大数据英文版PPT模板
大数据相关的PPT模板
大数据定义
高增长率
多样化
来适应海量、高增长率和多样 化的信息资产。
大数据是“未来的新石油”
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。 大数据就是“未来的新石 油”。
何谓大？
（数据度量）
1Byte = 8 Bit 1 KB = 1,024 Bytes = 8192 bit 1 MB = 1,024 KB = 1,048,576 Bytes 1 GB = 1,024 MB = 1,048,576 KB 1 TB = 1,024 GB = 1,048,576 MB 1 PB = 1,024 TB = 1,048,576 GB 1 EB = 1,024 PB = 1,048,576 TB 1 ZB = 1,024 EB = 1,048,576 PB 1 YB = 1,024 ZB = 1,048,576 EB 1 BB = 1,024 YB = 1,048,576 ZB 1 NB = 1,024 BB = 1,048,576 YB 1 DB = 1,024 NB = 1,048,576 BB
斯诺登的爆料引起一片哗然，根据他提供的资料，被卷入“棱镜门”事件的公司包括微 软、雅虎、谷歌、苹果、Facebook等9大IT业巨头。在“棱镜门”事件开始发酵之后， 这些公司先是赶紧出面否认与美国政府的监视项目进行过合作，并相继发表声明，呼吁 政府采取更透明态度，以证明他们的“清白”。
大数据的三个层面
特征 价值 现在 大数据 定义 探讨 和未来 隐私
1
2
分布式处理平台 感知技术
云计算
存储技术
3
互联网的 政府的 企业的 个人的 大数据 大数据 大数据 大数据
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
大数据英文版
大数据英文版Big Data: An IntroductionIntroduction:In the era of technology and information, data has become one of the most valuable assets for organizations across various industries. With the exponential growth of data, the need to analyze and extract meaningful insights from it has led to the emergence of Big Data. This article aims to provide a comprehensive introduction to Big Data, its characteristics, and its significance in today's world.Definition and Characteristics of Big Data:Big Data refers to extremely large and complex datasets that cannot be effectively managed, processed, and analyzed using traditional data processing methods. It is characterized by the three Vs: Volume, Velocity, and Variety.1. Volume: Big Data is characterized by a massive volume of data generated from various sources such as social media, sensors, online transactions, and more. The sheer size of data poses challenges in terms of storage, processing, and analysis.2. Velocity: Big Data is generated at an unprecedented speed. With the advent of real-time data sources, such as Internet of Things (IoT) devices, data is generated continuously and needs to be processed and analyzed in real-time to derive timely insights.3. Variety: Big Data encompasses a wide variety of data types, including structured, semi-structured, and unstructured data. It includes text, images, videos, social media posts, and more. The diversity of data types poses challenges in terms of data integration and analysis.Significance of Big Data:Big Data has revolutionized the way organizations operate and make data-driven decisions. Its significance can be observed in various domains:1. Business Insights: Big Data analytics enables organizations to gain valuable insights into customer behavior, market trends, and preferences. This information helps businesses to personalize their products and services, improve customer satisfaction, and make informed business decisions.2. Healthcare: Big Data analytics has the potential to revolutionize healthcare by analyzing large volumes of patient data, identifying patterns, and predicting diseases. It can help in early detection, personalized treatment plans, and improving overall healthcare outcomes.3. Fraud Detection: Big Data analytics plays a crucial role in detecting fraudulent activities in various sectors, such as banking, insurance, and e-commerce. By analyzing large volumes of transactional data, anomalies can be identified, and fraud can be prevented.4. Smart Cities: Big Data analytics is instrumental in creating smart cities by analyzing data from various sources, such as sensors, social media, and traffic cameras. It helps in optimizing transportation systems, reducing energy consumption, and improving overall urban living.Challenges of Big Data:While Big Data offers numerous opportunities, it also presents several challenges that need to be addressed:1. Data Privacy and Security: With the increasing volume and variety of data, ensuring data privacy and security becomes a critical concern. Organizations need to implement robust security measures to protect sensitive data from unauthorized access and breaches.2. Data Quality: Big Data often contains noise, errors, and inconsistencies. Ensuring data quality is crucial for accurate analysis and decision-making. Data cleansing and validation processes need to be implemented to maintain data integrity.3. Infrastructure and Scalability: Managing and processing large volumes of data requires robust infrastructure and scalable systems. Organizations need to invest in technologies such as cloud computing and distributed computing frameworks to handle Big Data efficiently.4. Skills Gap: The field of Big Data requires specialized skills such as data analytics, data engineering, and machine learning. The shortage of skilled professionals poses a challenge in effectively utilizing Big Data for business benefits.Conclusion:Big Data has emerged as a game-changer in today's data-driven world. With its massive volume, high velocity, and diverse variety, Big Data presents both opportunities and challenges for organizations. By harnessing the power of Big Data analytics, organizations can gain valuable insights, make informed decisions, and unlock new avenues for growth and innovation. However, addressing the challenges associated with Big Data is crucial to ensure data privacy, quality, and scalability. As organizations continue to embrace Big Data, it is expected to reshape industries and drive the next wave of innovation.。
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.
