大数据时代英文翻译
大数据专业英语
大数据专业英语English:Big data is a field that encompasses the collection, storage, processing, analysis, and visualization of large and complex datasets to extract meaningful insights and knowledge. Professionals in this field utilize various tools and techniques such as data mining, machine learning, and artificial intelligence to uncover patterns, trends, and correlations within the data. They also employ advanced technologies like distributed computing and cloud computing to handle the massive volume, velocity, and variety of data generated in today's digital age. Moreover, big data professionals are proficient in programming languages like Python, R, and SQL to manipulate and manage data effectively. With the exponential growth of data across industries and domains, big data professionals play a crucial role in helping organizations make data-driven decisions, optimize processes, enhance customer experiences, and gain a competitive edge in the market.中文翻译:大数据是一个涵盖了大规模和复杂数据集的收集、存储、处理、分析和可视化,以提取有意义的见解和知识的领域。
复试计算机专业文献翻译
复试计算机专业⽂献翻译数据挖掘(Data Mining)NO.1:In the current era of big data, the mining and analysis of massive data is particularly important. Data mining technology has been widely applied in the fields of media, finance, medical care, transportation and e-commerce.However, the complexity and diversity of big data and the particularity of the application of data mining technology in various industries have also put forward new theoretical and technical challenges in the field of data mining.NO.2:The era of big data for the data mining technology has brought more opportunities and problems, such as the big data content for more efficient data mining algorithm and the accumulation of large data more quickness requirement of real-time data mining algorithms, the complexity of the large data diversity requires more flexible data mining algorithm, the universality of big data in all walks of life to the particularity in the field of data mining algorithm, etc.This also presents a new demand for data mining.翻译NO1在⼤数据时代,⼤量数据的挖掘和分析变得⾮常重要。
大学综合教程Unit原文及翻译大数据时代下的隐私保护
大学综合教程Unit原文及翻译大数据时代下的隐私保护With the rapid development of technology in the digital age, big data has become a powerful tool that drives various aspects of our society, including businesses, government, and academia. While big data brings tremendous opportunities and benefits, it also raises concerns about privacy protection.In this unit, we will explore the challenges and strategies for safeguarding privacy in the age of big data.随着数字时代技术的快速发展,大数据已经成为推动社会各个方面发展的强大工具,涵盖了商业、政府和学术界等多个领域。
尽管大数据带来了巨大的机遇和好处,但它也引发了人们对于隐私保护的担忧。
在本单元中,我们将探讨在大数据时代保护隐私所面临的挑战和策略。
1. The Power of Big Data大数据的力量In recent years, the sheer volume, variety, and velocity of data generated have surged exponentially. This explosion of data enables us to gain insights and knowledge that were once unimaginable. Big data analytics allows us to identify patterns, predict trends, and make informed decisions. Industries such as healthcare, finance, and marketing have greatly benefited from big data, leading to advancements and innovations.近年来,数据量的激增、种类的多样化以及速度的迅猛增长。
大数据演讲稿
大数据演讲稿篇一:大数据演讲稿大數據演講稿第二頁:人類從十三世紀以來,透過測量世界、進而征服世界,為了減少資料錯誤,確保資料品質,我們不斷改善工具,好讓測量更精準。
然而現在有愈來愈多的資料,我們必須要知道資料量越多,就愈不可能精確,因此我們必須換個心態,來接受這個事實。
第三頁:在大數據的概念裏頭,我們必須以新觀念來面對新局面,我們必須跳脫「越多越好」的概念,讓愈多會比品質愈好更重要。
因此我們要開始認識在這些越多的東西裏頭,無可避免會產生的雜亂問題,而也就是這個問題,我們必須了解有哪些雜亂!雜亂基本上分成三種,第一種是資料量多而產生的雜亂,越多的資料出錯率越高。
第二種是資料型態不同而產生相容性問題,例如:消防員用語音辨識系統和人做受災資料蒐集,機器和人收集資料型態不同,比對時無可避面會產生雜亂,但往往更能掌握當下的實際情況。
第三種是不同格式的資料型態產生的雜亂,此雜亂往往發生在提取或處理資料時,因為接收端與輸出端,資料格式不一,而產生的雜亂問題。
但我們不用擔心,舉個例子-用十隻很貴的溫度計量和一百隻便宜的溫度計量,雖然便宜不準,但蒐集越多的數據,也可以越看清全貌,因此更多的資料點,帶來的巨大價值,使得雜亂變得微不足道。
總之,我們可以犧牲一點精確度,取用所有的資料點,我們更能看出整體的大趨勢。
第四頁:西洋棋規則完善,行之有年,其主要歸功於他的演算法和殘局處理能力,而殘局處理能力往往源自於它內建的殘局應對資料,而這個殘局應對分析主要是在只剩下六顆棋子的情況下,每一步都經過完整的分析,做成巨量的表供程式做運算處理,那我們發現,如果我們讓其殘局應對資料增加越多,甚至高達1TB,我們越能讓程式變得完成無暇,無人能敵。
在語料庫的例子,這個例子來自微軟在做word 的文法檢查所得到的發現。
他們一開始在增進文法檢查這個功能上,考慮到,是否要改良演算法、用更複雜的功能去實現,or使用更多的資料去餵給現有的演算系統,結果發現,改良演算法,準確率提升8%,但用後者方法,準確率提升足足20%以上,由此兩個例子可知資料數量子資料品質更重要。
【独家原文翻译56页版】麦肯锡大数据:创新、竞争和生产力的下一个前沿(原文翻译)
大数据:创新、竞争和生产力的下一个前沿(原文翻译)麦肯锡在2011年5月发布了一个关于大数据方面的报告:《Big data: The next frontier for innovation, competition, and productivity》,虽然是6年前的报告,但是今天读来,还是非常用指导意义。
报告分为两个版本,一个是概要版20页,一个是完整版156页。
正好最近看了一遍概要版,觉得收益甚大。
所以试着翻译一下,仅供参考。
标题:Big data: The next frontier for innovation, competition, and productivity译文:大数据:创新、竞争和生产力的下一个前沿第二页是关于MGI(麦肯锡全球研究院)的介绍,就不翻译了。
略。
Data have become a torrent flowing into every area of the global economy. 1 Companies churn out a burgeoning volume of transactional data, capturing trillions of bytes of information about their customers, suppliers, and operations. millions of networked sensors are being embedded in the physical world in devices such as mobile phones, smart energy meters, automobiles, and industrial machines that sense, create, and communicate data in the age of the Internet of Things. 2 Indeed, as companies and organizations go about their business and interact with individuals, they are generating a tremendous amount of digital“exhaust data,”i.e., data that are created as a by-product of other activities. Social media sites, smartphones, and other consumer devices including PCs and laptops have allowed billions of individuals around the world to contribute to the amount of big data available. And the growing volume of multimedia content has played a major role in the exponential growth in the amount of big data (see Box 1, “What do we mean by ‘big data’?”). Each second of high-definition video, for example, generates more than 2,000 times as many bytes as required to store a single page of text. In a digitized world, consumers going about their day—communicating, browsing, buying, sharing, searching—create their own enormous trails of data.译文:数据已成为流入全球经济各个领域的激流。
英文作文技术对现代生活中的影响附中文翻译
