《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。
《大数据专业英语》课件—06Database Basic Concept
结构化英语查询语言 SQL 访问组
Listening to Text A
参考译文
数据库基本概念
1. 数据、数据库和数据库管理系统 在计算机科学中，适合电脑使用的任何形式的东西都是数据。数据通常与程序不同。 程序是一组指令，详细地描述了计算机要执行的任务。在这个意义上说，不是程序 代码的东西都是数据。 数据库是所组织的信息的集合，这样可以很容易地访问、管理和更新这些信息。有 一种观点认为，数据库可以根据其内容分为以下几类：概要、文字、数字和图像。 在计算中，有时也根据数据库的组织方法对其分类。最普遍的组织方法是关系数据 库——表式数据库，在这个数据库中定义数据以便可以用不同的方式进行重组和访 问。分布式数据库分散在网络中的不同位置，可以有多个副本。一个面向对象编程 数据库与对象类和子类中定义的数据相一致。
[ˈækses] [ˌbɪblɪə'ɡræfɪk] [fʊl-tekst] [əˈprəʊtʃ] [riˈɔ:gənaɪz] [dɪˈspɜ:sl] [ˈreplɪkeɪt] [ˈkɒŋgrʊənt] ['sʌbklɑ:s] [ˈəʊvəsaɪt] [ˌri:əˈsembl] [ɪkˈstend] [ˈmɒdɪfaɪ]
大数据专业英语教程
Unit 6
Database Basic Concept
Contents
New Words Abbreviations
Phrases 参考译文
New Words
access bibliographic full-text approach reorganize dispersal replicate congruent subclass oversight reassemble extend modify
《大数据专业英语》课件—09Data Mining
[əˈsembl] [ˌekspləˈreɪʃn] [skæn] [prɪˈskraɪb]
vt.（用示例、图画等）说明；给…加插 图 vt.引发，触发 n.需求，要求
adj.初步的，初级的；预备的；开端的 n.准备工作；初步措施
n.计划，打算 v.规划，计划，打算 v.集合，收集
n.探测；搜索，研究 v.审视 vt.指定，规定 vi.建立规定，法律或指示
obtain solicitation exclude
[əbˈteɪn] [ˌsəlɪsɪ'teɪʃn] [ɪkˈsklu:d]
vt.构建，建造；构成；创立 n.电子表格 n.关系；联系 vt.隐藏，隐匿 adj.凭经验的；以观察或实验为依据的 adj.可识别的；可辨别的 n.行动，活动；功能，作用；手段 n.行为；态度 n.解决方案，答案 vt.构想出，规划；确切地阐述；用公式 表示
参考译文
1.7数据挖掘和数据仓库 无论数据是存储在平面文件、电子表格、数据库表还是一些其它存储格式中，都可 以挖掘数据。数据的重要标准不是存储格式，而是它对要解决的问题的适用性。 正确的数据清理和准备对于数据挖掘非常重要，数据仓库可以促进这些活动。但是， 如果数据仓库不包含解决问题所需的数据，则它将毫无用处。 Oracle Data Mining要求将数据显示为单记录格式的案例表。每个记录（案例）的所 有数据必须包含在一行中。最典型的情况是，案例表是一个视图，用挖掘所需的格 式显示数据。
correctness hypothesis sample
[kə'rektnɪs] [haɪˈpɒθɪsɪs] [ˈsɑ:mpl]
summarization inductive inference conclusion cube
《大数据专业英语》课件—01What Is Big Data
参考译文
最近的技术极大地降低了数据存储和计算的成本，使存储更多的数据比以往更容 易、成本更低。随着现在更便宜、更易于访问的大数据量的增加，你可以做出更 精准的业务决策。 在大数据中寻找价值不仅仅是分析它。这是一个完整的发现过程，需要富有洞察 力的分析师、业务用户和高管，他们会提出正确的问题、识别模式，做出切合实 际的假设并预测行为。
format engine on-demand gradually popularity clarity explore discover
[ˈfɔrmæt] [ˈɛndʒɪn] [ɒn-dɪˈmɑ:nd] [ˈɡrædʒʊəlɪ] [ˌpɒpjuˈlærɪtɪ] [ˈklærɪtɪ] [ɪkˈsplɔ:] [dɪsˈkʌvə]
2.大数据的三V 2.1大量 数据量很重要。对于大数据，必须处理大量低密度、非结构化的数据。这可以是未 知价值的数据，例如Twitter反馈的数据，网页或移动应用上的点击流，或来自有效 传感器设备的数据。这可能是的数十TB的数据，而对其它组织，数据甚至可以达到 数百PB的量级。
参考译文
2.2高速 高速是接收数据并可能以此采取行动的速率很快。一些支持互联网的智能产品实 时或接近实时运行，需要实时评估和行动。
vt.递送，交付 vi.投递，传送 vt.实施，执行；使生效，实现 n.工具，器械；手段 adj.无尽的，无边的 n.可能，可能性 vt.许诺；给人以…的指望或希望；保证 vi.许诺；有指望，有前途 n.许诺；希望，指望 n.科技（总称），技术 adj.有价值的，可评估的 n.策略，战略
New Words
New Words
storage compute
[ˈstɔrɪdʒ] [kəmˈpju:t]
大数据英文版
大数据英文版Big Data: Unlocking the Power of DataIntroduction:In today's digital age, the amount of data generated is growing exponentially. This vast amount of data, known as big data, holds immense potential for businesses and organizations across various industries. Harnessing the power of big data can provide valuable insights, drive innovation, and improve decision-making processes. In this text, we will explore the concept of big data, its benefits, challenges, and its impact on various sectors.Definition of 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, machines, and transactional systems. It encompasses three main characteristics