《大数据专业英语》课件—12Data Security
大数据专业英语教程 Unit 12 How to Manage Big Data’s Big
Notes
[3] The variety, velocity and volume of big data amplify the security management challenges that are addressed in traditional security management.
v.制作 adj.巨大的，庞大的 n.无效率，无能 adj.整理过的；统一的；加固的 adj.诱惑人的 n.攻击者
New Words
ቤተ መጻሕፍቲ ባይዱ
recognition devastating amplify deposit
dataset regulatory adequate workflow adversary configuration authenticate
Phrases
consumer need share with crown jewels trade secret upwards of financial institution government regulation come into play on a case-by-case basis data transfer distributed environment
New Words
node vulnerability straightforward
patch
automation framework uniform deactivate inactive probability offensive
prudent
[] [❖] [ ]
[ ]
[] [ ] [] [ ❖] [ ❖] [] [❖]
[t]
n.节点 n.弱点，攻击 adj.坦率的，简单的，易懂的，直截了 当的
大数据英文版
大数据英文版Big Data: An IntroductionIntroduction:Big Data refers to the large and complex datasets that cannot be easily managed, processed, and analyzed using traditional data processing tools and techniques. With the rapid advancement in technology, organizations are now able to collect and store massive amounts of data from various sources such as social media, sensors, and online transactions. This data, when properly analyzed, can provide valuable insights and help businesses make informed decisions. In this article, we will explore the concept of Big Data in detail, its characteristics, and its importance in today's digital age.Characteristics of Big Data:1. Volume: Big Data is characterized by its sheer volume. Traditional databases are not capable of handling such large amounts of data. For example, social media platforms generate billions of posts, comments, and likes every day, resulting in massive amounts of data that needs to be processed and analyzed.2. Velocity: The speed at which data is generated is another characteristic of Big Data. Real-time data streams, such as stock market data or sensor data, need to be processed and analyzed quickly to extract meaningful insights. The ability to process data in real-time is crucial for businesses to respond promptly to changing market conditions.3. Variety: Big Data comes in various formats and types. It includes structured data, such as relational databases, as well as unstructured data, such as text documents, images, and videos. Additionally, Big Data can also include semi-structured data, such as XML or JSON files. The ability to handle and analyze different types of data is essential in deriving valuable insights.Importance of Big Data:1. Decision Making: Big Data analytics enables organizations to make data-driven decisions. By analyzing large datasets, businesses can identify patterns, trends, and correlations that can help them understand customer behavior, optimize operations, and develop targeted marketing strategies. For example, an e-commerce company can use Big Data analytics to analyze customer browsing patterns and preferences to offer personalized product recommendations.2. Innovation: Big Data has the potential to drive innovation in various industries. By analyzing large datasets, businesses can identify new market opportunities, develop innovative products and services, and improve existing processes. For instance, healthcare organizations can leverage Big Data analytics to identify disease patterns, predict outbreaks, and develop effective treatment plans.3. Cost Reduction: Big Data technologies can help organizations reduce costs and improve efficiency. By analyzing data from various sources, businesses can identify areas of wastage, optimize resource allocation, and streamline operations. For example, logistics companies can use Big Data analytics to optimize their delivery routes, reduce fuel consumption, and improve overall operational efficiency.Challenges of Big Data:1. Data Privacy and Security: With the increasing amount of data being collected, data privacy and security have become major concerns. Organizations need to ensure that they have robust security measures in place to protect sensitive data from unauthorized access or breaches. Additionally, they must comply with relevant data protection regulations and ensure that customer data is handled responsibly.2. Data Quality: The quality of data is crucial for accurate analysis and decision-making. Big Data often comes from various sources and may contain errors, inconsistencies, or missing values. Data cleansing and preprocessing techniques are necessary to ensure that the data is accurate, complete, and reliable.3. Skills and Expertise: Analyzing Big Data requires a specialized skill set. Data scientists and analysts need to have a deep understanding of statistical analysis, machinelearning, and data visualization techniques. Organizations need to invest in training and hiring skilled professionals to effectively leverage Big Data.Conclusion:Big Data has revolutionized the way organizations operate and make decisions. The ability to collect, store, and analyze massive amounts of data has opened up new possibilities for businesses across various industries. By harnessing the power of Big Data analytics, organizations can gain valuable insights, drive innovation, and improve operational efficiency. However, it is important to address the challenges associated with Big Data, such as data privacy and security, data quality, and the need for skilled professionals.。
大数据英文版 (2)