英语作文技术对现代生活中的影响带中文翻译The impact of technology on modern life is significant. With the continuous advancement of technology, the emergence and application of various new technologies have profoundly changed our lifestyles, social structures, and economic models. Here are several aspects of how technology has influenced modern life:1. Communication and Socialization: Technological innovations have made communication and socialization more convenient. The prevalence of mobile phones, the internet, and social media has eliminated barriers of distance, enabling people to communicate anytime and anywhere. In addition, video calls, social platforms, and instant messaging tools allow people to stay connected with loved ones who are far away, promoting the development of interpersonal relationships.2. Education and Learning: Technological advancements have brought about significant changes in the field of education. New technologies such as online education, e-learning platforms, and e-books provide richer and more global learning resources. Students can acquire knowledge through online courses, and teachers and students caninteract and communicate through online teaching tools. Furthermore, virtual reality (VR) and augmented reality (AR) technologies provide more immersive and interactive learning experiences in education.3. Business and Economy: Technological advancements have had a profound impact on business and the economy. The prevalence of the internet has made e-commerce possible, allowing people to conveniently engage in transactions through online shopping and online payment methods. Additionally, technologies like cloud computing, big data analysis, and artificial intelligence provide businesses with more efficient management and operational means. Moreover, technological innovation has brought about new business models and the reconfiguration of industrial chains, driving economic development and innovation.4. Quality of Life and Convenience: Technological advancements have greatly enhanced our quality of life and convenience. Smartphones, smart homes, and smart devices enable us to conveniently manage and control various devices and household facilities. Smart assistants and voice recognition technologies provide us with more intelligent and personalized services. Furthermore,technological advancements have also brought about innovations in more efficient transportation systems, smart city construction, healthcare, and other areas, improving our living standards and well-being.In conclusion, the impact of technology on modern life is multifaceted. It has changed our lifestyles, social structures, and economic models. The continuous development of technology provides us with more convenience and opportunities while also posing challenges and issues. Therefore, we need to adapt to and utilize technology wisely, maximizing its benefits, and pay attention to addressing potential negative impacts.中文翻译为:技术在现代生活中的影响是巨大的。
大数据时代 英文作文
大数据时代英文作文英文:In the era of big data, information is being generated at an unprecedented rate. As a result, the field of data analysis has become increasingly important. In my opinion, big data has both advantages and disadvantages.On the one hand, big data allows us to gain insights into patterns and trends that would be impossible to detect otherwise. For example, a company could use data analysis to identify which products are most popular with customers, and adjust their marketing strategy accordingly. Similarly, doctors could use data to identify risk factors for certain diseases, and develop preventative measures.On the other hand, big data can be overwhelming. With so much information available, it can be difficult to know where to start. Additionally, there is a risk of relying too heavily on data analysis, and neglecting other factorsthat may be important. For example, a company may focus solely on data analysis to determine which products to sell, and overlook the importance of customer feedback.Overall, I believe that big data has the potential to revolutionize many industries, but it is important to approach it with caution and balance.中文:在大数据时代,信息以前所未有的速度产生。
大数据英文翻译
大数据英文翻译Big Data TranslationWith the rapid advancement of technology, the amount of data collected and generated is increasing exponentially. This immense volume of data is commonly referred to as "Big Data". Big Data refers to data sets that are too large and complex to be processed by traditional data processing systems.In recent years, Big Data has become a hot topic in various industries as it has the potential to provide valuable insights and improve decision-making processes. Big Data is often characterized by the "3Vs" – volume, velocity, and variety. Volume refers to the vast amount of data that is being produced every second. Velocity refers to the speed at which this data is being generated and needs to be processed. Lastly, variety refers to the different types and formats of data that are being collected, including structured data (such as numbers and dates) and unstructured data (such as text, images, and videos).The analysis of Big Data requires advanced analytics techniques and tools such as data mining, machine learning, and predictive modeling. These techniques allow organizations to extract meaningful patterns and trends from the vast amount of data. Additionally, Big Data analytics can help identify hidden correlations and relationships that may not be apparent at first glance. By understanding these patterns, organizations can make data-driven decisions and gain a competitive advantage in their respective industries.The impact of Big Data can be seen in various fields. In healthcare, Big Data analytics can be used to improve patient outcomes and personalize treatments. By analyzing patient records, genetic data, and other medical information, healthcare providers can identify risk factors, predict diseases, and recommend personalized treatment plans. In finance, Big Data analytics can be used to detect fraudulent activities and identify investment opportunities. By analyzing market trends, consumer behavior, and economic indicators, financial institutions can make informed decisions and mitigate risks.However, the use of Big Data also raises concerns about privacy and security. With the collection of vast amounts of personal data, there is an increased risk of data breaches and unauthorized access. To address these concerns, organizations need to implement robust security measures and ensure compliance with data protection regulations.In conclusion, Big Data has the potential to revolutionize various industries by providing valuable insights and improving decision-making processes. However, it also poses challenges in terms of data management, analysis, and security. Organizations that are able to effectively harness the power of Big Data will be better equipped to succeed in the data-driven era.。
大数据挖掘外文翻译文献