大数据英语幻灯片
The early years of data revoallenges
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.
fig. New types of research data about human behavior and society pose many opportunities if crucial infrastructural challenges are tackled.
Part 5 conclusion
Part 5 conclusion
Today data require scientific and computational intelligence. Big Data Future is a free, public, multidisciplinary conference on
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;
大数据英文版
大数据英文版Big Data (English Version)Introduction:Big data refers to the large and complex sets of data that cannot be easily managed, processed, or analyzed using traditional data processing techniques. It involves the collection, storage, and analysis of massive amounts of structured and unstructured data from various sources. This English version document aims to provide a comprehensive understanding of big data, its applications, challenges, and future prospects.1. Definition and Characteristics of Big Data:Big data is characterized by the "3Vs": volume, velocity, and variety. It refers to data that is too large to be processed using traditional methods, generated at high speeds, and comes in various formats such as text, images, videos, and sensor data. Additionally, big data possesses the following characteristics:1.1. Volume: Big data involves a massive amount of data that exceeds the processing capacity of conventional databases. It includes data from social media, e-commerce transactions, scientific research, and more.1.2. Velocity: Big data is generated at an unprecedented speed. Real-time data streams, such as social media updates, sensor data, and financial transactions, require immediate processing to extract valuable insights.1.3. Variety: Big data encompasses diverse data types, including structured, semi-structured, and unstructured data. It includes text, numerical data, images, audio, and video files.2. Applications of Big Data:2.1. Business Analytics: Big data enables organizations to gain valuable insights into customer behavior, preferences, and market trends. It helps in making data-driven decisions, improving customer satisfaction, and optimizing business processes.2.2. Healthcare: Big data analytics can be used to analyze patient records, medical imaging, and genomic data. It aids in personalized medicine, disease prediction, and improving healthcare outcomes.2.3. Finance: Big data analytics helps financial institutions in fraud detection, risk assessment, and customer segmentation. It enables real-time monitoring of transactions and enhances security measures.2.4. Transportation: Big data is used to optimize traffic management, predict maintenance needs, and improve public transportation systems. It helps in reducing congestion, enhancing safety, and improving efficiency.2.5. Manufacturing: Big data analytics assists in optimizing production processes, predicting equipment failures, and improving supply chain management. It enables proactive maintenance, reduces downtime, and enhances overall productivity.3. Challenges of Big Data:Despite its numerous advantages, big data also presents several challenges that need to be addressed:3.1. Data Quality: Big data often contains errors, inconsistencies, and missing values. Ensuring data quality is crucial for accurate analysis and decision-making.3.2. Privacy and Security: Handling large volumes of sensitive data raises concerns about privacy and security. Safeguarding data from unauthorized access and ensuring compliance with regulations is essential.3.3. Scalability: Big data requires scalable infrastructure and technologies to handle the ever-increasing volume and velocity of data. Scaling up existing systems can be complex and costly.3.4. Data Integration: Combining and integrating data from various sources with different formats and structures can be challenging. Data integration techniques and tools need to be employed for effective analysis.4. Future Prospects:The future of big data holds immense potential for advancements in various fields. Some of the key areas of development include:4.1. Artificial Intelligence (AI): Big data fuels the development of AI algorithms and machine learning