Big Data platforms for science automation

我的研究项目的目标是实现大数据分析从数据处理到知识发布的自动化。在接下来的几年里，我将针对这一巨大挑战，集中精力实现以下三个目标。(1)大数据平台之间的互操作性：目前科学领域使用的大数据平台处于孤岛状态，这阻碍了科学的开放，通过限制竞争降低了平台的整体质量，并对特定的软件项目产生了技术依赖。我将设计一个连接平台的去中心化网络的构建块，以便数据、处理管道和分析可以在各种平台上统一发现、访问和重用。(2)大数据分析随时间和空间变化的可重复性：科学正在经历一场严重的可重复性危机，如果不能妥善解决，将阻止各种学科利用所获得的巨大数据财富：大数据必须转化为可信的知识。我将开发方法来识别、量化和纠正再现性问题，重点关注源自计算基础设施的挑战。(3)大数据计算的性能优化：虽然大数据技术在工业上具有变革性，但由于缺乏适当的基准和性能优化研究，大数据技术在科学上的应用仍然不足。我将为大型科学数据集设计和创建一个优化的，易于使用的大数据处理环境。这三个目标与大数据中臭名昭著的“V”相呼应：互操作性解决了数据的多样性，再现性目标是准确性，而性能提供了速度和管理数据量。******总体而言，该计划将加快并提高知识生产的质量。它将促进网络基础设施自动化，允许科学对象跨平台无缝移动，确保不同平台计算结果的一致性，并加速大数据分析。虽然这项研究的潜在应用跨越了数据科学的所有科学学科，但我将专注于神经信息学，利用我在该领域长期的顶级合作，并利用VIP和CBRAIN平台开发和运营所获得的丰富经验。在IT（大数据和云计算）和制药领域，技术转移也有望向加拿大工业转移。

The objective of my research program is to automate Big Data analyses from data processing to knowledge publication. In the next years, I will focus on the following three objectives of this immense challenge. (1) Interoperability among Big Data platforms: Big Data platforms used in science currently operate in silos, which hinders open science, reduces the overall quality of platforms by limiting competition, and creates technological dependence on particular software projects. I will design the building blocks of a decentralized network connecting platforms so that data, processing pipelines and analyses can be uniformly found, accessed and reused in various platforms. (2) Reproducibility of Big Data analyses over time and space: science is going through a severe reproducibility crisis, which, if not properly addressed, will prevent various disciplines from leveraging the tremendous wealth of data that is acquired: Big Data has to be converted into trusted knowledge. I will develop methods to identify, quantify and correct reproducibility issues, focusing on the challenges that originate in the computing infrastructure. (3) Performance optimization of Big Data computations: while they have been transformational for industry, Big Data technologies remain underused in science, due to the lack of appropriate benchmarks and performance optimization studies. I will design and create an optimized, easy-to-use Big Data processing environment for large scientific datasets. These three objectives echo the notorious V's of Big Data: interoperability addresses data Variety, reproducibility targets Veracity, and performance provides Velocity and manages data Volume.******Overall, this program will speed up and improve the quality of knowledge production. It will foster cyberinfrastructure automation, by allowing scientific objects to seamlessly move across platforms, by ensuring the consistency of results computed in different platforms and by accelerating Big Data analyses. While potential applications of this research span the whole spectrum of scientific disciplines engaged in data science, I will focus on neuroinformatics, exploiting my long-standing top-level collaborations in this field and leveraging the tremendous experience acquired with the development and operation of the VIP and CBRAIN platforms. Technology transfer to the Canadian industry is also expected, in the IT (Big Data and cloud) and pharmaceutical sectors.