文献信息:文献标题:A Study of Data Mining with Big Data(大数据挖掘研究)国外作者:VH Shastri,V Sreeprada文献出处:《International Journal of Emerging Trends and Technology in Computer Science》,2016,38(2):99-103字数统计:英文2291单词,12196字符;中文3868汉字外文文献:A Study of Data Mining with Big DataAbstract Data has become an important part of every economy, industry, organization, business, function and individual. Big Data is a term used to identify large data sets typically whose size is larger than the typical data base. Big data introduces unique computational and statistical challenges. Big Data are at present expanding in most of the domains of engineering and science. Data mining helps to extract useful data from the huge data sets due to its volume, variability and velocity. This article presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective.Keywords: Big Data, Data Mining, HACE theorem, structured and unstructured.I.IntroductionBig Data refers to enormous amount of structured data and unstructured data thatoverflow the organization. If this data is properly used, it can lead to meaningful information. Big data includes a large number of data which requires a lot of processing in real time. It provides a room to discover new values, to understand in-depth knowledge from hidden values and provide a space to manage the data effectively. A database is an organized collection of logically related data which can be easily managed, updated and accessed. Data mining is a process discovering interesting knowledge such as associations, patterns, changes, anomalies and significant structures from large amount of data stored in the databases or other repositories.Big Data includes 3 V’s as its characteristics. They are volume, velocity and variety. V olume means the amount of data generated every second. The data is in state of rest. It is also known for its scale characteristics. Velocity is the speed with which the data is generated. It should have high speed data. The data generated from social media is an example. Variety means different types of data can be taken such as audio, video or documents. It can be numerals, images, time series, arrays etc.Data Mining analyses the data from different perspectives and summarizing it into useful information that can be used for business solutions and predicting the future trends. Data mining (DM), also called Knowledge Discovery in Databases (KDD) or Knowledge Discovery and Data Mining, is the process of searching large volumes of data automatically for patterns such as association rules. It applies many computational techniques from statistics, information retrieval, machine learning and pattern recognition. Data mining extract only required patterns from the database in a short time span. Based on the type of patterns to be mined, data mining tasks can be classified into summarization, classification, clustering, association and trends analysis.Big Data is expanding in all domains including science and engineering fields including physical, biological and biomedical sciences.II.BIG DATA with DATA MININGGenerally big data refers to a collection of large volumes of data and these data are generated from various sources like internet, social-media, business organization, sensors etc. We can extract some useful information with the help of Data Mining. It is a technique for discovering patterns as well as descriptive, understandable, models from a large scale of data.V olume is the size of the data which is larger than petabytes and terabytes. The scale and rise of size makes it difficult to store and analyse using traditional tools. Big Data should be used to mine large amounts of data within the predefined period of time. Traditional database systems were designed to address small amounts of data which were structured and consistent, whereas Big Data includes wide variety of data such as geospatial data, audio, video, unstructured text and so on.Big Data mining refers to the activity of going through big data sets to look for relevant information. To process large volumes of data from different sources quickly, Hadoop is used. Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. Its distributed supports fast data transfer rates among nodes and allows the system to continue operating uninterrupted at times of node failure. It runs Map Reduce for distributed data processing and is works with structured and unstructured data.III.BIG DATA characteristics- HACE THEOREM.We have large volume of heterogeneous data. There exists a complex relationship among the data. We need to discover useful information from this voluminous data.Let us imagine a scenario in which the blind people are asked to draw elephant. The information collected by each blind people may think the trunk as wall, leg as tree, body as wall and tail as rope. The blind men can exchange information with each other.Figure1: Blind men and the giant elephantSome of the characteristics that include are:i.Vast data with heterogeneous and diverse sources: One of the fundamental characteristics of big data is the large volume of data represented by heterogeneous and diverse dimensions. For example in the biomedical world, a single human being is represented as name, age, gender, family history etc., For X-ray and CT scan images and videos are used. Heterogeneity refers to the different types of representations of same individual and diverse refers to the variety of features to represent single information.ii.Autonomous with distributed and de-centralized control: the sources are autonomous, i.e., automatically generated; it generates information without any centralized control. We can compare it with World Wide Web (WWW) where each server provides a certain amount of information without depending on other servers.plex and evolving relationships: As the size of the data becomes infinitely large, the relationship that exists is also large. In early stages, when data is small, there is no complexity in relationships among the data. Data generated from social media and other sources have complex relationships.IV.TOOLS:OPEN SOURCE REVOLUTIONLarge companies such as Facebook, Yahoo, Twitter, LinkedIn benefit and contribute work on open source projects. In Big Data Mining, there are many open source initiatives. The most popular of them are:Apache Mahout:Scalable machine learning and data mining open source software based mainly in Hadoop. It has implementations of a wide range of machine learning and data mining algorithms: clustering, classification, collaborative filtering and frequent patternmining.R: open source programming language and software environment designed for statistical computing and visualization. R was designed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand beginning in 1993 and is used for statistical analysis of very large data sets.MOA: Stream data mining open source software to perform data mining in real time. It has implementations of classification, regression; clustering and frequent item set mining and frequent graph mining. It started as a project of the Machine Learning group of University of Waikato, New Zealand, famous for the WEKA software. The streams framework provides an environment for defining and running stream processes using simple XML based definitions and is able to use MOA, Android and Storm.SAMOA: It is a new upcoming software project for distributed stream mining that will combine S4 and Storm with MOA.Vow pal Wabbit: open source project started at Yahoo! Research and continuing at Microsoft Research to design a