【正文】 是整個軟件業(yè)務(wù)增長速度的2倍。在19902005年間，全球有10億人進(jìn)入中產(chǎn)階級，這意味著越來越多的掙到錢的人將變得更加有文化，這又反過來刺激信息的增長。額外的適用于大數(shù)據(jù)的技術(shù)包括海量并行處理（MPP）數(shù)據(jù)庫、基于搜索的應(yīng)用、數(shù)據(jù)挖掘網(wǎng)格（計算）、分布式文件系統(tǒng)、分布式數(shù)據(jù)庫、基于云的基礎(chǔ)設(shè)施（應(yīng)用、存儲、和計算資源）和互聯(lián)網(wǎng)。對共享存儲架構(gòu)——SAN和NAS——的看法是它們相對低速、復(fù)雜和昂貴。內(nèi)存數(shù)據(jù)就非常好——數(shù)據(jù)在另一端的旋轉(zhuǎn)磁盤上而不是連接在FC SAN上。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]該計劃包括國家科學(xué)基金會“計算探險”向加州大學(xué)伯克利分校AMPLab實驗室的5年1000萬美元的捐款，該實驗室（AMPLab）還受到了來自DAPARA的資金以及十?dāng)?shù)個行業(yè)贊助，目的是使用大數(shù)據(jù)來解決廣泛的問題，范圍從預(yù)測交通阻塞到與癌癥抗?fàn)帯ｑR薩諸塞技術(shù)研究所主辦的麻省理工學(xué)院（MIT）計算機(jī)科學(xué)和人工智能實驗室的大數(shù)據(jù)Intel科學(xué)與技術(shù)中心聯(lián)合了政府、公司和機(jī)構(gòu)資金進(jìn)行研究工作。對異構(gòu)數(shù)據(jù)源的集成（綜合）——有些人認(rèn)為是大數(shù)據(jù)而另外一些則不同意——提出了巨大的物流和分析的挑戰(zhàn)，但是許多研究人員認(rèn)為這種集成很可能代表最有前途的科學(xué)前沿。為了克服這種洞察的不足，“大數(shù)據(jù)”，無論怎樣全面和很好的分析，都需要由“大判斷”來補(bǔ)充。 expert panels have released various policy remendations to conform practice to expectations of privacy.[47]消費(fèi)者私密性倡導(dǎo)者們對由越來越多的個人身份信息的存儲和集成為代表的隱私（暴露）的擔(dān)憂。s assertion that big data will spell the end of theory:更廣泛的爭論在Chris Anderson的“大數(shù)據(jù)將終結(jié)的理論”的斷言所平息：關(guān)注在特定的概念上，就是大數(shù)據(jù)將總是需要存在在社會、經(jīng)濟(jì)和政治背景之中。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 panies invest eight and ninefigure 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 overe this insight deficit, big data, no matter how prehensive or well analyzed, needs to be plemented by big judgment, according to an article in the Harvard Business Review.[46]Danah Boyd提出了對忽視科學(xué)準(zhǔn)則使用大數(shù)據(jù)的擔(dān)憂，包括選擇過于專注對大量數(shù)據(jù)實際處理的代表性樣本。SDAV實驗室旨在將六個國家實驗室和7所大學(xué)的專業(yè)人士聚合在一起開發(fā)新的工具來幫助科學(xué)家們對能源部的超級計算機(jī)上的數(shù)據(jù)進(jìn)行管理和可視化。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]在大數(shù)據(jù)分析中，共享存儲有其自身的優(yōu)缺點(diǎn)，但是在2011年的大數(shù)據(jù)實踐者們并不喜歡它。Real or nearreal 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.實時和準(zhǔn)實時信息傳送是定義大數(shù)據(jù)的特征之一。即在RDMS中裝載、監(jiān)視和優(yōu)化使用大型數(shù)據(jù)表的能力。TechnologiesBig 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 parallelprocessing (MPP) databases, searchbased applications, datamining grids, distributed file systems, distributed databases, cloud based infrastructure (applications, storage and puting resources) and the I