微软引领的第四范式：数据密集型科学发现

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"微软第四范式：数据密集型科学发现" "第四范式"是科学界对科学研究方法的一种新描述，尤其在信息技术（IT）领域中，它标志着科学研究方式的重大转变。这个概念由微软提出，并在《第四范式：数据密集型科学发现》一书中进行了深入探讨。这本书汇集了多位作家的观点，旨在阐述在大数据时代，如何利用海量数据进行科学研究并推动创新。传统的科学研究通常遵循三个范式：实验科学、理论科学和计算科学。第一范式强调观察和实验；第二范式注重理论构建和预测；第三范式则引入了计算机模拟和计算能力，使科学家能够处理复杂的模型和数据。然而，随着互联网、物联网（IoT）和云计算等技术的发展，数据的生成速度和规模呈指数级增长，科学界进入了第四范式。第四范式的核心是数据密集型科学，它强调数据的收集、存储、分析和解释，以及通过高级分析工具和机器学习算法来发现模式和洞察。在这个范式下，科学家不再依赖于先验理论来指导研究，而是让数据本身引导发现。大数据分析和人工智能（AI）技术在第四范式中起着关键作用，它们能够揭示传统方法无法发现的复杂关系和趋势。微软在这一领域的工作，如Amalga、Bing、HealthVault、Microsoft Surface、SQL Server、Virtual Earth 和 Windows，都是第四范式实践的例子。这些产品和服务不仅处理大量数据，而且通过创新的方式将数据转化为有价值的洞见。例如，Bing搜索引擎利用大数据改进搜索结果，HealthVault则通过整合个人健康数据来改善医疗服务。此外，书中可能还讨论了数据共享和开放科学的重要性，因为第四范式强调合作和跨学科的研究。Creative Commons Attribution-ShareAlike 3.0 United States 许可证的使用，表明微软鼓励知识的传播和创新，这与第四范式的精神相吻合。《第四范式：数据密集型科学发现》反映了科学界对大数据利用的最新认识，展示了如何通过数据驱动的方法来推动科技进步，同时也揭示了微软在推动这一变革中的角色。这本书对于理解科学的未来方向以及如何在数据洪流中寻找价值具有重要参考价值。

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must think carefully about which data should be able to live forever and what ad-

ditional metadata should be captured to make this feasible.

Data analysis covers a whole range of activities throughout the workﬂow pipe-

line, including the use of databases (versus a collection of ﬂat ﬁles that a database

can access), analysis and modeling, and then data visualization. Jim Gray’s recipe

for designing a database for a given discipline is that it must be able to answer the

key 20 questions that the scientist wants to ask of it. Much of science now uses data-

bases only to hold various aspects of the data rather than as the location of the data

itself. This is because the time needed to scan all the data makes analysis infeasible.

A decade ago, rereading the data was just barely feasible. In 2010, disks are 1,000

times larger, yet disc record access time has improved by only a factor of two.

DIGITAL LIBRARIES FOR DATA AND DOCUMENTS: JUST LIKE MODERN DOCUMENT LIBRARIES

Scientiﬁc communication, including peer review, is also undergoing fundamental

changes. Public digital libraries are taking over the role of holding publications

from conventional libraries—because of the expense, the need for timeliness, and

the need to keep experimental data and documents about the data together.

At the time of writing, digital data libraries are still in a formative stage, with

various sizes, shapes, and charters. Of course, NCAR is one of the oldest sites for

the modeling, collection, and curation of Earth science data. The San Diego Su-

percomputer Center (SDSC) at the University of California, San Diego, which is

normally associated with supplying computational power to the scientiﬁc commu-

nity, was one of the earliest organizations to recognize the need to add data to

its mission. SDSC established its Data Central site,

which holds 27 PB of data in

more than 100 speciﬁc databases (e.g., for bioinformatics and water resources). In

2009, it set aside 400 terabytes (TB) of disk space for both public and private data-

bases and data collections that serve a wide range of scientiﬁc institutions, includ-

ing laboratories, libraries, and museums.

The Australian National Data Service

(ANDS) has begun oering services

starting with the Register My Data service, a “card catalog” that registers the

identity, structure, name, and location (IP address) of all the various databases,

including those coming from individuals. The mere act of registering goes a long

way toward organizing long-term storage. The purpose of ANDS is to inﬂuence

national policy on data management and to inform best practices for the curation

http://datacentral.sdsc.edu/index.html

www.ands.org.au

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