models. AI, in turn, enhances big data analytics capabilities, enabling better predictions and insights.4.2. Internet of Things (IoT): The proliferation of IoT devices generates vast amounts of data. Big data analytics helps in extracting meaningful information from IoT-generated data for improved decision-making.4.3. Cloud Computing: The scalability and flexibility of cloud computing make it an ideal platform for big data processing and storage. The integration of big data and cloud computing enables cost-effective and efficient data analysis.4.4. Data Governance: As big data continues to grow, the need for effective data governance becomes crucial. Establishing policies, procedures, and frameworks for data management and privacy protection will be a priority.Conclusion:Big data has revolutionized the way organizations analyze and leverage data for decision-making. Its applications span across various sectors, including business, healthcare, finance, transportation, and manufacturing. However, challenges related to data quality, privacy, scalability, and integration need to be addressed. The future prospects of big data are promising, with advancements in AI, IoT, cloud computing, and data governance. Embracing big data and harnessing its potential will undoubtedly drive innovation and transform industries globally.。
互联网大数据 PPT模板
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基本工作概述报告
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02 04
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PART 04
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基本工作概述报告
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“人工”
“人工”比较好理解，争议性也不大。有时我们会要考虑什么是人力所 能及制造的，或者人自身的智能程度有没有高到可以创造人工智能的地 步，等等。但总的来说，“人工系统”就是通常意义下的人工系统。
关于什么是“智能”，就问题多多了。这涉及到其它诸如意识（CONSCIOUSNESS）、自我 （SELF）、思维（MIND）（包括无意识的思维（UNCONSCIOUS_MIND））等等问题。人 唯一了解的智能是பைடு நூலகம்本身的智能，这是普遍认同的观点。
洞察发现力和流程优化能 力。
大数据定义
高增长率
多样化
来适应海量、高增长率和多样 化的信息资产。
大数据是“未来的新石油”
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。 大数据就是“未来的新石 油”。
何谓大？
（数据度量）
1Byte = 8 Bit 1 KB = 1,024 Bytes = 8192 bit 1 MB = 1,024 KB = 1,048,576 Bytes 1 GB = 1,024 MB = 1,048,576 KB 1 TB = 1,024 GB = 1,048,576 MB 1 PB = 1,024 TB = 1,048,576 GB 1 EB = 1,024 PB = 1,048,576 TB 1 ZB = 1,024 EB = 1,048,576 PB 1 YB = 1,024 ZB = 1,048,576 EB 1 BB = 1,024 YB = 1,048,576 ZB 1 NB = 1,024 BB = 1,048,576 YB 1 DB = 1,024 NB = 1,048,576 BB
机遇和挑战
机遇
大数据技术促进国家和社会发展大数据蓝海成为 企业竞争的新焦点大数据时代呼唤创新型人才
挑战
大数据技术的运用仍有困难大数据给信息安全带 来新挑战
机遇1：大数据技术促进国家和社会发展
实现科学发展 做出科学决策
当前，我国正处在全面建成小康社会征程 中，工业化、信息化、城镇化、农业现代 化任务很重，建设下一代信息基础设施， 发展现代信息技术产业体系，健全信息安 全保障体系，推进信息网络技术广泛运 用，是实现四化同步发展的保证。大数据 分析对我们深刻领会世情和国情，把握规 律，实现科学发展，做出科学决策具有重 要意义，我们必须重新认识数据的重要价 值。
“智能”
大数据带 来的变革
1
更多 不是随机样本
而是全部数据
2
更好 不是因果关系
而是相关关系
3
更杂 不是精确性
而是混杂性
02
结构特征
Remember what should be remembered, and forget what should be forgotten.Remember what should be remembered, and forget what should be forgotten.
大数据的特征
❖ 容量（Volume）
数据的大小决定所考虑的数
E
据的价值和潜在的信息
❖ 种类（Variety）
数据类型的多样性
❖ 速度（Velocity）
A 40
32
指获得数据的速度
30
20
10 15
0
❖ 价值（value）
32 B
合理运用大数据，以低成本
创造高价值
12
28
D
C
❖ 真实性（Veracity）
大数据的三个层面
特征 价值 现在 大数据 定义 探讨 和未来 隐私
1
2
分布式处理平台 感知技术
云计算
存储技术
3
互联网的 政府的 企业的 个人的 大数据 大数据 大数据 大数据
理论
THEORY
技术
TECHNOLOGY
实践
UTILIZATION
03
机遇挑战
Remember what should be remembered, and forget what should be forgotten.Remember what should be remembered, and forget what should be forgotten.
Cloud Computing / Big Data / PPT Templates
汇报人：
目录
01 大数据是什么
02 03 特征和构成
机遇和挑战
04 发展趋势
05 应用案例
01
大数据是什么
Remember what should be remembered, and forget what should be forgotten.Remember what should be remembered, and forget what should be forgotten.
数据的质量
大数据的结构
结构化
非结构化
半结构化
大数据包括结构化、半结构化和非结构化数据， 非结构化数据越来越成为数据的主要部分。 据IDC的调查，报告显示： 企业中80%的数据都是非结构化数据，这些数据每年都按指数增 长60%。大数据就是互联网发展到现今阶段的一种表象或特征而 已，没有必要神话它或对它保持敬畏之心， 在以云计算为代表的技术创新大幕的衬托下，这些原本看起来很 难收集和使用的数据开始容易被利用起来了，通过各行各业的不 断创新，大数据会逐步为人类创造更多的价值。
大数据（BIG DATA）
指无法在一定时间范围内用常规软件工具进行捕 捉、管理和处理的数据集合，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。
对于“大数据”（Big data） 研究机构Gartner给出了这样
的定义。
海量
“大数据”是需要新处理模 式才能具有更强的决策力、
机遇2：大数据蓝海成为企业竞争的新焦点
“棱镜门”引爆大数据时代争议
❖ 事情的起因是美国中情局前职员斯诺登向媒体爆料，过去6 年间，美国的情报部门通过一个代号为“棱镜”的项目，从 多家知名互联网公司获取电子邮件、在线聊天内容、照片、 文档、视频等网络私人数据，跟踪用户一举一动。他说，自 己只需要坐在办公桌前，动动指头，敲敲键盘，就能了解很 多人的私密信息。