大数据和云计算技术研究外文文献翻译2017

大数据和云计算技术研究外文文献翻译2017
外文文献翻译原文及译文
文献出处Bryant R. The research of big data and cloud computing
technology [J]. Information Systems, 2017, 3(5): 98-109 原文 The
research of big data and cloud
computing technology Bryant Roy
Abstract
Mobile Internet and the rapid development of Internet of things and
cloud computing technology open the prelude of the era of mobile cloud,
big data is becoming more and more attract the line of sight of people.
The emergence of the Internet shortens people, the distance between
people and the world, the whole world into a
through the network barrier-free exchange, exchange information and work
together. At the same time, with the rapid development of Internet,
mature and popular database technology, high memory, high- performance
storage devices and storage media, human in daily study, life and work
of the amount of data is growing exponentially. Big data problem is
produced under such background, become research hot topic in academia
and relevant industry, and as one of the important frontier research
topic in the field of information technology, attracting more and more
scholars studying the effects of large data related problems.
Key words: Big data; Data analysis; Cloud computing
1 Introduction

Big data is a kind of can reflect the material world and spiritual
world motion state and the change of state of information resources, it
has the complexity, sparse of decision usefulness, high-speed growth,
value and repeatable mining, generally has many potential value. Based
on the perspective of big data resource view and management, we think
that big data is a kind of important resources that can support
management decisions. Therefore, in order to effectively manage the
resources and give full play to their potential value, need to study and
solve this kind of resource acquisition, processing and application, the
definition of the property industry
development and policy guarantee management issues.
Big data has the following features:
Complexity, as many definition points out, the form and the
characteristic of big data is very complicated. The complexity of the
large data in addition to performance in its quantity scale, the source
of the universality and diversity of morphological structure, but also
in the change of state and the uncertainty of the respect such as
development way. Decision usefulness, big data itself is objective
existence of large-scale data resource. Its direct function is limited.
Through the analysis and mining, and found its knowledge, can provide
all kinds of practical application with other resources to provide
decision support, the value of big data is mainly reflected by its
decision usefulness. The total stock of non-renewable natural resources
with the mining and gradually reduce human, while big data with high

speed growth, namely along with the continuous mining, large data
resources not only will not reduce, instead will increase rapidly.
Sparse sex value, great amount of data that the data in has brought many
opportunities at the same time, also brought a lot of challenges. One of
the major challenges is the problem of big data value low density, large
data resources quantity is big, but its useful value is sparse, this
increases the difficulty of the development and use of big data
resources.
2 Processing of big data
2.1 Data collection
Big data, originally meant the number and types of the more complex,
therefore, it becomes especially important to get the data information
through various methods. Data acquisition is the basis of a large data
processing in the process step, the common methods for data collection
with RFID, the classification of the data retrieval tools such as Google
and other search engines, as well as bar code technology and so on. And
because the emergence of the mobile devices, such as the rapid
popularity of smart phones and tablets, makes a large number of mobile
application software is developed, social network gradually large, it
also accelerated the velocity of
circulation of information and acquisition precision. 2.2 Data
processing and integration
Data processing and integration is mainly completed to have properly
deal with the data collected, cleaning demising, and further integration

of storage. According to the mentioned above, is one of the features
large data diversity. This decision through various channels to obtain
the data types and structures are very complex, brought after the data
analysis and processing of great difficulty. Through data processing and
integration this step, first, the structure of complex data into a
single or a structure for easy handling, to lay a good foundation for
the later data analysis, because not all of the information in the data
are required, therefore, will need to be
to ensure the quality and reliability of the data. Commonly used method
is in the process of data processing design some data filter, through
clustering or the rules of the correlation analysis method will be
useless or wrong pick out from the group of data filtering to prevent
its adverse influence on the final data results. Then these good
integration and the data storage, this is an important step, if it's
pure random placement, will affect the later data access, could easily
lead to data accessibility issues, the general solution is now for
specific types of data to establish specialized database, the different
kinds of data information classify placement, can effectively reduce the
number of data query and the access time, increase the speed of data
extraction. 2.3 Data analysis
Data analysis is the core part in the whole big data processing,
because in the process of data analysis, find the value of the data.
After a step data processing and integration, the data will become the
raw data for data analysis, according to the requirements of the

