来自维基百科对大大数据地定义

Big data-From Wikipedia

In information technology, big data[1][2] is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage,[3] search, sharing, analysis,[4] and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to \"spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions.\"[5][6][7]

在信息技术中，“大数据”是指一些使用目前现有数据库管理工具或传统数据处理应用很难处理的大型而复杂的数据集。其挑战包括采集、管理、存储、搜索、共享、分析和可视化。更大的数据集的趋势是由于从相关数据的单一大数据集推导而来的额外信息，与分离的较小的具有相同数据总量的数据集相比，能够发现相关性来“识别商业趋势（spot business trends）、确定研究的质量、预防疾病、法律引用链接、打击犯罪以及实时确定道路交通状态”。

As of 2012, limits on the size of data sets that are feasible to process in a reasonable amount of time were on the order of exabytes of data.[8][9] Scientists regularly encounter limitations due to large data sets in many areas, including meteorology, genomics,[10] connectomics, complex physics simulations,[11] and biological and environmental research.[12] The limitations also affect Internet search, finance and business informatics. Data sets grow in size in part because

they are increasingly being gathered by ubiquitous information-sensing mobile devices, aerial sensory technologies (remote sensing), software logs, cameras, microphones, radio-frequency identification readers, and wireless sensor networks.[13][14] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[15] as of 2012, every day 2.5 quintillion (2.5×1018) bytes of data were created.[16]

截至2012年，数据集大小尺寸的是exabyte数量级的数据，这种规模是指以可行的处理方式在合理的时间内进行数据处理。在许多领域科学家们经常遇到大数据集的，这些领域包括气象学、基因学、connectomics、复杂的物理仿真、以及生物和环境研究。这些也影响到了互联网、金融和商业情报信息的研究。数据集大小的增长是由于这些数据集不断地通过无处不在的信息感应移动设备、航空传感技术（遥感）、软件日志、摄像头、麦克风、无线频率识别阅读器（radio-frequency identification readers）-RFID和无线传感网络来收集和聚集。从80年代起，全球存储信息人均信息存储能力在技术上大致每40个月就翻一番；as of 2012, every day 2.5 quintillion (2.5×1018) bytes of data were created.[16]截至到2012年，每天产生的数据为2.5 quintillion（2.5*10^18）字节。

Big data is difficult to work with using relational databases and desktop statistics and visualization packages, requiring instead \"massively parallel software running on tens, hundreds, or even thousands of servers\".[17] What is considered \"big data\" varies depending on the capabilities of the organization managing the set, and on the capabilities of the applications that are traditionally used to process and analyze the data set in its domain. \"For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider

data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration.\"[18]

使用关系型数据库和桌面统计和可视化软件包对大数据进行处理是困难的，它需要“将大规模并行软件运行在数十台、数百台或甚至数千台服务器（来处理）”。什么是“大数据”取决于企业管理数据集的能力、以及在其领域内使用传统方式对数据集的处理和分析能力。“对某些企业来说，在第一次面对处理上百G字节的数据时就要重新考虑数据管理的选择，而对其他的企业来说，处理数百TB字节的数据量不成问题。

Definition

Big data usually includes data sets with sizes beyond the ability of commonly-used software tools to capture, curate, manage, and process the data within a tolerable elapsed time. Big data sizes are a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data in a single data set. With this difficulty, a new platform of \"big data\" tools has arisen to handle sensemaking over large quantities of data, as in the Apache Hadoop Big Data Platform.

大数据通常包括在尺寸上超出常用软件工具对数据在一定的可容忍时间间隔内进行采集、管理和处理的能力的数据集。大数据的尺寸是一个不断变化的目标，截至到2012年在一个单一数据集中的数据范围从十数TB到数个PB。由于这种困难性，出现了新的“大数据“平台工具来在大量的数据中处理合理的数据，例如Apache Hadoop大数据平台。

MIKE2.0, an open approach to Information Management, defines big data in

terms of useful permutations, complexity, and difficulty to delete individual records.

