A Brief Introduction of Big Data 大数据PPT
Big_Data_大数据的介绍(全英)
Why ‘Big Data’ is a big Deal
Big data differs from traditional information in mind-bending ways: Not knowing why but only what The challenge with leadership is that it’s very driven by gut instinct in most cases Air travelers can now figure out which flights are likeliest to be on time, thanks to data scientists who tracked a decade of flight history correlated with weather patterns Publishers use data from text analysis and social networks to give readers personalized news. health care is one of the biggest opportunities, If we had electronic records of Americans going back generations, we'd know more about genetic propensities, correlations among symptoms, and how to individualize treatments.
介绍大数据的小英语作文
介绍大数据的小英语作文Big Data: The Fuel of the Modern Economy.In the contemporary era marked by rapid technological advancements, the concept of "big data" has emerged as a pivotal force shaping the global economy and society. Big data refers to the immense volume of structured and unstructured data generated from various sources, including social media platforms, e-commerce transactions, Internet of Things (IoT) devices, and scientific research.The sheer scale and complexity of big data pose significant challenges for traditional data management systems. However, advances in computing power and distributed storage technologies have paved the way for the effective capture, storage, and analysis of these vast data sets.Characteristics of Big Data.Big data is characterized by its unique attributes known as the "four Vs":Volume: Big data encompasses massive amounts of data, measured in terabytes, petabytes, or even exabytes.Variety: It includes data from diverse sources and formats, such as text, images, videos, audio, and sensor readings.Velocity: Big data is generated and processed at an unprecedented speed, requiring real-time or near-real-time analysis.Veracity: The quality and accuracy of big data can vary significantly, necessitating data cleaning and verification processes.Benefits of Big Data.Harnessing the power of big data offers numerous benefits across various domains:Improved Decision-Making: Big data provides businesses and organizations with valuable insights into customer behavior, industry trends, and operational efficiency. By analyzing large data sets, they can make informed decisions based on data-driven evidence.Personalized Experiences: Big data enables tailored products, services, and marketing campaigns by identifying individual preferences and behaviors. This personalization enhances customer satisfaction and loyalty.Operational Optimization: Industries such as manufacturing, transportation, and healthcare leverage big data to optimize operations, reduce costs, and improve productivity.Scientific Discovery: Big data plays a crucial role in scientific research, facilitating the analysis of complex phenomena and unlocking new knowledge in fields such as genomics, climate science, and astrophysics.Social Good: Big data has the potential to address societal challenges, such as improving healthcare outcomes, promoting education, and reducing crime.Challenges of Big Data.While big data offers immense benefits, it also presents challenges that must be addressed:Data Security and Privacy: The vast amount ofsensitive data collected and stored poses risks of data breaches and misuse, which require robust security measures and ethical considerations.Data Management and Analysis: The scale and complexity of big data require specialized tools and skills for efficient data management, analysis, and visualization.Data Governance: Organizations need to establish data governance frameworks to ensure data quality, consistency, and accessibility while mitigating risks.Ethical Implications: The use of big data raises ethical concerns related to privacy, discrimination, and the potential for manipulative practices.Conclusion.Big data is transforming the way we live, work, and interact with the world. By leveraging the vast amounts of data generated in the digital age, organizations and individuals can gain unprecedented insights, optimize operations, and drive innovation. However, it is crucial to address the challenges associated with big data while ensuring ethical and responsible data management practices to harness its full potential for the benefit of society.。
最新Big-Data-大数据介绍(全英)ppt课件
Why ‘Big Data’ is a big Deal
Big data differs from traditional information in mind-bending ways: Not knowing why but only what The challenge with leadership is that it’s very driven by gut instinct in most cases Air travelers can now figure out which flights are likeliest to be on time, thanks to data scientists who tracked a decade of flight history correlated with weather patterns Publishers use data from text analysis and social networks to give readers personalized news. health care is one of the biggest opportunities, If we had electronic records of Americans going back generations, we'd know more about genetic propensities, correlations among symptoms, and how to individualize treatments.
Main steps in adopting an analytical system
大数据BigData培训课件(PPT 101页)
MapReduce 技术框架
• 分布式文件系统 • 并行编程模型 • 并行执行引擎
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分布式文件系统
(Google file system)
• 分布式文件系统运行于大规模集群之上,集 群使用廉价的机器构建.
• 数据采用键/值对(key/value)模式进行存储.
• 整个文件系统采用元数据集中管理、数据 块分散存储的模式,通过数据的复制(每份数 据至少3 个备份)实现高度容错.
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大数据时代
大规模数据主要来源2: 网站点击流数据
为了进行有效的市场营销和推广,用户在网 上的每个点击及其时间都被记录下来;利用 这些数据,服务提供商可以对用户存取模式 进行仔细的分析,从而提供更加具有针对性 的服务
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大数据时代
大规模数据主要来源3: 移动设备数据
通过移动电子设备包括移动电话和PDA、 导航设备等,我们可以获得设备和人员的位 置、移动、用户行为等信息,对这些信息进 行及时的分析,可以帮助我们进行有效的决 策,比如交通监控和疏导系统
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时间序列分析
– 比如在金融服务行业,分析人员可以开发针对性 的分析软件,对时间序列数据进行分析,寻找有 利可图的交易模式(profitable trading pattern), 经过进一步验证之后,操作人员可以使用这些交 易模式进行实际的交易,获得利润
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大规模图分析和网络分析
• 社会网络虚拟环境本质上是对实体连接性 的描述.在社会网络中,每个独立的实体表示 为图中的一个节点,实体之间的联系表示为 一条边.