fast, scalable, useful learning algorithm. VW is able to learn from terafeature datasets. It can exceed the throughput of any single machine networkinterface when doing linear learning, via parallel learning.V.DATA MINING for BIG DATAData mining is the process by which data is analysed coming from different sources discovers useful information. Data Mining contains several algorithms which fall into 4 categories. They are:1.Association Rule2.Clustering3.Classification4.RegressionAssociation is used to search relationship between variables. It is applied in searching for frequently visited items. In short it establishes relationship among objects. Clustering discovers groups and structures in the data.Classification deals with associating an unknown structure to a known structure. Regression finds a function to model the data.The different data mining algorithms are:Table 1. Classification of AlgorithmsData Mining algorithms can be converted into big map reduce algorithm based on parallel computing basis.Table 2. Differences between Data Mining and Big DataVI.Challenges in BIG DATAMeeting the challenges with BIG Data is difficult. The volume is increasing every day. The velocity is increasing by the internet connected devices. The variety is also expanding and the organizations’ capability to capture and process the data is limited.The following are the challenges in area of Big Data when it is handled:1.Data capture and storage2.Data transmission3.Data curation4.Data analysis5.Data visualizationAccording to, challenges of big data mining are divided into 3 tiers.The first tier is the setup of data mining algorithms. The second tier includesrmation sharing and Data Privacy.2.Domain and Application Knowledge.The third one includes local learning and model fusion for multiple information sources.3.Mining from sparse, uncertain and incomplete data.4.Mining complex and dynamic data.Figure 2: Phases of Big Data ChallengesGenerally mining of data from different data sources is tedious as size of data is larger. Big data is stored at different places and collecting those data will be a tedious task and applying basic data mining algorithms will be an obstacle for it. Next we need to consider the privacy of data. The third case is mining algorithms. When we are applying data mining algorithms to these subsets of data the result may not be that much accurate.VII.Forecast of the futureThere are some challenges that researchers and practitioners will have to deal during the next years:Analytics Architecture:It is not clear yet how an optimal architecture of analytics systems should be to deal with historic data and with real-time data at the same time. An interesting proposal is the Lambda architecture of Nathan Marz. The Lambda Architecture solves the problem of computing arbitrary functions on arbitrary data in real time by decomposing the problem into three layers: the batch layer, theserving layer, and the speed layer. It combines in the same system Hadoop for the batch layer, and Storm for the speed layer. The properties of the system are: robust and fault tolerant, scalable, general, and extensible, allows ad hoc queries, minimal maintenance, and debuggable.Statistical significance: It is important to achieve significant statistical results, and not be fooled by randomness. As Efron explains in his book about Large Scale Inference, it is easy to go wrong with huge data sets and thousands of questions to answer at once.Distributed mining: Many data mining techniques are not trivial to paralyze. To have distributed versions of some methods, a lot of research is needed with practical and theoretical analysis to provide new methods.Time evolving data: Data may be evolving over time, so it is important that the Big Data mining techniques should be able to adapt and in some cases to detect change first. For example, the data stream mining field has very powerful techniques for this task.Compression: Dealing with Big Data, the quantity of space needed to store it is very relevant. There are two main approaches: compression where we don’t loose anything, or sampling where we choose what is thedata that is more representative. Using compression, we may take more time and less space, so we can consider it as a transformation from time to space. Using sampling, we are loosing information, but the gains inspace may be in orders of magnitude. For example Feldman et al use core sets to reduce the complexity of Big Data problems. Core sets are small sets that provably approximate the original data for a given problem. Using merge- reduce the small sets can then be used for solving hard machine learning problems in parallel.Visualization: A main task of Big Data analysis is how to visualize the results. As the data is so big, it is very difficult to find user-friendly visualizations. New techniques, and frameworks to tell and show stories will be needed, as for examplethe photographs, infographics and essays in the beautiful book ”The Human Face of Big Data”.Hidden Big Data: Large quantities of useful data are getting lost since new data is largely untagged and unstructured data. The 2012 IDC studyon Big Data explains that in 2012, 23% (643 exabytes) of the digital universe would be useful for Big Data if tagged and analyzed. However, currently only 3% of the potentially useful data is tagged, and even less is analyzed.VIII.CONCLUSIONThe amounts of data is growing exponentially due to social networking sites, search and retrieval engines, media sharing sites, stock trading sites, news sources and so on. Big Data is becoming the new area for scientific data research and for business applications.Data mining techniques can be applied on big data to acquire some useful information from large datasets. They can be used together to acquire some useful picture from the data.Big Data analysis tools like Map Reduce over Hadoop and HDFS helps organization.中文译文:大数据挖掘研究摘要数据已经成为各个经济、行业、组织、企业、职能和个人的重要组成部分。
未来企业的英文作文带翻译
Twitter announced in 2020 that employees will have the option to work from home permanently, even after the pandemic. This decision was made in response to positive feedback from employees who appreciated the flexibility and autonomy that remote work offers. By embracing remote work, Twitter is setting a precedent for the future of work and redefining traditional office norms.
Case Study: Amazon
Amazon is a prime example of a company that has successfully embraced digital transformation. By leveraging advanced algorithms and machine learning, Amazon has been able topersonalize recommendations for its customers, optimize its supply chain, and improve the overall shopping experience. As a result, Amazon has become a leader in e-commerce and has disrupted traditional retail models.