大数据英文版Big Data: Revolutionizing the WorldIntroduction:Big Data, a term that refers to the large and complex sets of data that cannot be easily managed or processed using traditional data processing tools, has emerged as a game-changer in various industries. This article aims to explore the significance of Big Data and its impact on different sectors of the economy.1. What is Big Data?Big Data refers to the massive volume of structured and unstructured data that is generated from various sources such as social media, sensors, mobile devices, and more. It is characterized by the five V's: volume, velocity, variety, veracity, and value. The volume of data generated is enormous, and it is generated at an unprecedented velocity. The variety of data includes text, images, videos, and more. Veracity refers to the quality and reliability of data, while value represents the insights and benefits that can be derived from analyzing this data.2. Importance of Big Data:Big Data has become increasingly important due to its potential to provide valuable insights and drive decision-making processes. It has the power to transform businesses, governments, and society as a whole. The key reasons why Big Data is important are as follows:2.1. Improved Decision Making:Big Data analytics enables organizations to analyze vast amounts of data to uncover patterns, trends, and correlations. These insights help businesses make informed decisions, identify new opportunities, and optimize their operations.2.2. Enhanced Customer Experience:By analyzing customer data, organizations can gain a deeper understanding of their preferences, behavior, and needs. This allows them to personalize their offerings, improve customer service, and enhance overall customer experience.2.3. Cost Reduction and Efficiency:Big Data analytics can identify inefficiencies and areas of improvement within processes, leading to cost reductions and increased operational efficiency. For example, predictive maintenance can help prevent equipment failures, saving both time and money.2.4. Innovation and New Business Models:Big Data has the potential to drive innovation and the development of new business models. By analyzing data, organizations can identify emerging trends, market gaps, and untapped opportunities, leading to the creation of new products and services.3. Impact of Big Data on Different Sectors:Big Data has revolutionized various sectors, bringing about significant changes and improvements. Let's explore its impact on some key sectors:3.1. Healthcare:Big Data analytics has the potential to transform healthcare by improving patient outcomes, reducing costs, and enabling personalized medicine. By analyzing patient data, healthcare providers can identify patterns and predict diseases, leading to early diagnosis and timely interventions. Moreover, Big Data can help optimize healthcare operations, supply chain management, and resource allocation.3.2. Retail:Big Data analytics has revolutionized the retail industry by enabling personalized marketing, inventory optimization, and demand forecasting. By analyzing customer data, retailers can provide personalized recommendations, promotions, and offers, enhancing the customer experience. Additionally, Big Data analytics helps retailers optimize their inventory levels, reducing costs and minimizing stockouts.3.3. Finance:Big Data has transformed the finance industry by enabling better risk management, fraud detection, and customer insights. By analyzing financial data, banks and financial institutions can identify potential risks, detect fraudulent activities, and make informed lending decisions. Moreover, Big Data analytics helps financial institutions understand customer behavior, preferences, and needs, enabling them to provide personalized financial services.3.4. Transportation:Big Data analytics has revolutionized the transportation industry by improving efficiency, reducing congestion, and enhancing safety. By analyzing data from sensors, GPS devices, and traffic cameras, transportation companies can optimize routes, predict traffic patterns, and improve fleet management. Additionally, Big Data analytics enables the development of smart transportation systems, such as intelligent traffic lights and real-time public transportation updates.4. Challenges and Future Trends:While Big Data offers immense opportunities, it also presents several challenges. Some of the key challenges include data privacy and security, data quality, data integration, and talent shortage. Organizations need to address these challenges to fully leverage the potential of Big Data.Looking ahead, the future of Big Data seems promising. With the advancements in technology, such as artificial intelligence and machine learning, the capabilities of Big Data analytics will continue to expand. Moreover, the increasing adoption of Internet of Things (IoT) devices will generate even more data, further fueling the Big Data revolution.Conclusion:Big Data has become a driving force in today's digital era. Its ability to analyze large volumes of data and extract valuable insights has transformed various sectors, includinghealthcare, retail, finance, and transportation. By harnessing the power of Big Data, organizations can make informed decisions, enhance customer experiences, and drive innovation. However, addressing challenges such as data privacy and talent shortage is crucial to fully realize the potential of Big Data. As technology continues to evolve, the future of Big Data looks promising, opening up new possibilities for businesses and society as a whole.。
大数据英语PPT演示课件
The early years of data revolution:
challenges
challenges
Data
privacy access and sharing
Analysis
“what is the data really telling us?”
