A. 大數據英文怎麼說
大數據（bigdata），IT行業術語，是指無法在一定時間范圍內用常規軟體工具進行捕捉、管理和處理的數據集合，是需要新處理模式才能具有更強的決策力、洞察發現力和流程優化能力的海量、高增長率和多樣化的信息資產。
在維克托·邁爾-舍恩伯格及肯尼斯·庫克耶編寫的《大數據時代》中大數據指不用隨機分析法（抽樣調查）這樣捷徑，而採用所有數據進行分析處理。大數據的5V特點（IBM提出）：Volume（大量）、Velocity（高速）、Variety（多樣）、Value（低價值密度）、Veracity（真實性）。
大數據包括結構化、半結構化和非結構化數據，非結構化數據越來越成為數據的主要部分。據IDC的調查報告顯示：企業中百分之80的數據都是非結構化數據，這些數據每年都按指數增長百分之60。大數據就是互聯網發展到現今階段的一種表象或特徵而已，沒有必要神話它或對它保持敬畏之心，在以雲計算為代表的技術創新大幕的襯托下，這些原本看起來很難收集和使用的數據開始容易被利用起來了，通過各行各業的不斷創新，大數據會逐步為人類創造更多的價值。
B. "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？
C. 大數據用英文怎麼說
大數據(big data)指規模巨大且復雜，用現有的數據處理工具(on-hand database management
tools)難以獲取(capture)、整理( curate)、管理(
manage)以及處理(process)的數據信息統稱。大數據的特點可以總結為4V：volume(大量)、velocity(高速)、variety(多變)、veracity(准確)。
D. 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
E. hadoop參考文獻有哪些
《大數據技術原理與應用—概念、存儲、處理、分析與應用》。hadoop參考文獻有《大數據技術原理與應用—概念、存儲、處理、分析與應用》，Hadoop是一個開源的框架，可編寫和運行分布式應用處理大規模數據。
F. 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
G. 大數據是什麼
大數據是什麼意思呢?
如果從字面意思來看，大數據指的是巨量數據。那麼可能有人會問，多大量級的數據才叫大數據?不同的機構或學者有不同的理解，難以有一個非常定量的定義，只能說，大數據的計量單位已經越過TB級別發展到PB、EB、ZB、YB甚至BB級別。
最早提出「大數據」這一概念的 是全球知名咨詢公司麥肯錫，它是這樣定義大數據的：一種規模大到在獲取、存儲、管理、分析方面大大超出了傳統資料庫軟體工具能力范圍的數據集合,具有海量的數據規模、快速的數據流轉、多樣的數據類型以及價值密度低四大特徵。
研究機構Gartner是這樣定義大數據的：「大數據」是需要新處理模式才能具有更強的決策力、洞察發現力和流轉優化能力來適應海量、高增長率和多樣化的信息資產。若從技術角度來看，大數據的戰略意義不在於掌握龐大的數據，而在於對這些含有意義的數據進行專業化處理，換言之，如果把大數據比作一種產業，那麼這種產業盈利的關鍵在於提高對數據的「加工能力」，通過「加工」實現數據的「增值」。