大数据名词多语翻译
大数据名词多语翻译学习大数据相关名词的多语言翻译是一个很好的方式来扩展你的词汇量并提高你的语言能力。
下面是一些常见的大数据名词及其中英文对照:1. 大数据(Big Data)2. 数据分析(Data Analysis)3. 数据挖掘(Data Mining)4. 数据可视化(Data Visualization)5. 数据仓库(Data Warehouse)6. 数据模型(Data Model)7. 数据集(Dataset)8. 数据处理(Data Processing)9. 数据清洗(Data Cleansing)10. 数据科学家(Data Scientist)11. 机器学习(Machine Learning)12. 人工智能(Artificial Intelligence)13. 云计算(Cloud Computing)14. 预测分析(Predictive Analytics)15. 实时分析(Real-time Analytics)当学习这些名词时,你可以采取以下学习技巧来记忆和理解它们:1. 制作词汇卡片:将中英文对照的名词写在一张卡片的一面,另一面写上对应的释义。
每天复习一些卡片,直到你记住所有的名词和它们的意思。
2. 应用名词:尽量将这些名词应用到你的写作、口语练习或者与他人的交流中。
这样能帮助你更好地理解和记忆这些词汇。
3. 创造相关的例句:为每个名词创造一些例句,这样可以帮助你更好地理解其用法和上下文。
4. 多媒体学习:寻找相关的视频、音频或文章来帮助你更好地理解和记忆这些名词。
你可以通过观看教学视频、听听流行歌曲或者阅读相关的新闻文章来扩展你对这些名词的理解。
通过不断地练习和应用这些学习技巧,你将能够更轻松地掌握大数据领域的词汇,并提高你的语言能力。
记住,持之以恒是成功的关键,所以要坚持学习并保持积极的学习态度!。
互联网大数据金融中英文对照外文翻译文献
互联网大数据金融中英文对照外文翻译文献(文档含英文原文和中文翻译)原文:Internet Finance's Impact on Traditional FinanceAbstractAs the advances in modern information and Internet technology, especially the develop of cloud computing, big data, mobile Internet, search engines and social networks, profoundly change, even subvert many traditional industries, and the financial industry is no exception. In recent years, financial industry has become the most far-reaching area influenced by Internet, after commercial distribution and the media. Many Internet-based financial service models have emerged, and have had a profound and huge impact on traditional financial industries. "Internet-Finance" has win the focus of public attention.Internet-Finance is low cost, high efficiency, and pays more attention to the user experience, and these features enable it to fully meet the special needs of traditional "long tail financial market", to flexibly provide more convenient and efficient financial services and diversified financial products, to greatly expand the scope and depth of financial services, to shorten the distance between people space and time, andto establish a new financial environment, which effectively integrate and take use of fragmented time, information, capital and other scattered resources, then add up to form a scale, and grow a new profit point for various financial institutions. Moreover, with the continuous penetration and integration in traditional financial field, Internet-Finance will bring new challenges, but also opportunities to the traditional. It contribute to the transformation of the traditional commercial banks, compensate for the lack of efficiency in funding process and information integration, and provide new distribution channels for securities, insurance, funds and other financial products. For many SMEs, Internet-Finance extend their financing channels, reduce their financing threshold, and improve their efficiency in using funds. However, the cross-industry nature of the Internet Finance determines its risk factors are more complex, sensitive and varied, and therefore we must properly handle the relationship between innovative development and market regulation, industry self-regulation.Key Words:Internet Finance; Commercial Banks; Effects; Regulatory1 IntroductionThe continuous development of Internet technology, cloud computing, big data, a growing number of Internet applications such as social networks for the business development of traditional industry provides a strong support, the level of penetration of the Internet on the traditional industry. The end of the 20th century, Microsoft chairman Bill Gates, who declared, "the traditional commercial bank will become the new century dinosaur". Nowadays, with the development of the Internet electronic information technology, we really felt this trend, mobile payment, electronic bank already occupies the important position in our daily life.Due to the concept of the Internet financial almost entirely from the business practices, therefore the present study focused on the discussion. Internet financial specific mode, and the influence of traditional financial industry analysis and counter measures are lack of systemic research. Internet has always been a key battleground in risk investment, and financial industry is the thinking mode of innovative experimental various business models emerge in endlessly, so it is difficult to use a fixed set of thinking to classification and definition. The mutual penetration andintegration of Internet and financial, is a reflection of technical development and market rules requirements, is an irreversible trend. The Internet bring traditional financial is not only a low cost and high efficiency, more is a kind of innovative thinking mode and unremitting pursuit of the user experience. The traditional financial industry to actively respond to. Internet financial, for such a vast blue ocean enough to change the world, it is very worthy of attention to straighten out its development, from the existing business model to its development prospects."Internet financial" belongs to the latest formats form, discusses the Internet financial research of literature, but the lack of systemic and more practical. So this article according to the characteristics of the Internet industry practical stronger, the several business models on the market for summary analysis, and the traditional financial industry how to actively respond to the Internet wave of financial analysis and Suggestions are given, with strong practical significance.2 Internet financial backgroundInternet financial platform based on Internet resources, on the basis of the big data and cloud computing new financial model. Internet finance with the help of the Internet technology, mobile communication technology to realize financing, payment and information intermediary business, is a traditional industry and modern information technology represented by the Internet, mobile payment, cloud computing, data mining, search engines and social networks, etc.) Produced by the combination of emerging field. Whether financial or the Internet, the Internet is just the difference on the strategic, there is no strict definition of distinction. As the financial and the mutual penetration and integration of the Internet, the Internet financial can refer all through the Internet technology to realize the financing behavior. Internet financial is the Internet and the traditional financial product of mutual infiltration and fusion, the new financial model has a profound background. The emergence of the Internet financial is a craving for cost reduction is the result of the financial subject, is also inseparable from the rapid development of modern information technology to provide technical support.2.1 Demands factorsTraditional financial markets there are serious information asymmetry, greatly improve the transaction risk. Exhibition gradually changed people's spending habits, more and more high to the requirement of service efficiency and experience; In addition, rising operating costs, to stimulate the financial main body's thirst for financial innovation and reform; This pulled by demand