summarizing the data interpreting defining and detecting anomalties
Data revolution
today a massive amount of data is regularly being generated and flowing from various sources, through different channels, every minute in today’s Digital Age.
fig. New types of research data about human behavior and society pose many opportunities if crucial infrastructural challenges are tackled.
Part 5 conclusion
Characteristics:
Volume : data size Velocity :speed of change Variety : different forms of data sources
application
application
Bank transactions
1.3 million transactions in 2015 worldwide;
《大数据专业英语》课件—12Data Security
个性化互动 购物体验 数据井 网络罪犯 只是…的问题 设立，安上 留神，谨防，提防 风险管理，风险管控 在许多方面 安全威胁
Phrases
dynamic data static data storage medium computational security access control method granular access control mandatory access control security flaw keep in mind
参考译文
2.10数据存储的隐私保护 NoSQL等数据存储存在许多安全漏洞，这些漏洞会导致隐私威胁。一个突出的安 全漏洞是，在标记或记录数据期间或在流式传输或收集数据时，无法加密数据； 把数据分发到不同的组的时候，也无法加密数据。
3.结论 组织必须确保所有大数据库都免受安全威胁和漏洞的影响。在数据收集期间，应 实现所有必要的安全保护，例如实时管理。考虑到大数据的庞大规模，组织应该 记住管理此类数据可能很困难并需要非常努力。但是，采取所有这些步骤将有助 于维护消费者隐私。
v.自动分级
n.验证，确认 n.过滤；筛选 adj.可信的，可靠的；认证了的 adj.合法的，合理的；正规的
n.预防；阻止，制止 n.映射器；映射程序 adj.智能的；聪明的；有智力的 adj.易受攻击的 n.来源，起源，出处 n.身份验证；认证；证明，鉴定 v.辨认，识别，承认
New Words
参考译文
2.大数据安全和隐私的挑战 大数据无法仅根据其规模来描述。但是，最基本的理解是，大数据是无法以传统数 据库方式处理其大小的数据集。这种数据积累有助于以多种方式改善客户服务。但 是，如此庞大的数据也会带来许多隐私问题，使大数据安全成为任何组织的主要关 注点。在数据安全和隐私领域，许多组织正在承认这些威胁的存在，并采取措施防 止这些威胁。
大数据专业词汇英语
大数据专业词汇英语Key Terminology in Big Data Analytics.In the realm of big data analytics, a comprehensive understanding of key terminology is paramount toeffectively navigate and harness the vast sea of data.Here's a glossary of essential terms that will empower youto engage confidently in big data discussions and endeavors:Data Analytics: The systematic examination and interpretation of data to extract meaningful insights and patterns.Hadoop: An open-source software framework thatfacilitates distributed data processing, enabling the efficient handling of vast datasets across clusters of computers.Cloud Computing: A model for delivering computing services, including servers, storage, databases, networking,software, analytics, and intelligence, over the internet ("the cloud") to offer flexible and scalable access to computing resources.Data Lake: A centralized repository for storing vast volumes of raw, unstructured data in its native format, enabling flexible exploration and analysis.Data Warehouse: A structured repository of data, typically consisting of historical data, organized and optimized for querying and reporting purposes.Data Mining: The process of extracting hidden patterns and insights from large datasets through automated or semi-automated