MIKE2.0，一个开放的信息管理方式，从有用的排列、复杂性和难以删除单一记录几个方面定义了大数据。

In a 2001 research report[19] and related lectures, META Group (now Gartner) analyst Doug Laney defined data growth challenges and opportunities as being three-dimensional, i.e. increasing volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources). Gartner, and now much of the industry, continue to use this \"3Vs\" model for describing big data.[20] In 2012, Gartner updated its definition as follows: \"Big Data are high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.\"[21]

在2001年的研究报告和相关文献中，META Group（现在的Gartner）的分析师Doug Laney将数据增长的挑战和机遇定义成三维方式，即总量（数据量）、速度（数据进出（变化）的速度）和多样性（数据类型和数据源的范围）。Gartner和目前业界大多数（人）延续使用这种“3V“模型来描述大数据。在2012年，Gartner更新了其对大数据的定义：”大数据是具备大数据量、高变化速度和/或高度多样新的信息资产，这些信息资产需要新型的处理方式来强化决策制定、洞察发现和处理优化。

Examples

Examples include web logs, RFID, sensor networks, social networks, social data (due to the social data revolution), Internet text and documents, Internet search indexing, call detail records, astronomy, atmospheric science, genomics, biogeochemical, biological, and other complex and often interdisciplinary scientific research, military surveillance, medical records, photography archives, video archives, and large-scale e-commerce.

例子包括网络日志、RFID、传感器网络、社交网络、社交数据（由于社交数据）、互联网文本和文档、互联网搜索索引、呼叫详细记录（话单-CDR）、天文学、大气科学、基因学、生物化学、生物科学以及其他复杂和常常跨学科的科学研究、军事侦查、医疗记录、图片档案、视频档案、和大规模电子商务。

Science and research

➢ When the Sloan Digital Sky Survey (SDSS) began collecting astronomical data in 2000, it amassed more in its first few weeks than all data collected in the history of astronomy. Continuing at a rate of about 200 GB per night, SDSS has amassed more than 140 terabytes of information. When the Large Synoptic Survey Telescope, successor to SDSS, comes online in 2016 it is anticipated to acquire that amount of data every five days.[5]

在Sloan Digital Sky Survey (SDSS)于2000年开始采集天文数据时，在最初的几周内它积累了比天文史上收集的所有数据还要多的数据。现在他还以每夜大约200GB数据量的速率增加。SDSS已经累积了超过140TB的信息。一旦大型的天文望远镜，SDSS的继任者，在2016年上线，预计它将每5天采集的数据量。

➢ In total, the four main detectors at the Large Hadron Collider (LHC) produced 13 petabytes of data in 2010 (13,000 terabytes).[22]

总的说来，四个主要的大型强子碰撞机在2010年所产生的是数据达到13PB（13000TB）。

Decoding the human genome originally took 10 years to process; now it can be achieved in one week.[5]

解码人体基因原来需要10年的时间，现在它能在1周之内完成。

➢ Computational social science — Tobias Preis et al. used Google Trends data to demonstrate that Internet users from countries with a higher per capita gross domestic product (GDP) are more likely to search for information about the future than information about the past. The findings suggest there may be a link between online behaviour and real-world economic indicators.[23][24][25] The authors of the study examined Google queries logs made by Internet users in 45 different countries in 2010 and calculated the ratio of the volume of searches for the coming year (‘2011’) to the volume of searches for the previous year (‘2009’), which they call the ‘future orientation index’.[26] They compared the future orientation index to the per capita GDP of each country and found a strong tendency for countries in which Google users enquire more about the future to exhibit a higher GDP. The results hint that there may potentially be a relationship between the economic success of a country and the information-seeking behavior of its citizens captured in big data.