factors, become the Internet financial produce powerful inner driving force.2.2 Supply driving factorData mining, cloud computing and Internet search engines, such as the development of technology, financial and institutional technology platform. Innovation, enterprise profit-driven mixed management, etc., for the transformation of traditional industry and Internet companies offered financial sector penetration may, for the birth and development of the Internet financial external technical support, become a kind of externalization of constitution. In the Internet "openness, equality, cooperation, share" platform, third-party financing and payment, online investment finance, credit evaluation model, not only makes the traditional pattern of financial markets will be great changes have taken place, and modern information technology is more easily to serve various financial entities. For the traditional financial institutions, especially in the banking, securities and insurance institutions, more opportunities than the crisis, development is better than a challenge.3 Internet financial constitute the main body3.1 Capital providersBetween Internet financial comprehensive, its capital providers include not only the traditional financial institutions, including penetrating into the Internet. In terms of the current market structure, the traditional financial sector mainly include commercial Banks, securities, insurance, fund and small loan companies, mainly includes the part of the Internet companies and emerging subject, such as the amazon, and some channels on Internet for the company. These companies is not only the providers of capital market, but also too many traditional so-called "low net worth clients" suppliers of funds into the market. In operation form, the former mainly through the Internet, to the traditional business externalization, the latter mainlythrough Internet channels to penetrate business, both externalization and penetration, both through the Internet channel to achieve the financial business innovation and reform.3.2 Capital demandersInternet financial mode of capital demanders although there is no breakthrough in the traditional government, enterprise and individual, but on the benefit has greatly changed. In the rise and development of the Internet financial, especially Internet companies to enter the threshold of made in the traditional financial institutions, relatively weak groups and individual demanders, have a more convenient and efficient access to capital. As a result, the Internet brought about by the universality and inclusive financial better than the previous traditional financial pattern.3.3 IntermediariesInternet financial rely on efficient and convenient information technology, greatly reduces the financial markets is the wrong information. Docking directly through Internet, according to both parties, transaction cost is greatly reduced, so the Internet finance main body for the dependence of the intermediary institutions decreased significantly, but does not mean that the Internet financial markets, there is no intermediary institutions. In terms of the development of the Internet financial situation at present stage, the third-party payment platform plays an intermediary role in this field, not only ACTS as a financial settlement platform, but also to the capital supply and demand of the integration of upstream and downstream link multi-faceted, in meet the funds to pay at the same time, have the effect of capital allocation. Especially in the field of electronic commerce, this function is more obvious.3.4 Large financial dataBig financial data collection refers to the vast amounts of unstructured data, through the study of the depth of its mining and real-time analysis, grasp the customer's trading information, consumption habits and consumption information, and predict customer behavior and make the relevant financial institutions in the product design, precise marketing and greatly improve the efficiency of risk management, etc. Financial services platform based on the large data mainly refers to with vast tradingdata of the electronic commerce enterprise's financial services. The key to the big data from a large number of chaotic ability to rapidly gaining valuable information in the data, or from big data assets liquidation ability quickly. Big data information processing, therefore, often together with cloud computing.4 Global economic issuesFOR much of the past year the fast-growing economies of the emerging world watched the Western financial hurricane from afar. Their own banks held few of the mortgage-based assets that undid the rich world’s financial firms. Commodity exporters were thriving, thanks to high prices fo r raw materials. China’s economic juggernaut powered on. And, from Budapest to Brasília, an abundance of credit fuelled domestic demand. Even as talk mounted of the rich world suffering its worst financial collapse since the Depression, emerging economies seemed a long way from the centre of the storm.No longer. As foreign capital has fled and confidence evaporated, the emerging world’s stockmarkets have plunged (in some cases losing half their value) and currencies tumbled. The seizure in the credit market caused havoc, as foreign banks abruptly stopped lending and stepped back from even the most basic banking services, including trade credits.Like their rich-world counterparts, governments are battling to limit the damage (see article). That is easiest for those with large foreign-exchange reserves. Russia is spending $220 billion to shore up its financial services industry. South Korea has guaranteed $100 billion of its banks’ debt. Less well-endowed countries are asking for help.Hungary has secured a EURO5 billion ($6.6 billion) lifeline from the European Central Bank and is negotiating a loan from the IMF, as is Ukraine. Close to a dozen countries are talking to the fund about financial help.Those with long-standing problems are being driven to desperate measures. Argentina is nationalising its private pension funds, seeminglyto stave off default (see article). But even stalwarts are looking weaker. Figures released this week showed that China’s growth slowed to 9% in the year to the third quarter-still a rapid pace but a lot slower than the double-digit rates of recent years.The various emerging economies are in different states of readiness, but the cumulative impact of all this will be enormous. Most obviously, how these countries fare will determine whether the world economy faces a mild recession or something nastier. Emerging economies accounted for around three-quarters of global growth over the past 18 months. But their economic fate will also have political consequences.In many places-eastern Europe is one example (see article)-financial turmoil is hitting weak governments. But even strong regimes could suffer. Some experts think that China needs growth of 7% a year to contain social unrest. More generally, the coming strife