known as the three Vs: volume, velocity, and variety. Volume refers to the large amount of data generated, velocity represents the speed at which data is generated and processed, and variety refers to the different types and formats of data.Benefits of Big Data:1. Improved Decision-Making: Big data analytics enables organizations to analyze vast amounts of data in real-time, providing valuable insights that can drive informed decision-making. By identifying patterns, trends, and correlations, businesses can make data-driven decisions that lead to improved efficiency and competitiveness.2. Enhanced Customer Experience: Big data analytics allows organizations to gain a deeper understanding of their customers by analyzing customer behavior, preferences, and feedback. This enables businesses to personalize their offerings, improve customer service, and deliver a seamless customer experience.3. Increased Operational Efficiency: Big data analytics can optimize operational processes by identifying bottlenecks, inefficiencies, and areas for improvement. By analyzing large datasets, organizations can streamline operations, reduce costs, and enhance productivity.4. Innovation and New Product Development: Big data provides valuable insights into market trends, customer needs, and emerging technologies. This information can fuel innovation and drive the development of new products and services that meet the evolving demands of customers.Challenges of Big Data:While big data offers numerous benefits, it also presents several challenges that organizations must overcome to fully leverage its potential:1. Data Security and Privacy: With the proliferation of data, ensuring the security and privacy of sensitive information becomes crucial. Organizations need to implement robust security measures and comply with data protection regulations to safeguard data from unauthorized access and breaches.2. Data Quality and Integration: Big data often comes from various sources and in different formats, making data quality and integration a significant challenge. Data cleansing, standardization, and integration processes are essential to ensure accurate and reliable insights.3. Scalability and Infrastructure: Handling and processing large volumes of data requires scalable infrastructure and advanced technologies. Organizations need to invest in suitable hardware, software, and IT infrastructure to manage and analyze big data effectively.4. Skills and Expertise: The field of big data analytics requires specialized skills and expertise. Organizations need to hire and train professionals who possess the necessary knowledge in data science, statistics, programming, and machine learning to extract meaningful insights from big data.Impact of Big Data across Industries:1. Healthcare: Big data analytics is revolutionizing the healthcare industry by enabling predictive analytics, personalized medicine, and improved patient outcomes. By analyzing patient data, medical records, and clinical research, healthcare providers