Ⅰ 大數據時代，對於我們普通人來說，有什麼好處
就業機會變多，眼界可以更開闊。大數據時代，需要的人才越來越多，對年輕人而言就業變得容易了。大數據時代也可以讓人的眼界更開闊，畢竟網路上什麼都查得到。
Ⅱ 對數字商業的看法120字英語作文
數字商務（Digital Commerce）是指利用互聯網、物聯網、無線通信等通信技術和數據分析手段將商務的流程、渠道、營銷、運營等流程數字化、互聯網化、智能化。
同電子商務不同的是，數字商務不僅僅是將現代信息技術和商務的結合，而是集中於將數據的價值應用到商業中去，將商業的流程和業務智能化，有機化。
各大城市也在推行數字商務的發展，建立各種機制和提供優惠政策來促進數字商務的發展。比如說上海嘉定新城，依託江浙滬一帶的商務優勢和信息化基礎，在產業升級的過程中將促進數字商務的發展作為重要發展戰略。
數字化轉型對中國企業而言，不僅是一道戰略選擇題，更是一道生存題。
有這樣一種說法:農業文明3000年，中國領先了2000多年的歷史；工業文明300年，中國落後了150年甚至200年的歷史；智能文明60年，中國與世界基本同步。也就是說，在工業文明時期，中國企業輸在了起跑線上。但是在數字化、智能文明時代，中國企業如果能夠抓住數字化智能文明時代的歷史性發展機遇，中國企業不僅能夠跟歐美企業同步，甚至可以變道超車。
在過去10年裡，美國幾乎所有的行業都數字化了。從會計到倉儲，從人事到日程安排，數字技術無處不在，通過這種數字化進程，美國的企業也嘗到了甜頭。據IDG（美國國際數據集團）研究報告：全球1000大企業中67％已將數字化轉型變成企業級戰略。企業數字化轉型也正成為許多中國企業的核心戰略。
中國已將大數據上升為國家戰略，但研究顯示：過去，中國的互聯網發展以消費者為主帶動、而不是企業為導向。中國消費者網路零售消費佔比為7%-8%，已超過美國的6%；而在企業雲服務滲透率方面中國比率為21%，遠低於美國的55%-63%；中小企業運營中互聯網使用率為20%-25%，而美國同一數據則是72%-85%。
Ⅲ 跪求寫一篇數據科學與大數據技術對社會的 貢獻的英語作文，不少於200字。急用急用。
這個從社會的發展角度分析數據科學與大數據對人民生活的影響，以及重要性，說明人們生活越來越離不開數據，應該積極面對社會的發展和進步。
Ⅳ 大數據的優勢在哪裡
現在的社會是一個高速發展的社會，科技發達，信息流通，人們之間的交流越來越密切，生活也越來越方便，大數據就是這個高科技時代的產物。大數據」是指以多元形式，自許多來源搜集而來的龐大數據組，往往具有實時性。在企業對企業銷售的情況下，這些數據可能得自社交網路、電子商務網站、顧客來訪紀錄，還有許多其他來源。
目前大數據幾乎每個企業都在使用，大數據分析提供了一個真正具有潛在利益的礦藏，但它也帶來了可能抵消潛在收益的重大挑戰。
• 更精準的決策 ：在NewVantage Partners公司調查中，36.2%的受訪者表示更好的決策是他們大數據分析工作的首要目標。此外，84.1%的受訪者表示已開始朝著這一目標努力，59.0%的受訪者表示取得了一些可衡量的成功，其總體成功率為69.0%。大數據分析可以為業務決策者提供他們所需的數據驅動的洞察力，以幫助企業開展競爭和業務發展。
• 提高生產力 ：來自供應商Syncsort公司的另一項調查發現，59.9%的受訪者使用Hadoop和Spark等大數據工具來提高業務的工作效率。現代大數據工具使分析師能夠更快地分析更多數據，從而提高個人生產力。此外，從這些分析中獲得的見解通常使組織能夠在整個公司內更廣泛地提高生產力。
• 降低成本 ：Syncsort公司和NewVantage公司的調查均發現大數據分析正在幫助企業降低成本。近五分之三(59.4%)的受訪者表示Syncsort公司的大數據工具幫助他們提高了運營效率，並降低了成本，NewVantage公司的調查中，約三分之二(66.7%)的受訪者表示他們已開始使用大數據來降低成本。然而有趣的是，只有13.0%的受訪者選擇降低成本作為大數據分析的主要目標，這表明對於許多人而言，這只是一個非常受歡迎的附帶好處。
