for establishing, maintaining and expanding the cyclical flow from Biomedical Challenges ↔ Scientific Models ↔ Data Analysis ↔ Computational Infrastructure ↔ Sustainable Education.…”

Section: Discussionmentioning

confidence: 99%

The perfect neuroimaging-genetics-computation storm: collision of petabytes of data, millions of hardware devices and thousands of software tools

Dinov

Petrosyan

Liu

et al. 2013

Brain Imaging and Behavior

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The volume, diversity and velocity of biomedical data are exponentially increasing providing petabytes of new neuroimaging and genetics data every year. At the same time, tens-of-thousands of computational algorithms are developed and reported in the literature along with thousands of software tools and services. Users demand intuitive, quick and platform-agnostic access to data, software tools, and infrastructure from millions of hardware devices. This explosion of information, scientific techniques, computational models, and technological advances leads to enormous challenges in data analysis, evidence-based biomedical inference and reproducibility of findings. The Pipeline workflow environment provides a crowd-based distributed solution for consistent management of these heterogeneous resources. The Pipeline allows multiple (local) clients and (remote) servers to connect, exchange protocols, control the execution, monitor the states of different tools or hardware, and share complete protocols as portable XML workflows. In this paper, we demonstrate several advanced computational neuroimaging and genetics case-studies, and end-to-end pipeline solutions. These are implemented as graphical workflow protocols in the context of analyzing imaging (sMRI, fMRI, DTI), phenotypic (demographic, clinical), and genetic (SNP) data.

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