can identify patterns, predict disease outbreaks, and develop targeted treatment plans.2. Retail: Big data analytics helps retailers understand customer behavior, preferences, and buying patterns. This information allows retailers to optimize inventory management, personalize marketing campaigns, and enhance the overall shopping experience.3. Finance: Big data is transforming the financial sector by enabling fraud detection, risk assessment, and algorithmic trading. By analyzing vast amounts of financial data, organizations can identify fraudulent activities, assess creditworthiness, and make data-driven investment decisions.4. Manufacturing: Big data analytics is enhancing manufacturing processes by optimizing supply chain management, improving production efficiency, and reducing downtime. By analyzing sensor data, machine logs, and customer feedback, manufacturers can identify areas for improvement and implement proactive maintenance strategies.Conclusion:Big data has emerged as a game-changer in today's data-driven world. By harnessing the power of big data analytics, organizations can unlock valuable insights, drive innovation, and gain a competitive edge. However, it is essential to address the challenges associated with big data, such as data security, quality, scalability, and skills. As big data continues to evolve, its impact across industries will only grow, transforming the way organizations operate and make decisions.。
《大数据专业英语》课件—02Data Model
参考译文
数据模型的主要目的是通过提供数据的定义和格式来支持信息系统的开发。 数据模型明确地确定数据的结构。数据模型的典型应用包括数据库模型、信息系 统设计和数据交换。通常，数据模型以数据建模语言定义。
2.数据模型的三个视角 在1975年，ANSI确定数据模型实例可以是以下三种类型之一（见图2-1）： •概念数据模型：它描述了一个域的语义，即模型的范围。例如，它可以是组织或 行业感兴趣领域的模型。它由实体类组成，表示域中重要的各类事物，以及实体 类对之间关联的关系断言。概念模式指定了可以使用模型表达的事实或命题的种 类。从这个意义上讲，它定义了一个人工“语言”中允许的表达式，其范围受到 模型范围的限制。 •逻辑数据模型：它描述了语义，由特定的数据操作技术表示。这包括表和列的描 述、面向对象的类和XML标记等。 •物理数据模型：它描述了存储数据的物理方法。这涉及分区、CPU、表空间及类 似的东西。
[baɪnd] [ˈæ rəʊ] [ɪ kˈstenʃn] [ˈnəʊtəbl] [kɑ:dɪ'næ lɪ tɪ] [rəʊˈbʌst]
n.继承，遗传 n.图表；示意图 vt.用图表示；图解 adj.图画的，绘画的 n.记号，标记法 n.（计算机）文档 vt.证明；记录；为…提供证明 vt.绑定；约束；捆绑 n.箭头记号 n.伸展，扩大，延长 adj.值得注意的；显著的 n.基数 adj.健壮的，强健的，结实的
涉及到... ...；与... ...相关 与... ...一致 执行，进行 被转换为 数据库模型 平面模型 表模型 层次模型，分层模型 网络模型 树状结构 基于... ... 一阶谓词逻辑 有限集
Phrases
mathematical foundation object-relational model attribute free star schema data warehouse fact table dimension table entity-relationship model differ from semantic data model physical data model software engineering geographic data model geographic information system generic data model conceptual data model
大数据英语PPT讲义.
Thank you
人有了知识，就会具备各种分析能力， 明辨是非的能力。 所以我们要勤恳读书，广泛阅读， 古人说“书中自有黄金屋。 ”通过阅读科技书籍，我们能丰富知识， 培养逻辑思维能力； 通过阅读文学作品，我们能提高文学鉴赏水平， 培养文学情趣； 通过阅读报刊，我们能增长见识，扩大自己的知识面。 有许多书籍还能培养我们的道德情操， 给我们巨大的精神力量， 鼓舞我们前进。
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 anomalies
opportunities
opportunities
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. Now: available digital data:150 EB(Exabyte)(2005) 1200 EB(2010) Predicted: the stock of digital data is expected to increase 44 times between 2007 and 2020, doubling every 20 months.