techniques.Machine Learning: A subset of artificial intelligence that enables computers to learn from data without explicit programming by identifying patterns and making predictions.Artificial Intelligence (AI): The simulation of human intelligence processes by machines, encompassing learning,reasoning, and problem-solving capabilities.NoSQL: A non-relational database management system designed to handle large volumes of unstructured or semi-structured data, offering flexibility and scalability.Hadoop Distributed File System (HDFS): A distributed file system that enables the storage of large data files across multiple commodity servers, providing fault tolerance and high availability.MapReduce: A programming model for processing and generating large datasets that is used in conjunction with Hadoop, where data is processed in parallel and aggregated to produce the final result.Business Intelligence (BI): A set of techniques and technologies used to transform raw data into meaningful and actionable information for business decision-making.Apache Spark: A fast and versatile open-source distributed computing engine that supports a wide range ofbig data processing tasks, including real-time stream processing.Extract, Transform, Load (ETL): The process of extracting data from disparate sources, transforming itinto a consistent format, and loading it into a target system for analysis.Data Governance: The policies, processes, and practices that ensure the reliability, integrity, and security of data throughout its lifecycle.Data Visualization: The graphical representation of data to facilitate the identification of patterns, trends, and insights.Data Scientist: A professional who possesses expertise in data analysis, machine learning, and statistical modeling, responsible for extracting insights and building predictive models from large datasets.Big Data: A term used to describe extremely large andcomplex datasets that traditional data processing softwareis inadequate to handle.Data Quality: The degree to which data conforms to predefined standards of completeness, accuracy, consistency, timeliness, and validity.Data Security: The measures and practices implementedto protect data from unauthorized access, use, disclosure, disruption, modification, or destruction.Open Data: Data that is made freely available to the public without any copyright, patent, or other restrictions, promoting transparency and innovation.Data Privacy: The regulations and ethicalconsiderations governing the collection, storage, use, and disclosure of personal data to protect individuals' privacy rights.Data Curation: The selection, acquisition, preservation, and documentation