application of the data required for further processing and analysis
data. Traditional data processing method of data mining, machine
learning, intelligent algorithm, statistical analysis, etc., and these
methods have already can't meet the demand of the era of big data
analysis. In terms of data analysis technology, Google is the most
advanced one, Google as big data is the most widely used Internet
company, in 2006, the first to put forward the concept of
computing
applications are backing, Google's own internal research and
development of a series of cloud computing technology.
2.4 Data interpretation
Data information for the majority of the users, the most concerned
about is not the analysis of the data processing, but the explanation
for big data analysis and display, as a result, in a perfect data
analysis process, the results of data interpretation steps is very
important. If the results of data analysis can not properly display,
will create trouble for data users, even mislead users. According to the
traditional way is to use text output or download user personal computer
display. But with the increase of amount of data, data analysis, the
result is often more complex, according to the traditional way is not
enough to satisfy the demand of the data analysis results output,
therefore, in order to improve data interpretation, show ability, now
most of the enterprise data visualization technology is introduced as a
way to explain the big data is the most powerful. Through the

visualization result analysis, can vividly show the user the data
analysis results, more convenient for users to understand and accept the
results. Common visualization techniques are based on a collection of
visualization technology, technology, based on image technology based on
ICONS. Pixel oriented technology and distributed technology, etc.
3 Challenges posed by big data
3.1 Big data security and privacy issues
With the development of big data, data sources and is finding wider
and wider application fields: casual web browsing on the Internet will
be a series of traces left behind. In the network login related websites
need to input personal important information, such as id number, address,
phone number etc. Ubiquitous cameras and sensors will record the
personal behavior and location information, etc. Through related data
analysis, data experts can easily dig up people's habits and personal
important information. If this information is applied proper, can help
enterprises to understand the needs of the customers at any time in the
field of related and habits,
facilitate enterprises to adjust the corresponding production plan,
make greater economic benefits. But if these important information is
stolen by bad molecules, followed is the security of personal
information, property, etc. In order to solve the problem of the data of
the era of large data privacy, academia and industry are put forward
their own solutions. In addition, the data of the era of big data update
speed change, and the general data privacy protection technology are

mostly based on static data protection, this gives privacy has brought
new challenges.
Under the condition of complex changes, how to implement the data
privacy protection will be one of the key directions in the study of
data in the future. 3.2 Large data integration and management
Throughout the development of large data, the source of the large
data and application is more and more widely, in order to spread in
different data management system of the data collected, it is necessary
for data integration and management. Although the data integration and
management have a lot of methods, but the traditional data storage
method already can't meet the demand of the era of big data processing,
it is faced with new challenges. Big data era, one of the
characteristics of big data is the diversity of data types. The data
type by gradually transforms the traditional structured data semi-
structured and unstructured data. In addition, the data sources are
increasingly diversified, the traditional data mostly come from a small
number of military enterprise or institute computer terminals. Now, with
the popularity of the Internet and mobile devices in the global, the
data storage is especially important.
You can see by the above, the traditional way of data storage is not
enough to meet the demand of present data storage, in order to deal with
more and more huge amounts of data and increasingly complex data
structures, many companies are working on is suitable for the era of big
data distributed file system and distributed parallel database. In the

process of data storage, data format conversion is necessary, and it is
very critical and complex, it puts forward higher requirements on data
storage system.
3.3 The ecological environment question in the big data The
ecological environment problems of big data firstly refer to data
resource management and sharing. This is an era of information opening,
the open architecture of the Internet can make people in different
corners of the earth all share network resources at the same time, it
brought great convenience to the scientific research work. But not all
of the data can be Shared, unconditional some data for the value of its
special properties and is protected by the law can be unconditional. Due
to the relevant legal measures is not sound enough, now still lack of a
strong enough data protection consciousness, so there is always the data
information stolen or data ownership problems, it has both technical
problems and legal problems. How to protect the interests of the parties
under the premise of solving the problem of data sharing is going to be
most important challenges in the era of big data. In the era of big data,
data of production and the application field is no longer limited to a
few special occasions, almost all of the fields such as you can see the
figure of big data, therefore, involve the problem of data in the field
of cross is inevitable.,
along with the development of large data influence the results of
analysis of large data set to be the state governance mode, enterprise
decision-making, organization and business process, such as personal