计算社会科学——Tobias Preis等，使用Google趋势搜索来证明来自较高人均GDP国家的互联网用户访问未来的信息比访问过去的信息要多。这个发现暗示有可能在连线行为与真实世界中的经济指标有某种联系。该研究的作者考差了在2010年终45个国家的互联网用户的Google查询日志并计算了来年（2011年）搜索数量与去年（2009年）搜索数量的比率，这个比率被他们称之为“未来取向指数”。他们比较了每个国家GDP的未来去向指数，发现具有较高的GDP国家的用户使用Google搜索的关于未来的信息的强烈倾向。这个结果暗示在经济成功的国家，其国民在大数据中寻找信息的行为有可能的关联。

Government

➢ In 2012, the Obama administration announced the Big Data Research and Development Initiative, which explored how big data could be used to address important problems facing the government.[27] The initiative was composed of 84 different big data programs spread across six departments.[28]

在2012年，奥巴马宣布了大数据研究和开发计划，探索如何使用大数据来解决面临的问题。该计划由跨越6个部门的84个大数据程序项目所组成。

➢ The United States Federal Government owns six of the ten most powerful supercomputers in the world.[29]

美国联邦拥有世界上10台超级计算机中的6台。

Private sector

➢ Walmart handles more than 1 million customer transactions every hour, which is imported into databases estimated to contain more than 2.5 petabytes of data – the equivalent of 167 times the information contained in all the books in the US Library of Congress.[5]

Walmart每小时处理100万个顾客事务，这些事务被输入进数据库，该数据库含有超过2.5PB的数据——是美国国会图书馆所有图书信息量的167倍

➢ handles 40 billion photos from its user base.

处理来自用户群的400亿张照片

➢ FICO Falcon Credit Card Fraud Detection System protects 2.1 billion active accounts world-wide.[30]

FICO Falcon信用卡欺诈检测系统保护全球21亿激活用户。

➢ The volume of business data worldwide, across all companies, doubles every 1.2 years, according to estimates.[31]

据估计，全球所有公司的商业数据量每1.2年要翻番。

Market

\"Big data\" has increased the demand of information management specialists in that Software AG, Oracle Corporation, IBM, Microsoft, SAP, and HP have spent

more than $15 billion on software firms only specializing in data management and analytics. This industry on its own is worth more than $100 billion and growing at almost 10 percent a year, about twice as fast as the software business as a whole.[5]

“大数据”增加了对信息管理专业人士的需求，因为Software AG、 Oracle、 IBM、 Microsoft、SAP、和 HP已经花费了超过150亿没劲收购软件公司，只为了数据管理和分析的专业人士。这个行业自身的价值超过1000亿美元并以每年10%的速度增长，是整个软件业务增长速度的2倍。

Developed economies make increasing use of data-intensive technologies. There are 4.6 billion mobile-phone subscriptions worldwide and there are between 1 billion and 2 billion people accessing the internet.[5] Between 1990 and 2005, more than 1 billion people worldwide entered the middle class which means more and more people who gain money will become more literate which in turn leads to information growth. The world's effective capacity to exchange information through telecommunication networks was 281 petabytes in 1986, 471 petabytes in 1993, 2.2 exabytes in 2000, 65 exabytes in 2007[15] and it is predicted that the amount of traffic flowing over the internet will reach 667 exabytes annually by 2013.[5]

发达的经济越来越多地使用数据密集型技术。全球有46亿移动手机签约和10-20亿人们接入互联网。在1990-2005年间，全球有10亿人进入中产阶级，这意味着越来越多的挣到钱的人将变得更加有文化，这又反过来刺激信息的增长。全球通过电信网络交换的有效信息在1986年是281PB，在1993年是471PB，在2000年是65 EB，预计到2013

年互联网的流量将超过每年667EB。

Technologies

Big data requires exceptional technologies to efficiently process large quantities of data within tolerable elapsed times. A 2011 McKinsey report[32] suggests suitable technologies include A/B testing, association rule learning, classification, cluster analysis, crowdsourcing, data fusion and integration, ensemble learning, genetic algorithms, machine learning, natural language processing, neural networks, pattern recognition, anomaly detection, predictive modelling, regression, sentiment analysis, signal processing, supervised and unsupervised learning, simulation, time series analysis and visualisation. Additional technologies being applied to big data include massively parallel-processing (MPP) databases, search-based applications, data-mining grids, distributed file systems, distributed databases, cloud based infrastructure (applications, storage and computing resources) and the Internet.