• 改善客戶服務 ：在NewVantage公司調查的受訪者中，改善客戶服務是大數據分析項目的第二個最常見的主要目標，53.4%的受訪者表示在這方面取得了一些成功。社交媒體、客戶關系管理(CRM)系統、其他客戶為當今的企業提供了大量有關其客戶的信息，他們很自然地會使用這些數據來更好地為這些客戶提供服務。
• 欺詐檢測 ：大數據分析的另一個常見用途用於欺詐檢測，特別是在金融服務行業。依賴於機器學習的大數據分析系統的一大優勢是它們在檢測模式和異常方面非常出色。這些能力可以讓銀行和信用卡公司能夠發現被盜信用卡或欺詐性購買，並且通常是在持卡人知道出現問題之前發現問題。
• 增加收入 ：當組織使用大數據來改善決策並改善客戶服務時，增加收入通常是一個自然的結果。在Syncsort公司的調查中，超過一半的受訪者(54.7%)表示他們正在使用大數據工具來增加收入，並根據更好的洞察力加速增長。
• 提高靈活性 ：同樣，從Syncsort公司的調查報告中，41.7%的受訪者表示大數據的好處之一是能夠提高業務/IT敏捷性。許多組織正在使用其大數據來更好地調整其IT和業務工作，並且他們正在使用他們的分析來支持更快、更頻繁地更改其業務戰略和策略。
• 更好的創新 ：創新是大數據的另一個共同利益，NewVantage公司的調查發現，11.6%的高管正在投資分析，主要是作為創新和顛覆市場的手段。他們認為，如果他們能夠收集競爭對手所沒有的見解，他們就可以通過新產品和服務領先於其他企業。
• 上市速度 ：在這些方面，很多企業表示將使用大數據來加快產品上市速度。只有8.8%的受訪者表示這是大數據的首要目標，但53.6%受訪者已經開始朝著這個目標努力，其中54.1%的受訪者表示取得了一些成功。大數據的這種優勢也可能帶來額外的好處，例如更快的增長和更高的收入。
更多資訊請關註： 辰宇智搜
Ⅳ 關於貴陽大數據的高中英語作文
相信自己
之所以成功來之不易，是因為用汗水堆積成的堡壘；之所以未來明燈常亮，是因為用堅持疊成的燈塔；之所以人生絢爛多姿，是因為用努力築成的豐碑。因為相信自己，所以堅持不懈；因為相信自己，所以勇往直前。揚起自信的風帆，成功僅一步之遙。
曾經遇事膽怯的我，連與他人溝通都會害怕。直到有一次，我和另兩位同學代表班級去參加辯論賽，小組成員個個都對答如流，盡管自己心裡很著急，但依舊沒有勇氣站起來辯論。對於我的一大考驗擺在面前。因為心裡希望自己能成功，希望自己能為班級做出貢獻，希望自己所在的小組能獲獎，在經過一番思想斗爭後，我還是站了起來，反對對方的觀點，說完之後，我贏得了屬於我自己的掌聲，我的內心十分欣喜。第一次，我有勇氣挑戰了自己；第一次，我不再膽怯；第一次，我也獲得了深深的鼓勵;我相信自己，我對自己說：「我能行！」
其實無論做什麼事，如何去做。都必須縣相信自己，別人才會更相信你，因為相信了自己，才會有信心一直做下去，才會學的自我欣賞，才會學有所成。不要害怕困難，也不要害怕自己不行，因為不試又怎麼知道？不經歷風雨有怎能見彩虹？相信自己就是塑造未來。
伴著盛開的花，蝴蝶才能快樂地飛舞；帶著希望，夢想才能飛往高處；迎著溫暖的風，我們不再感到孤獨。用自己的實力來證明自己，不停下追逐快樂的腳步，不停下追趕幸福的步伐，最後獲得最勇敢的幸福，只因相信自己，一直相信自己。也未曾說過放棄。
所以，相信自己，用自己的雙手去創造一個屬於自己的天堂。卸下重負，張開雙翼，自由的翱翔於天邊。
請，相信自己，揚起自信的風帆，駛向成功的彼岸！
Ⅵ 求一篇與大數據或者大數據信息安全專業相關的原版英文文獻及其翻譯,3000字左右。好人，拜託！
Big data refers to the huge volume of data that cannot
be stored and processed with in a time frame in
traditional file system.