lifestyles will have a significant impact, and the impact model is worth
in-depth research in the future.
译文大数据和云计算技术研究
Bryant Roy
摘要移动互联网、物联网和云计算技术的迅速发展，开启了移动云时代的序
幕，
大数据也越来越吸引人们的视线。Internet的出现缩短了人与人、人与世界之
间
的距离，整个世界连成一个“地球村”，人们通过网络无障碍交流、交换信息
和
协同工作。与此同时，借助Internet的高速发展、数据库技术的成熟和普
及、高
内存高性能的存储设备和存储介质的出现，人类在日常学习、生活、工作中产
生
的数据量正以指数形式增长。大数据问题就是在这样的背景下产生的，成为科
研
学术界和相关产业界的热门话题，并作为信息技术领域的重要前沿课题之一，
吸
引着越来越多的学者研究大数据带来的相关问题。
关键词:大数据;数据分析;云计算
1 引言
大数据是一类能够反映物质世界和精神世界运动状态和状态变化的信息资源，它具有复杂性、决策有用性、高速增长性、价值稀疏性和可重复开采性，一般具有
多种潜在价值。基 于大数据的资源观和管理的视角，认为大数据是一类能支持管理

决策的重要资源。因此， 为了有效管理这种资源并充分发挥其潜在价值，就需要研
究并解决这种资源的获取、加工、应用、产权界 定、产业发展和政策保障等管理问
题。大数据具有以下特征:
复杂性，正如很多定义所指出 的，大数据的形式和特征是极其复杂的。大数据
的复杂性除了表现在其数量规模之大、来源的广泛性和形 态结构的多样性外，还表
现在其状态变化和开发方式等方面的不确定性。决策有用性，大数据本身是客观 存
在的大规模数据资源，其直接功用是有限的。通过分析、挖掘和发现其中蕴藏的知
识，可以为 各种实际应用提供其它资源难以提供的决策支持，大数据的价值也主要
通过其决策有用性体现。高速增长 性，大数据资源的这一特征与石油等自然资源是
不同的。不可再生的自然资源的总存量会随着人类不断开 采而逐渐减少，而大数据
却具有高速增长性，即随着不断开采，大数据资源不仅不会减少，反而会迅速增
加。价值稀疏性，大数据的数据量之大在带来了诸多机遇的同时，也带来了不少挑
战。其主要挑 战之一就是大数据价值的低密度问题，大数据资源的数量虽大，但其
中蕴藏的有用的价值却是稀疏的，这 就增加了开发和利用大数据资源的难度。
2大数据处理流程
2.1数据采集
大数据，原本就意味着数量多、种类复杂，因此，通过各种方法获取数据信息
便显得格外重要。数据采 集是大数据处理流程中最基础的一步，目前常用的数据采
集手段有RFID、数据检索分类工具如谷歌等 搜索引擎，以及条形码技术等。并且
由于移动设备的出现，如智能手机和平板电脑的迅速普及，使得大量 移动软件被开
发应用，社交网络逐渐庞大，这也加速了信息的流通速度和采集精度。 2.2数据
处理与集成
数据的处理与集成主要是完成对于已经采集到的数据进行适当的处理 、清洗去
噪以及进一步的集成存储。根据前文所述，大数据特点之一就是多样性。这就决定

了经过各种渠道获取的数据种类和结构都非常复杂，给之后的数据分析处理带了极
大的困难。 通过数据处理与集成这一步骤，首先将这些结构复杂的数据转换为单一
的或是便于处理的结构，为以后的 数据分析打下良好的基础，因为这些数据里并不
是所有的信息都是必需的，因此，还需对这些数据进行“ 去噪”和清洗，以保证数
据的质量以及可靠性。常用的方法是在数据处理的过程中设计一些数据过滤器， 通
过聚类或关联分析的规则方法将无用或错误的离群数据挑出来过滤掉，防止其对最
终数据结果 产生不利影响;然后将这些整理好的数据进行集成和存储，这是很重要
的一步，若是单纯随意的放置，则 会对以后的数据取用造成影响，很容易导致数据
访问性的问题，现在一般的解决方法是针对特定种类的数 据建立专门的数据库，将
这些不同种类的数据信息分门别类的放置，可以有效地减少数据查询和访问的时
间，提高数据提取速度。
2.3 数据分析
数据分析是整个大数据处理流程里 最核心的部分，因为在数据分析的过程中，
会发现数据的价值所在。经过上一步骤数据的处理与集成后， 所得的数据便成为数
据分析的原始数据，根据所需数据的应用需求对数据进行进一步的处理和分析。传< br>统的数据处理分析方法有数据挖掘、机器学习、智能算法、统计分析等，而这些方
法已经不能满足 大数据时代数据分析的需求(在数据分析技术方面，Google是最先
进的一个, Google作为 互联网大数据应用最为广泛的公司，于2006年率先提出了
“云计算”的概念，其内部各种数据的应用 都是依托,Google自己内部研发的一系
列云计算技术。
2.4数据解释
对于广大的数据信息用户来讲，最关心的并非是数据的分析处理过程，而是对
大数据分析结果的解释与展 示，因此，在一个完善的数据分析流程中，数据结果的
解释步骤至关重要。若数据分析的结果不能得到恰 当的显示，则会对数据用户产生