大数据要求特殊的技术在可容忍的时间内来处理大量的数据。McKinsey（麦卡锡）2011年的报告指出适合大数据的技术包括A/B测试、关联规则学习、分类、集群分析、大众外包、数据融合和集成、遗传算法、机器学习、自然语言处理、神经网络、模式识别、异常检测、预测建模、回归分析、情感分析、信号处理、监督和无监督学习、仿真、时序分析和可视化。额外的适用于大数据的技术包括海量并行处理（MPP）数据库、基于搜索的应用、数据挖掘网格（计算）、分布式文件系统、分布式数据库、基于云的基础设施（应用、存储、和计算资源）和互联网。

Some but not all MPP relational databases have the ability to store and manage petabytes of data. Implicit is the ability to load, monitor, back up, and optimize the use of the large data tables in the RDBMS.[33]

一些但并不是全部MPP关系型数据库具备存储和管理petabytes级数据的能力。即在RDMS中装载、监视和优化使用大型数据表的能力。

The practitioners of big data analytics processes are generally hostile to slower shared storage[citation needed], preferring direct-attached storage (DAS) in its various forms from solid state disk (SSD) to high capacity SATA disk buried inside parallel processing nodes. The perception of shared storage architectures—SAN and NAS—is that they are relatively slow, complex, and expensive. These qualities are not consistent with big data analytics systems that thrive on system performance, commodity infrastructure, and low cost.

大数据的实践者们一般都讨厌较慢的共享存储，偏爱各种形式的直接附着的存储（DAS），从固态硬盘（SSD）到内置在高性能并行处理节点内的高容量SATA磁盘。对共享存储架构——SAN和NAS——的看法是它们相对低速、复杂和昂贵。这些特质不符合大数据分析系统的要求，即要满足促进性能提高、商品化基础设施和低成本的要求。

Real or near-real time information delivery is one of the defining characteristics of big data analytics. Latency is therefore avoided whenever and wherever possible. Data in memory is good—data on spinning disk at the other end of a FC SAN connection is not. The cost of a SAN at the scale needed for analytics applications is very much higher than other storage techniques.

实时和准实时信息传送是定义大数据的特征之一。因此只要可能就应当尽量避免时延。内存数据就非常好——数据在另一端的旋转磁盘上而不是连接在FC SAN上。用于分析应用规模的SAN的成本远高于其他形式的存储技术。

There are advantages as well as disadvantages to shared storage in big data analytics, but big data analytics practitioners as of 2011 did not favour it.[34]

在大数据分析中，共享存储有其自身的优缺点，但是在2011年的大数据实践者们并不喜欢它。

Research activities

In March 2012, The White House announced a national \"Big Data Initiative\" that consisted of six Federal departments and agencies committing more than $200 million to Big Data research projects.[35]

2012年3月，白宫宣布了国家“大数据计划”，包括6个联邦部门和机构投资超过2亿美元实施大数据项目。

The initiative included a National Science Foundation \"Expeditions in Computing\" grant of $10 million over 5 years to the AMPLab[36] at the University of California, Berkeley.[37] The AMPLab also received funds from DARPA, and over a dozen industrial sponsors and uses Big Data to attack a wide range of problems from predicting traffic congestion[38] to fighting cancer.[39]

该计划包括国家科学基金会“计算探险”向加州大学伯克利分校AMPLab实验室的5年1000万美元的捐款，该实验室（AMPLab）还受到了来自DAPARA的资金以及十数个行业赞助，目的是使用大数据来解决广泛的问题，范围从预测交通阻塞到与癌症抗争。

The White House Big Data Initiative also included a commitment by the Department of Energy to provide $25 million in funding over 5 years to establish the Scalable Data Management, Analysis and Visualization (SDAV) Institute,[40] led by the Energy Department’s Lawrence Berkeley National Laboratory. The SDAV Institute aims to bring together the expertise of six national laboratories and seven universities to develop new tools to help scientists manage and visualize data on the Department’s supercomputers.