of data to ensure its availability,usability, and authenticity over time.Data Lakehouse: A unified data management platform that combines the scalability and flexibility of a data lakewith the structure and governance of a data warehouse, enabling both operational and analytical workloads.Modern Data Stack: A collection of cloud-based toolsand technologies that facilitate the collection, storage, transformation, and analysis of big data in a scalable and cost-effective manner.Data Fabric: An architectural approach that enables the integration and interoperability of data across diverse systems and environments to provide a unified andconsistent data experience.By understanding these key terms, you'll be well-equipped to navigate the ever-evolving world of big data analytics and leverage its transformative potential todrive informed decisions and achieve organizational success.。
《大数据专业英语》课件—02Data Model
adj.麻烦的;累赘的；复杂的 n.矢量 n.多边形，多角形 n.光栅 n.地理（学）；地形，地势；布局 adj.接触的，邻近的；共同的 adj.不相重叠的 n.三角形 n.一般化，普通化；归纳，概论 adj.传统的；平常的；依照惯例的 n.短处，缺点 n.障碍，障碍物
New Words
invariably attributable instantiate concretely interrelationship satisfy
New Words
inheritance diagram
[ɪ nˈherɪtəns] [ˈdaɪəgræ m]
graphical notation document
[ˈgræ fɪ kl] [nəʊˈteɪʃn] [ˈdɒkjʊmənt]
bind arrow extension notable cardinality robust
[ɪnˈveərɪəblɪ ] [əˈtrɪbjʊtəbl] [ɪns'tæ nʃɪ eɪ t] ['kɒŋkri:tlɪ ] [ˌɪ ntərɪ ˈleɪʃnʃɪp] [ˈsæ tɪsfaɪ ]
resource
[rɪˈsɔ:s]
adv.总是；不变的 adj.可归因于…的；由…引起的 vt.例示 adv.具体地 n.相互关系，相互联系；影响，干扰 vt.符合，达到（要求、规定、标准等） vi.使足够；使满意 n.资源
[ˈmɒdl]
[ˈstæ ndədaɪz] [sens]
[ˌfɔ:məlaɪ'zeɪʃn] [ˌmæ njʊˈfæ ktʃərɪŋ]
[ˈteɪbl]
[ˈdi:teɪ l]
[dɪˈzaɪn] [ɪ ˈneɪ bl]
《大数据专业英语》课件—08Data Processing
adj.预定义的 n.沉淀物 v.沉淀 v.连接；联结
vt.调查；审查；研究 vi.作调查
Phrases
data pre-processing garbage in, garbage out data gathering missing value computational biology knowledge discovery training set survey data be split into macro editing aggregation method
[ɪˈreləvənt] [ˈnɔɪzɪ]
unreliable preparation filter considerable
[ˌʌnrɪˈlaɪəbl] [ˌprepəˈreɪʃn] [ˈfɪltə] [kənˈsɪdərəbl]
selection transformation extraction perform manually assistance
参考译文
2.1.3宏编辑 宏编辑有两种方法： •聚合方法 在发布之前，几乎每个统计机构都遵循这种方法：验证要公布的数字是否合理。这 是通过将发布表中的数量与先前发布的相同数量进行比较来实现。如果观察到异常 值，则对导致可疑数量的各个记录和字段应用宏编辑程序。 •分布方法 可用数据用于表征变量的分布。然后将所有单个值与分布进行比较。包含可能被视 为不常见的值（给定分布）的记录是进一步检查和可能编辑的候选者。
参考译文
4.1典型用途 数据转换通常应用于数据集内的不同实体（例如，字段、行、列、数据值 等），并且可以包括诸如提取、解析、加入、标准化、扩充、清理、合并 和过滤操作。期望整理后的数据可供下游使用。 接收整理结果数据的可以是个人，例如将进一步调查数据的数据架构师或 数据科学家、将直接在报告中使用数据的业务用户或者进一步处理数据并 将其写入目标（如数据仓库、数据湖或下游应用程序）的系统。
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个性化互动 购物体验 数据井 网络罪犯 只是…的问题 设立，安上 留神，谨防，提防 风险管理，风险管控 在许多方面 安全威胁
Phrases
