从宇宙到连接组：数据密集型科学的发展。

From cosmos to connectomes: the evolution of data-intensive science.

机构信息

Institute for Data-Intensive Science and Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA.

Institute for Data-Intensive Science and Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA.

出版信息

Neuron. 2014 Sep 17;83(6):1249-52. doi: 10.1016/j.neuron.2014.08.045.

DOI:10.1016/j.neuron.2014.08.045

PMID:25233306

Abstract

The analysis of data requires computation: originally by hand and more recently by computers. Different models of computing are designed and optimized for different kinds of data. In data-intensive science, the scale and complexity of data exceeds the comfort zone of local data stores on scientific workstations. Thus, cloud computing emerges as the preeminent model, utilizing data centers and high-performance clusters, enabling remote users to access and query subsets of the data efficiently. We examine how data-intensive computational systems originally built for cosmology, the Sloan Digital Sky Survey (SDSS), are now being used in connectomics, at the Open Connectome Project. We list lessons learned and outline the top challenges we expect to face. Success in computational connectomics would drastically reduce the time between idea and discovery, as SDSS did in cosmology.

摘要

数据分析需要计算

最初是手工计算，最近则是通过计算机进行计算。不同的计算模型是为不同类型的数据设计和优化的。在数据密集型科学中，数据的规模和复杂性超出了科学工作站上本地数据存储的舒适区。因此，云计算作为一种主要的模型出现了，利用数据中心和高性能集群，使远程用户能够高效地访问和查询数据的子集。我们研究了最初为宇宙学构建的数据密集型计算系统——斯隆数字巡天（SDSS），现在如何在开放连接组项目（Open Connectome Project）中用于连接组学。我们列出了所学到的经验教训，并概述了我们预计将面临的主要挑战。如果在计算连接组学方面取得成功，将像 SDSS 在宇宙学中所做的那样，大大缩短从想法到发现的时间。