白宫的大数据计划还包括能源部承诺的提供2500万美元的资金在5年内建立可扩展的数据管理、分析和可视化（SDAV）研究所，由能源部的劳伦斯-伯克利国家实验室主导。SDAV实验室旨在将六个国家实验室和7所大学的专业人士聚合在一起开发新的工具来帮助科学家们对能源部的超级计算机上的数据进行管理和可视化。

The U.S. state of Massachusetts announced the Massachusetts Big Data Initiative in May 2012, which provides funding from the state government and private companies to a variety of research institutions.[41] The Massachusetts Institute of Technology hosts the Intel Science and Technology Center for Big Data in the MIT Computer Science and Artificial Intelligence Laboratory, combining government, corporate, and institutional funding and research efforts.[42]

美国马萨诸塞州在2012年5月宣布了马萨诸塞州大数据计划（Massachusetts

Big Data Initiative），它从州和私人公司提供资金给各种研究所。马萨诸塞技术研究所主办的麻省理工学院（MIT）计算机科学和人工智能实验室的大数据Intel科学与技术中心联合了、公司和机构资金进行研究工作。

Critique

danah Boyd has raised concerns about the use of big data in science neglecting principles such as choosing a representative sample by being too concerned about actually handling the huge amounts of data.[43] This approach may lead to results biased in one way or another. Integration across heterogeneous data resources — some that might be considered \"big data\" and others not — presents formidable logistical as well as analytical challenges, but many researchers argue that such integrations are likely to represent the most promising new frontiers in science.[44] Broader critiques have also been levelled at Chris Anderson's assertion that big data will spell the end of theory: focusing in particular on the notion that big data will always need to be contextualized in their social, economic and political contexts.[45] Even as companies invest eight- and nine-figure sums to derive insight from information streaming in from suppliers and customers, less than 40% of employees have sufficiently mature processes and skills to do so. To overcome this insight deficit, \"big data\no matter how comprehensive or well analyzed, needs to be complemented by \"big judgment\according to an article in the Harvard Business Review.[46]

Danah Boyd提出了对忽视科学准则使用大数据的担忧，包括选择过于专注对大量数据实际处理的代表性样本。这种方式可能导致结果有某种偏见。对异构数据源的集成（综

合）——有些人认为是大数据而另外一些则不同意——提出了巨大的物流和分析的挑战，但是许多研究人员认为这种集成很可能代表最有前途的科学前沿。Broader critiques have also been levelled at Chris Anderson's assertion that big data will spell the end of theory:更广泛的争论在Chris Anderson的“大数据将终结的理论”的断言所平息：关注在特定的概念上，就是大数据将总是需要存在在社会、经济和政治背景之中。根据哈佛商业观察的一篇文章，当公司向从来自供应商和客户的信息流推导洞察（这种解决方案）投资8-9位数时，低于40%的员工具备足够的处理技能。为了克服这种洞察的不足，“大数据”，无论怎样全面和很好的分析，都需要由“大判断”来补充。

Consumer privacy advocates are concerned about the threat to privacy represented by increasing storage and integration of personally identifiable information; expert panels have released various policy recommendations to conform practice to expectations of privacy.[47]

消费者私密性倡导者们对由越来越多的个人身份信息的存储和集成为代表的隐私（暴露）的担忧。专家们已经发布了各种策略建议来遵守预期的私密性。