dynamic data static data storage medium computational security access control method granular access control mandatory access control security flaw keep in mind
大数据专业英语教程
Unit 12
Data Security
Contents
New Words Abbreviations
Phrases 参考译文
New WoLeabharlann dssecurity[sɪˈkjʊərɪtɪ]
option cater silo personalize
[ˈɒpʃn] [ˈkeɪtə] [ˈsaɪləʊ] [ˈpɜ:sənəlaɪz]
adj.恶意的，存心不良的；预谋的 n.活动 adj.强制的；命令的；受委托的 n.漏洞，弱点 adj.突出的 adj.免疫的；有免疫力的；不受影响的 adj.非凡的；特别的
Phrases
personalize interaction shopping experience silo of data cyber criminal just a matter of throw up be wary of risk management in many way security threat
动态数据 静态数据 存储介质 计算安全性 访问控制方法 粒度访问控制 强制访问控制 安全缺陷 牢记
Listening to Text A
参考译文
数据安全
1.为什么大数据安全如此困难？ 今天收集和存储的数据比以往任何时候都要多，使数据可以解决几乎所有行业的需 求。顾客和客户希望在他们知道需要之前建立完全满足其需求的解决方案和选项。 数据仓库存储个人信息，允许公司和企业为每人提供个性化交互和购物体验。但是， 因为获得的数据量巨大，所以保护个人信息的难度很大。正如公司在大数据收集和 分析方面更加智能和不断创新一样，黑客也变得更加聪明，并且他们也不断创新攻 击敏感且昂贵信息的方法。 来自Target到Home Depot和JPMorgan Chase的消息表明，大名鼎鼎的公司受到了 黑客的攻击，但这并不意味着那些持有您个人信息的小公司不容易受影响。实际上， 它们有时更易受害，因为它们通常没有预算来投资一流的集成安全解决方案。公司 存储的这些数据井是网络犯罪分子的金矿。收集和存储大数据的公司的数据泄露正 变得越来越普遍，并且不会很快消失。
anonymity concern
[ˌænəˈnɪmɪtɪ] [kənˈsɜ:n]
mask
[mɑ:sk]
n.口令；密码
n.电子邮件 vt.给…发电子邮件 adv.让人担忧地 n.缺口；分歧 adj.(职位) 空缺的 n.缺乏，不足；缺点，缺陷 adj.低劣的；贫乏的；匮乏的
n.路障；障碍 vi.设置路障 n.匿名；作者不详；匿名者；无名者
n.顾虑；关心；关系，有关 vt. 使关心，使担忧；涉及，关系到 vt.掩盖，掩饰 vi.隐瞒，掩饰
New Words
aggregate handle
[ˈægrɪgeɪt] [ˈhændl]
tactics versatility weakness accumulation acknowledge threat aforementioned control patch log
['tæktɪks] [ˌvɜ:sə'tɪlɪtɪ] [ˈwi:knɪs] [əˌkju:mjʊ'leɪʃn] [əkˈnɒlɪdʒ] [θret] [əˌfɔ:ˈmenʃənd] [kənˈtrəʊl [pætʃ] [lɒg]
vt.使聚集，使积聚 vi.处理；操作，操控
n.手柄；句柄 n.战术；策略，手段 n.易变；多用途 n.弱点，缺点 n.积累；累积量；堆积物 vt.承认 n.威胁 adj.上述的，前述的 vt.控制；管理 n.补丁，补片 n.记录；日志
malicious activity mandatory vulnerability prominent immune extraordinary
[məˈlɪʃəs] [ækˈtɪvɪtɪ] [ˈmændətərɪ] [ˌvʌlnərə'bɪlɪtɪ] [ˈprɒmɪnənt] [ɪˈmju:n] [ɪkˈstrɔ:dɪnərɪ]
reap sensitive susceptible prey goldmine cyber firewall
[ri:p] [ˈsensɪtɪv] [səˈseptɪbl] [preɪ] ['gəʊldmaɪn] ['saɪbə] [ˈfaɪəwɔ:l]
n.安全；保护，防护 adj.安全的，保密的 n.选项，选择权 vt.满足需要，适合 n.井；筒仓；地下贮藏库 vt.个性化，使（某事物）针对个人或带有 个人感情 v.收获，获得 adj.敏感的；易受影响的 adj.易受影响的；易受感染的 n.受害者，受骗者 n.金矿；金山；财源；宝库 adj.计算机（网络）的，信息技术的
v.自动分级
n.验证，确认 n.过滤；筛选 adj.可信的，可靠的；认证了的 adj.合法的，合理的；正规的
n.预防；阻止，制止 n.映射器；映射程序 adj.智能的；聪明的；有智力的 adj.易受攻击的 n.来源，起源，出处 n.身份验证；认证；证明，鉴定 v.辨认，识别，承认
New Words
New Words
auto-tiering validation filtration authentic legitimate prevention mapper intelligent vulnerable provenance authentication recognize
[ˈɔ:təʊ-'taɪəɪŋ] [ˌvælɪ'deɪʃn] [fɪlˈtreɪʃn] [ɔ:ˈθentɪk] [lɪˈdʒɪtɪmɪt] [prɪˈvenʃn] ['mæpə] [ɪnˈtelɪdʒənt] [ˈvʌlnərəbl] [ˈprɒvənəns] [ɔ:ˌθentɪ'keɪʃn] [ˈrekəgnaɪz]
n.防火墙 vt.用作防火墙
New Words
password email
[ˈpɑ:swɜ:d] ['i:meɪl]
alarmingly gap unfilled deficiency poor roadblock
[ə'lɑːmɪŋlɪ] [gæp] [ˌʌn'fɪld] [dɪˈfɪʃnsɪ] [pʊə] [ˈrəʊdblɒk]