困扰，甚至会误导用户。传统的数据显示方式是用文本形 式下载输出或用户个人电
脑显示处理结果。但随着数据量的加大，数据分析结果往往也越复杂，用传统的 数
据显示方法已经不足以满足数据分析结果输出的需求，因此，为了提升数
据解释、展示能力 ，现在大部分企业都引入了数据可视化技术作为解释大数据
最有力的方式。通过可视化结果分析，可以形 象地向用户展示数据分析结果，更方
便用户对结果的理解和接受。常见的可视化技术有基于集合的可视化 技术、基于图
标的技术、基于图像的技术、面向像素的技术和分布式技术等。 3大数据带来的
挑战
3.1大数据的安全与隐私问题
随着大数据的发展，数据 的来源和应用领域越来越广泛:在互联网上随意浏览
网页，就会留下一连串的浏览痕迹;在网络中登录相 关网站需要输入个人的重要信
息，例如身份证号、手机号、住址等。随处可见的摄像头和传感器会记录下 个人的
行为和位置信息等。通过相关的数据分析，数据专家就可以轻易挖掘出人们的行为
习惯和 个人重要信息。如果这些信息运用得当，可以帮助相关领域的企业随时了解
客户的需求和习惯，便于企业 调整相应的产品生产计划，取得更大的经济效益。但
若是这些重要的信息被不良分子窃取，随之而来的就 是个人信息、财产等的安全性
问题。为了解决大数据时代的数据隐私问题，学术界和工业界纷纷提出自己 的解决
办法。此外，大数据时代数据的更新变化速度加快，而一般的数据隐私保护技术大
都基于 静态数据保护，这就给隐私保护带来了新的挑战。在复杂变化的条件下如何
实现数据隐私安全的保护，这 将是未来大数据研究的重点方向之一。
3.2 大数据的集成与管理纵观大数据的发展历程，大数据 的来源与应用越来越
广泛，为了把散布于不同的数据管理系统的数据收集起来统一整理，就有必要进行< br>数据的集成与管理。虽然对数据的集成和管理已经有了很多的方法，但是传统的数
据存储方法已经 不能满足大数据时代数据的处理需求，这就面临着新的挑战。数据

存储。大数据时代，大 数据的特征之一就是数据类型的多样性。数据类型由传统的
结构化数据逐渐转变为半结构化、非结构化数 据。另外，数据的来源也逐渐多样
化，传统的数据大都来自于少部分军事企业或是研究所的电脑终端;现 在，随着互
联网和移动设备在全球的普及，数据的存储就显得格外重要(由前文可看出，传统
的 数据存储方式已经不足以满足现在的数据存储需求，为了应对越来越多的海量数
据和日渐复杂的数据结构 ，很多公司都着手研发适用于大数据时代的分布式文件系
统和分布式并行数据库。在数据存储过程中，数 据格式的转
换是必要的，而且是非常关键和复杂的，这就对数据存储系统提出了更高的要
求。 3.3大数据的生态环境
大数据的生态环境问题首先涉及的是数据资源管理和共享的问题。这是一个 信
息化开放的时代，互联网的开放式结构使人们可以在地球的不同角落同时共享所有
的网络资源 ，这给科研工作带来了极大的便利。但是并不是所有的数据都是可以被
无条件共享的，有些数据因为其特 殊的价值属性而被法律保护起来不能随意被无条
件利用。由于现在相关的法律措施还不够健全，还缺乏足 够强的数据保护意识，所
以总会出现数据信息被盗用或是数据所有权归属的问题，这既有技术问题也有法 律
问题。如何在保护多方利益的前提下解决数据共享问题将是大数据时代的一大重要
挑战(在大 数据时代，数据的产生和应用领域已经不局限于某几个特殊的场合，几
乎所有的领域等都能看到大数据的 身影，因此，涉及这些领域的数据交叉问题就不
可避免。随着大数据影响力的深入，大数据的分析结果势 必将会对国家治理模式，
企业的决策、组织和业务流程，个人生活方式等都将产生巨大的影响，而这种影 响
模式是值得以后深入研究的。