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MapReduce应用领域的扩展
• 若干开发者发起了Apache Mahout 项目的 研究,该项目是基于Hadoop 平台的大规模 数据集上的机器学习和数据挖掘开源程序 库,为应用开发者提供了丰富的数据分析功 能
BIG DATA 大数据 英文演讲ppt
becoming an important production factor.
Big data: Taobao transaction volume
Fourth: The industrial Internet will drive big data to the ground. Big data is a focus of industrial Internet development, big data can land in traditional industries, Related to the development process of industrial Internet, so in the industrial Internet stage, big data will gradually land, but also will inevitably land.
Gather Data
AnGaatlhyezre DDaattaa
EAT
SPICY
HCHOINTESPEDORDIRNPINKK
RESTAURANT
Driving route planning
Discount push
speech recognition
search
Interest analysis
out remote diagnosis and treatment .It will help improve the relationship between doctors and patients and alleviate the problem of insufficient quality medical resources.
[课件]BigData数据大爆炸PPT
![[课件]BigData数据大爆炸PPT](https://img.taocdn.com/s3/m/fcd8152df12d2af90242e64f.png)
对于“大数据”（Big data）研究机构Gartner给出了这样的定义。“大数据 ”是需要新处理模式才能具有更强的决策力、洞察发现力和流程优化能 力的海量、高增长率和多样化的信息资产。 大数据”这个术语最早期的引用可追溯到apache org的开源项目Nutch。当时 ，大数据用来描述为更新网络搜索索引需要同时进行批量处理或分析的 大量数据集。随着谷歌MapReduce和GoogleFile System （GFS）的发布， 大数据不再仅用来描述大量的数据，还涵盖了处理数据的速度。
我们应该如何利用大数据？ 大数据包含几个方面的内涵 1. 数据量大，TB，PB，乃至EB等数据量的数据需要分析处理。 2. 要求快速响应，市场变化快，要求能及时快速的响应变化，那对数据的分析 也要快速，在性能上有更高要求，所以数据量显得对速度要求有些“大”。 3. 数据多样性：不同的数据源，非结构化数据越来越多，需要进行清洗，整理， 筛选等操作，变为结构数据。 4. 价值密度低，由于数据采集的不及时，数据样本不全面，数据可能不连续等 等，数据可能会失真，但当数据量达到一定规模，可以通过更多的数据达到 更真实全面的反馈。 很多行业都会有大数据需求，譬如电信行业，互联网行业等等容易产生大量数 据的行业，很多传统行业，譬如医药，教育，采矿，电力等等任何行业，都 会有大数据需求。
从某种程度上说，大数据是数据分析的前沿技术。简言之，从各种各样类型 的数据中，快速获得有价值信息的能力，就是大数据技术。明白这一点 至关重要，也正是这一点促使该技术具备走向众多企业的潜力。 大数据可分成大数据技术、大数据工程、大数据科学和大数据应用等领域。 目前人们谈论最多的是大数据技术和大数据应用。工程和科学问题尚未 被重视。大数据工程指大数据的规划建设运营管理的系统工程；大数据 科学关注大数据网络发展和运营过程中发现和验证大数据的规律及其与 自然和社会活动之间的关系。
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.。
大数据的介绍PPT课件
所谓大数据，是一个综合性概念，它包括： （1）因具备3V特征而难以进行管理的数据 （2）对这些数据进行存储、处理、分析的技术 （3）以及能够通过分析这些数据获得实用意义和观点的人才和组织
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麻省理工与通货紧缩预测软件
美国劳工统计局的人员每个月都要公布消费物价指数（CPI），这是用来测试通货膨 胀率的。
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VISA＆MasterCard与商户推荐
像VISA和MasterCard这样的信用卡发行商，它们能够从自己的服务网获取更多的 交易信息和顾客的消费信息
它们的商业模式从单纯的处理支付行为转变成了收集数据
一个称为MasterCard Advisors的部门收集和分析了来自210个国家的15亿信用卡 用户的650亿条交易记录，用来预测商业发展和客户的消费趋势。然后，它把这些分 析结果卖给其他公司
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大数据的典型特征（3V）
Volume（容量） 现在基本上是指从几十TB到几PB这样的数量级，未来，可能只有几EB数量级的数
据量才能称得上是大数据了。(1T=1024G,1P=1024T) Variety（多样性）
结构化和非结构化数据 Velocity（速度）
数据产生和更新的频率
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广义的大数据
如数据代理益百利旗下的网页流量测量公司Hitwise，让客户采集搜索流量来揭示消 费者的喜好。
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物联网
物联网（Internet of Things，缩写IOT）是一个基于互联网、传统电信网等信息承载 体，让所有能够被独立寻址的普通物理对象实现互联互通的网络。
在物联网上，每个人都可以应用电子标签将真实的物体上网联结，在物联网上都可 以查找出它们的具体位置。
疾控中心得到流感方面的信息往往会有一两周的滞后，这种滞后导致公共卫生机构 在疫情爆发的关键时期反而无所适从。