大数据英语 ppt课件
Predicted: the stock of digital data is expected to increase 44 times between 2007 and 2020, doubling every 20 months.
intelligence.
Big Data Future is a free, public, multidisciplinary conference on
the possibilities for new enterprises grounded in “big data” to
improve economic, social, and political life.
大数据起初在生物学，生物医学工程，医学，电子开发等领域发展，它 是为了将庞大数量的原始数据转变为 -用于分析的目的“有关数据的数 据”的工具和方法。
ppt课件
15
Part 6 conclusion
ppt课件
16
Part 6 conclusion
Data on today’s scales require scientific and computational
这个趋势在撒哈拉以南尤其令人印象 深刻，这里的移动电话技术已经被用 来作为弱电信和交通基础设施以及欠 发达的银行和金融系统的替代品。
（）：定语，修饰Sub-Saharan Africa （）：介词 （）：并列作用
ppt课件
14
sentences
2、（Initially developed in such fields as computational biology , biomedical engineering, medicine, and electronics, ） Big Data analytics refers to （tools and methodologies） that （ aim to transform massive quantities of raw data into “data about the data”—for analytical purposes）.
大数据英语PPTppt课件
EB(2010)
Predicted: the stock of digital data is expected to increase 44
times between 2007 and 2020, doubling every 20 months.
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 anomalies
Big data
Taobao search
definition
definition
Big data is the need for new processing mode to have a stronger decision-making power, insight into the ability to find and process optimization to adapt to the massive, high growth rate and diversification of information assets.
Characteristics:
Volume : data size Velocity :speed of change Variety : different forms of data sources
大数据专业英语教程课程设计
大数据专业英语教程课程设计课程简介本课程旨在帮助大数据专业的学生提高英语水平，以便更好地理解和应用国际上最新的大数据技术和理论。
本课程将涵盖大数据领域中的常用英语词汇、短语、概念、方法和技术，同时将探讨大数据背后的商业和社会价值。
课程目标•让学生掌握大规模数据分析的理论和实践•提高学生的英语听说读写能力•帮助学生理解大数据应用的商业和社会价值授课方式本课程采用课堂教学、案例分析、小组讨论等多种教学方式。
课堂上将重点侧重于学生的交互式学习和实践技能培养。
课程内容第一章：大数据概述本章将对大数据的定义、历史、来源、挑战和应用等方面进行介绍。
- Key vocabulary: big data, data analytics, data volume, data velocity, data variety - Key concepts: data explosion, data processing, data governance, data security, data privacy第二章：数据挖掘与分析本章将介绍数据挖掘和分析的方法和实践。
- Key vocabulary: data mining, predictive modelling, regression analysis, clustering analysis, decision tree - Key concepts: data visualization, exploratory data analysis, feature engineering, data preprocessing, data cleaning第三章：机器学习和人工智能本章将探讨机器学习和人工智能的基础知识、算法和应用。
- Key vocabulary: machine learning, deep learning, artificial intelligence, neural network, support vector machines - Key concepts: supervised learning, unsupervised learning,transfer learning, natural language processing, computervision第四章：数据管理和存储本章将介绍数据管理和存储的基本原则和技术。
大数据英文版
大数据英文版Title: Big Data: Revolutionizing the Digital LandscapeIntroduction:Big Data has emerged as a transformative force in the digital era, revolutionizing the way organizations operate and make decisions. This comprehensive text explores the concept of Big Data, its significance, and its impact on various sectors. It delves into the challenges and opportunities associated with harnessing and analyzing massive datasets, providing a detailed understanding of the potential applications and benefits of Big Data in the English-speaking world.1. Understanding Big Data:1.1 Definition:Big Data refers to large and complex datasets that cannot be effectively managed, processed, or analyzed using traditional data processing tools and techniques.1.2 Characteristics:- Volume: Big Data is characterized by its sheer volume, typically measured in petabytes or exabytes.- Velocity: Data is generated and collected at an unprecedented speed, requiring real-time or near-real-time analysis.- Variety: Big Data encompasses structured, semi-structured, and unstructured data from diverse sources, including text, images, videos, social media, and sensors.- Veracity: Data quality and reliability are crucial, as Big Data often includes noisy, incomplete, or inconsistent information.