大数据介绍英文讲义.
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.
大数据英文版

大数据英文版Big Data: Revolutionizing the WorldIntroduction:Big Data has emerged as a game-changer in today's digital era. It refers to the massive volume of structured and unstructured data that organizations accumulate on a daily basis. This data holds immense potential for businesses, governments, and individuals alike. In this text, we will explore the significance of big data, its applications, challenges, and the future it holds.Significance of Big Data:1. Improved Decision Making:Big data analytics enables organizations to analyze vast amounts of data to gain valuable insights. This helps in making informed decisions and developing effective strategies.2. Enhanced Customer Experience:By analyzing customer data, businesses can personalize their offerings, provide targeted marketing campaigns, and improve customer satisfaction.3. Cost Reduction:Big data analytics helps identify inefficiencies and optimize processes, resulting in cost savings for organizations.Applications of Big Data:1. Healthcare:Big data analytics can revolutionize healthcare by analyzing patient data to identify patterns, predict disease outbreaks, and improve treatment outcomes.2. Retail:Big data enables retailers to analyze customer behavior, predict trends, optimize inventory, and offer personalized recommendations, enhancing the overall shopping experience.3. Finance:Financial institutions can use big data analytics to detect fraudulent activities, assess risks, and make real-time trading decisions.Challenges in Big Data:1. Data Privacy and Security:With the abundance of data, ensuring privacy and security is a major concern. Organizations must implement robust security measures to protect sensitive information.2. Data Quality:Big data often consists of unstructured and incomplete data. Ensuring data accuracy and quality is crucial for reliable analysis.3. Scalability:As the volume of data continues to grow exponentially, organizations face challenges in storing, processing, and analyzing this data efficiently.The Future of Big Data:1. Artificial Intelligence (AI) Integration:AI technologies, such as machine learning and natural language processing, will play a crucial role in analyzing big data and extracting valuable insights.2. Internet of Things (IoT):The increasing number of connected devices will generate massive amounts of data. Big data analytics will be essential in harnessing the potential of IoT.3. Predictive Analytics:Advancements in big data analytics will enable organizations to make accurate predictions, forecast trends, and take proactive measures.Conclusion:Big data has the potential to transform industries and revolutionize decision-making processes. Its applications in healthcare, retail, finance, and various other sectors are already evident. However, challenges related to data privacy, quality, and scalability must be addressed. With the integration of AI and IoT, and advancements in predictive analytics, big data will continue to shape the future and drive innovation in every aspect of our lives.。
大数据英语介绍

Big Data, also known as megadata or massive data, refers to the vast amount of unstructured or structured data originating from various sources that is too large or complex for traditional data processing applications to handle. It is an IT industry term that characterizes the scale of data involved, which is immense to the point where it cannot be captured, managed, processed, and organized into intelligible information by mainstream software tools within a reasonable time frame.Big Data does not rely on statistical sampling methods; instead, it observes and tracks occurrences as they happen. This means that Big Data typically encompasses data sizes that exceed the processing capabilities of traditional software within acceptable timeframes. The emergence of Big Data has facilitated novel research across a wide range of topics, leading to the development of various statistical methods specifically for Big Data analysis.The significance of Big Data lies in its ability to reveal patterns, trends, and relationships that may not be apparent when analyzing smaller, isolated datasets. By combining and analyzing various small datasets, Big Data can provide valuable insights that can be used in areas such as business trend identification, research quality assessment, disease prevention, crime detection, and real-time traffic monitoring.Moreover, Big Data is characterized by five key dimensions: Volume (the vast amount of data), Velocity (the high speed of data generation and processing), Variety (the diverse nature of data types and sources), Value (the low-value density of useful information within vast datasets), and Veracity (the authenticity and trustworthiness of the data).In summary, Big Data represents a significant evolution in data management and analysis, enabling organizations to gain deeper insights and make more informed decisions based on the vast amounts of information available. Its potential applications are vast and diverse, spanning various industries and fields.。
大数据英文版介绍

The framework for big data in cloud computing
Big data is a term for data sets that are so large or complex that traditional data processing
Introduction
Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.
The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set.
hierarchical structure based on columns The NoSQL-type database supports a flexible
大数据介绍英文讲述