will shape the debate about the integration of the world economy. Unlike many previous emerging-market crises, today’s mess spread from the rich world, largely thanks to increasingly integrated capital markets. If emerging economies collapse-either into a currency crisis or a sharp recession-there will be yet more questioning of the wisdom of globalised finance.Fortunately, the picture is not universally dire. All emerging economies will slow. Some will surely face deep recessions. But many are facing the present danger in stronger shape than ever before, armed with large reserves, flexible currencies and strong budgets. Good policy-both at home and in the rich world-can yet avoid a catastrophe.One reason for hope is that the direct economic fallout from the rich world’s d isaster is manageable. Falling demand in America and Europe hurts exports, particularly in Asia and Mexico. Commodity prices have fallen: oil is down nearly 60% from its peak and many crops and metals have done worse. That has a mixed effect. Although it hurtscommodity-exporters from Russia to South America, it helps commodity importers in Asia and reduces inflation fears everywhere. Countries like Venezuela that have been run badly are vulnerable (see article), but given the scale of the past boom, the commodity bust so far seems unlikely to cause widespread crises.The more dangerous shock is financial. Wealth is being squeezed as asset prices decline. China’s house prices, for instance, have started falling (see article). This will dampen domestic confidence, even though consumers are much less indebted than they are in the rich world. Elsewhere, the sudden dearth of foreign-bank lending and the flight of hedge funds and other investors from bond markets has slammed the brakes on credit growth. And just as booming credit once underpinned strong domestic spending, so tighter credit will mean slower growth.Again, the impact will differ by country. Thanks to huge current-account surpluses in China and the oil-exporters in the Gulf, emerging economies as a group still send capital to the rich world. But over 80 have deficits of more than 5% of GDP. Most of these are poor countries that live off foreign aid; but some larger ones rely on private capital. For the likes of Turkey and South Africa a sudden slowing in foreign financing would force a dramatic adjustment. A particular worry is eastern Europe, where many countries have double-digit deficits. In addition, even some countries with surpluses, such as Russia, have banks that have grown accustomed to easy foreign lending because of the integration of global finance. The rich world’s bank bail-outs may limit the squeeze, but the flow of capital to the emerging world will slow. The Institute of International Finance, a bankers’ group, expects a 30% decline in net flows of private capital from last year.This credit crunch will be grim, but most emerging markets can avoid catastrophe. The biggest ones are in relatively good shape. The morevulnerable ones can (and should) be helped.Among the giants, China is in a league of its own, with a $2 trillion arsenal of reserves, a current-account surplus, little connection to foreign banks and a budget surplus that offers lots of room to boost spending. Since the country’s leaders have made clear that they will do whatev er it takes to cushion growth, China’s economy is likely to slow-perhaps to 8%-but not collapse. Although that is not enough to save the world economy, such growth in China would put a floor under commodity prices and help other countries in the emerging world.The other large economies will be harder hit, but should be able to weather the storm. India has a big budget deficit and many Brazilian firms have a large foreign-currency exposure. But Brazil’s economy is diversified and both countries have plenty of reserves to smooth the shift to slower growth. With $550 billion of reserves, Russia ought to be able to stop a run on the rouble. In the short-term at least, the most vulnerable countries are all smaller ones.There will be pain as tighter credit forces adjustments. But sensible, speedy international assistance would make a big difference. Several emerging countries have asked America’s Federal Reserve for liquidity support; some hope that China will bail them out. A better route is surely the IMF, which has huge expertise and some $250 billion to lend. Sadly, borrowing from the fund carries a stigma. That needs to change. The IMF should develop quicker, more flexible financial instruments and minimise the conditions it attaches to loans. Over the past month deft policymaking saw off calamity in the rich world. Now it is time for something similar in the emerging world.5 ConclusionsInternet financial model can produce not only huge social benefit, lower transaction costs, provide higher than the existing direct and indirect financingefficiency of the allocation of resources, to provide power for economic development, will also be able to use the Internet and its related software technology played down the traditional finance specialized division of labor, makes the financial participants more mass popularization, risk pricing term matching complex transactions, tend to be simple. Because of the Internet financial involved in the field are mainly concentrated in the field of traditional financial institutions to the current development is not thorough, namely traditional financial "long tail" market, can complement with the original traditional financial business situation, so in the short term the Internet finance from the Angle of the size of the market will not make a big impact to the traditional financial institutions, but the Internet financial business model, innovative ideas, and its apparent high efficiency for the traditional financial institutions brought greater impact on the concept, also led to the traditional financial institutions to further accelerate the mutual penetration and integration with the Internet.译文:互联网金融对传统金融的影响作者:罗萨米;拉夫雷特摘要网络的发展,深刻地改变甚至颠覆了许多传统行业,金融业也不例外。
译者注写作的形式及其原则——以《大数据时代》为例
DOI :10.19867/ki.writing.2021.02.010第2期2021年4月No.2Apr.2021作者简介:王海峰,上海大学文学院博士研究生。
电子信箱:*******************。
①熊宣东:《佛典译论译史研究:意义、现状与对策》,《上海翻译》2019年第6期。
②[英]维克托·迈尔-舍恩伯格、肯尼斯·库克耶:《大数据时代》,盛杨燕、周涛译,杭州:浙江人民出版社2013年版。
译者注历史悠久,自有不同语言著述的文化传播与交流开始,译者注便随之产生和发展。
我国译者注的发源要追溯至域外文化传播之际,具体可以追溯至西汉时期对异域佛经的翻译活动①。
译者注是跨文化传播而产生的一种文化解释现象与活动。
总体而言,其历史要晚于本土文献的注释活动。
译者注是翻译者对其翻译内容所作的注释。
对原文本而言,译者注属于他注,是在原文本对象翻译之外的一种衍生写作行为。
而本土文献的注释,则可以是他注,也可以是自注。
译者注和普遍意义上的注释一样,最开始的主要目的是便于读者更好地理解原文本的内容。
后来,译者注写作加入了翻译者的主观理解等内容。
译者注活动可以反映出,在跨语言与跨文化传播过程中,翻译者注重读者理解力的问题。
从阅读学的角度看,译者注一方面反映了翻译者对翻译文本的理解与思考,另一方面也反映了翻译者将读者的理解和接受置于文化传播中重要位置的思想。
本文以盛杨燕、周涛两位译者翻译的维克托·迈尔-舍恩伯格、肯尼斯·库克耶合著的《大数据时代》②一书的译者注为主要研究对象,兼及选取其他几本译著中的译者注为参考对象,通过对选取译本中的译者注样本进行文本分析与一定程度的量化分析研究,归纳和总结译者注写作的类属与层级,并在此基础上进一步探讨译者注的知识背景与写作原则。
本文认为,译者注写作要遵循补偿译者注写作的形式及其原则——以《大数据时代》为例王海峰摘要:译者注是一种帮助读者有效理解译著原文本的副文本,具有独特的文体特征和价值功能。
大数据用英文怎么说
大数据用英文怎么说
数据在现代社会中扮演着越来越重要的角色,并在一些领域中被大
规模地应用。
其中,“大数据”一词随着互联网技术的发展而广泛使用。
那么大数据用英文怎么说呢?