The next question comes in mind is how big this data
needs to be in order to classify as a big data. There is a
lot of misconception in referring a term big data. We
usually refer a data to be big if its size is in gigabyte,
terabyte, Petabyte or Exabyte or anything larger than
this size. This does not define a big data completely.
Even a small amount of file can be referred to as a big
data depending upon the content is being used.
Let』s just take an example to make it clear. If we attach
a 100 MB file to an email, we cannot be able to do so.
As a email does not support an attachment of this size.
Therefore with respect to an email, this 100mb file
can be referred to as a big data. Similarly if we want to
process 1 TB of data in a given time frame, we cannot
do this with a traditional system since the resource
with it is not sufficient to accomplish this task.
As you are aware of various social sites such as
Facebook, twitter, Google+, LinkedIn or YouTube
contains data in huge amount. But as the users are
growing on these social sites, the storing and processing
the enormous data is becoming a challenging task.
Storing this data is important for various firms to
generate huge revenue which is not possible with a
traditional file system. Here is what Hadoop comes in
the existence.
Big Data simply means that huge amount
of structured, unstructured and semi-structured
data that has the ability to be processed for information. Now a days massive amount of data
proced because of growth in technology,
digitalization and by a variety of sources, including
business application transactions, videos, picture ,
electronic mails, social media, and so on. So to process
these data the big data concept is introced.
Structured data: a data that does have a proper format
associated to it known as structured data. For example
the data stored in database files or data stored in excel
sheets.
Semi-Structured Data: A data that does not have a
proper format associated to it known as structured data.
For example the data stored in mail files or in docx.
files.
Unstructured data: a data that does not have any format
associated to it known as structured data. For example
an image files, audio files and video files.
Big data is categorized into 3 v』s associated with it that
are as follows:[1]
Volume: It is the amount of data to be generated i.e.
in a huge quantity.
Velocity: It is the speed at which the data getting
generated.
Variety: It refers to the different kind data which is
generated.
A. Challenges Faced by Big Data
There are two main challenges faced by big data [2]
i. How to store and manage huge volume of data
efficiently.
ii. How do we process and extract valuable
information from huge volume data within a given
time frame.
These main challenges lead to the development of
hadoop framework.
Hadoop is an open source framework developed by
ck cutting in 2006 and managed by the apache
software foundation. Hadoop was named after yellow
toy elephant.
Hadoop was designed to store and process data
efficiently. Hadoop framework comprises of two main
components that are:
i. HDFS: It stands for Hadoop distributed file
system which takes care of storage of data within
hadoop cluster.
ii. MAPREDUCE: it takes care of a processing of a
data that is present in the HDFS.
Now let』s just have a look on Hadoop cluster:
Here in this there are two nodes that are Master Node
and slave node.
Master node is responsible for Name node and Job
Tracker demon. Here node is technical term used to
denote machine present in the cluster and demon is
the technical term used to show the background
processes running on a Linux machine.
The slave node on the other hand is responsible for
running the data node and the task tracker demons.
The name node and data node are responsible for
storing and managing the data and commonly referred
to as storage node. Whereas the job tracker and task
tracker is responsible for processing and computing a
data and commonly known as Compute node.