- Value: Extracting insights and value from Big Data is the ultimate goal, enabling informed decision-making and driving innovation.2. Importance of Big Data:2.1 Business and Industry:Big Data analytics enables organizations to gain valuable insights into customer behavior, market trends, and competitive intelligence. This information empowers businesses to optimize operations, enhance customer experiences, and develop data-driven strategies for growth.2.2 Healthcare:Big Data plays a pivotal role in improving healthcare outcomes and patient care. Analyzing large healthcare datasets can identify disease patterns, predict epidemics, and optimize treatment plans. It also facilitates personalized medicine, enabling tailored treatments based on individual patient characteristics.2.3 Education:In the education sector, Big Data analytics helps institutions enhance learning experiences, identify at-risk students, and develop personalized learning paths. It enables educators to measure student performance, assess teaching methodologies, and make data-driven decisions to improve educational outcomes.2.4 Government:Governments utilize Big Data to enhance public services, optimize resource allocation, and improve policy-making. Analyzing large datasets can identify patterns, detect fraud, and predict potential risks, enabling proactive decision-making and efficient governance.3. Challenges and Opportunities:3.1 Data Privacy and Security:The collection and storage of vast amounts of personal data raise concerns about privacy and security. Striking a balance between data utilization and protecting individual privacy is crucial to ensure public trust and compliance with regulations.3.2 Data Quality and Integration:Integrating and cleaning heterogeneous datasets from various sources pose significant challenges. Ensuring data quality and accuracy is essential to derive meaningful insights and make informed decisions.3.3 Scalability and Infrastructure:Processing and analyzing Big Data require robust infrastructure capable of handling massive volumes of data. Organizations need to invest in scalable storage, processing power, and advanced analytics tools to effectively manage Big Data.3.4 Skills and Talent Gap:The demand for skilled data scientists and analysts exceeds the current supply, creating a talent gap. Organizations must invest in training and development programs to build a workforce proficient in Big Data analytics.4. Applications of Big Data:4.1 Retail and E-commerce:Big Data analytics enables retailers to understand customer preferences, optimize pricing strategies, and personalize marketing campaigns. It also facilitates inventory management, supply chain optimization, and fraud detection.4.2 Finance:In the financial sector, Big Data analytics helps identify fraudulent activities, assess creditworthiness, and improve risk management. It also enables algorithmic trading, portfolio optimization, and personalized financial advice.4.3 Transportation and Logistics:Big Data analytics enhances transportation systems by optimizing routes, predicting maintenance needs, and improving safety. It enables logistics companies to optimize delivery routes, reduce fuel consumption, and enhance overall operational efficiency.4.4 Social Media and Marketing:Big Data analytics provides valuable insights into consumer sentiment, preferences, and behavior on social media platforms. Marketers leverage this information to develop targeted campaigns, improve brand perception, and enhance customer engagement.Conclusion:Big Data has become an indispensable asset in the digital landscape, offering immense opportunities for organizations across various sectors. By harnessing the power of Big Data analytics, businesses, governments, and institutions can make data-driven decisions, gain a competitive advantage, and drive innovation. Embracing Big Data is essential for organizations aiming to thrive in the rapidly evolving digital world.。
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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]