3
NoSQL
3 NoSQL
NoSQL refers to document-oriented databases SQL doesn’t scale well horizontally. It is schemaless. But not formless (JSON format). JSON: data interchange format Mongo Database Couch Database
3 NoSQL
Benefits
• Column-oriented organizations are more efficient when an aggregate needs to be computed over many rows but only for a notably smaller subset of all columns of data, because reading that smaller subset of data can be faster than reading all data. • Column-oriented organizations are more efficient when new values of a column are supplied for all rows at once, because that column data can be written efficiently and replace old column data without touching any other columns for the rows. • Row-oriented organizations are more efficient when many columns of a single row are required at the same time, and when row-size is relatively small, as the entire row can be retrieved with a single disk seek. • Row-oriented organizations are more efficient when writing a new row if all of the column data is supplied at the same time, as the entire row can be written with a single disk seek.
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
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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.
大数据英文演讲 Big Data presention

Volunteered Geographic Information (VGI)
添加标题
VGI generates from emergence of online service platform providing geographical location. Main application field
@ Refinement of individual attributive data
Background
添加标题
Individual behavior and its spatio-temporal variation are main subjects and foundation in urban studies and planning practices. The following will particularly introduce some perspectives about them, as well as the main application fields of different types of big data.
Individual behavior; Spatial pattern of specific behavior; Visualization of social network; Connection intensity between cities; Urban spatial structure and function division
05
Open research issues
Open research issues
Big data
has become a very heated issue in the
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.。
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EVERY MINUTE…
Didi rides hailed:
1,388
cabs
2,777
private cars
EVERY MINUTE…
395,833 People log 4 people are video or audio chatting
3 NoSQL
RDBMS vs NoSQL
• Xszc
Row DB: 001:10,Smith,Joe,40000;002:12,Jones,Mary,50000;003:11,Johnson,Cathy,44000;004:22,Jones,Bob,5 5000; index: 001:40000;002:50000;003:44000;004:55000; Column DB: 10:001,12:002,11:003,22:004;Smith:001,Jones:002,Johnson:003,Jones:004;Joe:001,Mary:002,Cathy: 003,Bob:004;40000:001,50000 …;Smith:001,Jones:002,004,Johnson:003;…
EVERY MINUTE…
625,000
Youku Tudou videos being watched
EVERY MINUTE…
64,814
posts and reposts on Weibo
4,166,667 search queries
SEARCH
EVERY MINUTE…
774 people buy something
3 NoSQL
Basic Availability
spread data across many storage systems with a high degree of replication.
Base Model
Soft State
data consistency is the developer's problem and should not be handled by the database.
Eventual Consistency
at some point in the future, data will converge to a consistent state. No guarantees are made “when”.
3 NoSQL
JSON Structure
{ field1: value1, field2: value2 … fieldN: valueN } var mydoc = { _id:ObjectId("5099803df3f4948bd2f98391"), name: { first: "Alan", last: "Turing" }, birth: new Date('Jun 23, 1912'), death: new Date('Jun 07, 1954'), contribs: [ "Turing machine", "Turing test", …], views : NumberLong(1250000) }
Variability
Inconsistency of the data set can hamper processes to handle and manage it.
Veracity
The quality of captured data can vary greatly, affecting accurate analysis.
on Alibaba’s marketplaces
US$1,133,942
spent on Alibaba
1 2
Definition
Characteristic NoSQL RDBMS MapReduce Applications
C
3
ONTENTS 4 5
6
1
Definition
1 Definition
Variety
The type and nature of the data.
Velocity
In this context, the speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development.
3
NoSQL
3 NoSQL
NoSQL refers to document-oriented databases SQL doesn’t scale well horizontally. It is schemaless. But not formless (JSON format). JSON: data interchange format Mongo Database Couch Database
3 NoSQL
Benefits
• Column-oriented organizations are more efficient when an aggregate needs to be computed over many rows but only for a notably smaller subset of all columns of data, because reading that smaller subset of data can be faster than reading all data. • Column-oriented organizations are more efficient when new values of a column are supplied for all rows at once, because that column data can be written efficiently and replace old column data without touching any other columns for the rows. • Row-oriented organizations are more efficient when many columns of a single row are required at the same time, and when row-size is relatively small, as the entire row can be retrieved with a single disk seek. • Row-oriented organizations are more efficient when writing a new row if all of the column data is supplied at the same time, as the entire row can be written with a single disk seek.
on a day-to-day basis
volume of data
BIG DATA
for better decisions
important data
2
Characteristic
2 Characteristic
Volume
The quantity of generated and stored data.