Normally the name node and job tracker runs on a
single machine whereas a data node and task tracker
runs on different machines.
B. Features Of Hadoop:[3]
i. Cost effective system: It does not require any
special hardware. It simply can be implemented
in a common machine technically known as
commodity hardware.
ii. Large cluster of nodes: A hadoop system can
support a large number of nodes which provides
a huge storage and processing system.
iii. Parallel processing: a hadoop cluster provide the
accessibility to access and manage data parallel
which saves a lot of time.
iv. Distributed data: it takes care of splinting and
distributing of data across all nodes within a cluster
.it also replicates the data over the entire cluster.
v. Automatic failover management: once and AFM
is configured on a cluster, the admin needs not to
worry about the failed machine. Hadoop replicates
the configuration Here one of each data iscopied or replicated to the node in the same rack
and the hadoop take care of the internetworking
between two racks.
vi. Data locality optimization: This is the most
powerful thing of hadoop which make it the most
efficient feature. Here if a person requests for a
huge data which relies in some other place, the
machine will sends the code of that data and then
other person compiles it and use it in particular
as it saves a log to bandwidth
vii. Heterogeneous cluster: node or machine can be
of different vendor and can be working on
different flavor of operating systems.
viii. Scalability: in hadoop adding a machine or
removing a machine does not effect on a cluster.
Even the adding or removing the component of
machine does not.
C. Hadoop Architecture
Hadoop comprises of two components
i. HDFS
ii. MAPREDUCE
Hadoop distributes big data in several chunks and store
data in several nodes within a cluster which
significantly reces the time.
Hadoop replicates each part of data into each machine
that are present within the cluster.
The no. of copies replicated depends on the replication
factor. By default the replication factor is 3. Therefore
in this case there are 3 copies to each data on 3 different
machines。
reference:Mahajan, P., Gaba, G., & Chauhan, N. S. (2016). Big Data Security. IITM Journal of Management and IT, 7(1), 89-94.
自己拿去翻譯網站翻吧，不懂可以問
Ⅶ 隨著大數據時代英文
摘要：As the big data era, all kinds of data in the society is growing at a rapid speed, the library also inevitably faced with the impact of the wave data information. This paper analyses the characteristics of data, data source library management, focusing on the large data will be brought about by the challenge, the final analysis of the times books management development direction of large data. Mainly includes the influence on library management of large data: data of complex data processing test library computing power, data analysis to the mining depth of traditional requirements for Library Infrastructure challenges and big data era. Future library management from the exploratory data analysis tools and technology, attach importance to the construction of infrastructure and data collection, improving several books management intelligent degree of development. Keywords large structured data and unstructured Library
Ⅷ 英語作文大數據和我們的生活
First,I think my life is healthy.I'm allowed to play computer games every day.So I could be relaxed myself.Then,I go to school on foot,beacuse it isn't far from my home.I am healthy.I also paly football after school.Finally,I sleep eight hours every night.I get up early.I often have a quick breakfast,and take a shower.
In a word,I think it's necessary for us to keep in a good health.
Ⅸ "大數據"怎樣用英文表述呢
大數據的英文翻譯是big data。
釋義：大數據；巨量資料；海量資料；海量數據
big block data稱為大區塊資料 ; 大區塊資料
Big Bang Data數據大爆炸
Big Earth Data地球大數據
Big Brain Data大腦巨量資料
Big Complex Data大型復雜數據
1、?
大數據將如何改變您的做事方式？
2、.
但是龐大數據還會產生遠比這更為嚴重的後果。
3、 themselves.
在龐大數據的世界中，相關數據幾乎是自行浮出水面。
4、Ifyourservicedeals withbigdata,that's howthey'rerelated.
如果你的服務要處理大數據，那正是它們相關的東西。
5、Whatis therelationshiptoSOA?Related tothisisBigData, how isitrelatedtoSOA?
它與SOA之間有什麼關系嗎？與之關聯的是大數據，那麼它又是怎樣和SOA關聯起來的呢？