大数据的英文翻译是“Big Data”。
在信息技术领域中,它是指企业
或组织收集的大量非结构化或结构化的数据。
这些数据通常需要通过
一系列的技术手段来进行收集、处理和分析。
目前“Big Data”被广泛应用于商业、金融、医疗、智能交通、环境监测和军事等领域,成为企
业或组织获取信息、提高决策水平和创造价值的重要手段。
由于“Big Data”在全球范围内流行,已经成为国际标准术语,许多
企业和组织已经将其融入到日常业务和管理中。
同时,随着大数据时
代的到来,大数据分析技术已成为很多企业或组织的“核心竞争力”之一,深受业界的关注和认可。
除了“Big Data”外,在不同的领域中也存在着其他的大数据相关术语。
例如,在金融领域,“Big Data”与人工智能技术的结合被称为“Fintech(金融科技)”;在医疗领域,“Big Data”与人工智能技术的结合
被称为“Healthtech(健康科技)”等等。
总之,“Big Data”是指海量的数据和与之相关的技术手段,被广泛
应用于各个领域。
它是当今国际上通用的术语之一,我们需要掌握这
一术语的英文表达,以便更广泛地参与到国际化合作和交流中。
信息时代相关的词汇英语
信息时代相关的词汇英语English:"In the Information Age, a plethora of vocabulary has emerged to describe the technologies, phenomena, and concepts shaping our digital world. Terms like 'big data' refer to the massive volumes of structured and unstructured data generated by organizations and individuals, driving insights and innovation. 'Artificial intelligence' (AI) encompasses machine learning, natural language processing, and other technologies enabling computers to perform tasks that traditionally required human intelligence. 'Cybersecurity' involves protecting computer systems, networks, and data from theft, damage, or unauthorized access, a crucial concern in an interconnected world. 'Cloud computing' revolutionizes IT infrastructure by providing on-demand access to computing resources over the internet, offering scalability, flexibility, and cost-efficiency. 'Internet of Things' (IoT) describes the network of interconnected devices embedded with sensors and software, enabling them to collect and exchange data, transforming industries and daily life. 'Blockchain' technology underpins cryptocurrencies like Bitcoin, facilitating secure and transparent transactions throughdecentralized ledgers. 'Virtual reality' (VR) immerses users in computer-generated environments, while 'augmented reality' (AR) overlays digital content onto the real world, both reshaping entertainment, education, and various industries. '5G' refers to the fifth generation of mobile networks, promising faster speeds, lower latency, and greater connectivity, driving advancements in communication and technology. 'E-commerce' encompasses buying and selling goods and services online, reshaping retail and commerce globally. These terms exemplify the lexicon of the Information Age, reflecting the rapid evolution and profound impact of technology on society and the economy."中文翻译:"在信息时代,出现了大量词汇来描述塑造我们数字世界的技术、现象和概念。
大数据时代——生活、工作与思维的大变革
应用不当
会变成损害民众利益的工具
大数据时代,告知与许可、模糊化和匿名化三大隐私保护策略都失效! 挣脱大数据的困境,是大数据时代人类共同的战争!
面临的风险
我们的生活处处受到监视
人们可能因为将做而受惩罚 想象中XX“苍井老湿”也要受罚?
我们的隐私被二次利用
可怕的数据独裁 某天朝可实施更高明的和谐?
大数据时代
生活、工作与思维的大变革
作者:[英]维克托 · 迈尔-舍恩伯格 译者:盛劳燕 周涛 肯尼思 · 库克耶 出版:浙江人民出版社
制作:@天天向Qian前
大数据时代 之抱
身处
大数据时代
!
我们已经处在大数据时代,可能还浑然不知 维克托教授将带我们一窥大数据时代的全景
时间就是生命! 事件一:变革公共卫生
02.重组数据 05 . 数据废气
03.可扩展数据 06开放数据
本章的例子
IBM,电动汽车动力与电力供应系统优化预测 Hitwise,通过流量判断消费者喜好 在线教育课程,找到最合适阅读的帖子 巴诺与NOOK快照 亚马逊,让数据的价值再大一点 移动运营商与数据再利用
Facebook,估价从66亿到1040亿
《大数据时代》读书笔记
制作:@天天向Qian前
大数据时代 之拥抱
其次就要 转变数据价值的获取方式
02 挖掘数据价值的商业变革
‒ 数据的价值来源于万物数据化和数据交叉复用
‒ 大数据时代的重要价值在数据深挖掘
《大数据时代》读书笔记
制作:@天天向Qian前
大数据时代 的商业变革
01 数据化
一切皆可量化 数据交叉复用
可能的3大变革
变革1:个人隐私保护,从个人许可到让数据使用者承担责任 变革2:个从动因VS预测分析,为行为而不是为倾向负责 变革3:设立内部与外部算法师去监测数据的合法使用
读书笔记PPT-028《大数据时代》-@天天向Qian前-秋叶PPT
大数据时代
相关关系大放异彩
小数据时代
相关关系是有用的
大数据的核心:建立在相关关系分析基础上的预测。
相关关系是:A与B经常一起发生。只要注意到B发生,就能预测A的发生。
本章的例子
沃尔玛把蛋挞与飓风用品摆一起 FICO能预测个人的行为 美国折扣零售商塔吉特与怀孕预测 二手车质量预测 UPS与汽车修理预测 大数据预测早产儿病情 幸福感的非线性关系 纽约大型沙井盖爆炸预测
《大数据时代》读书笔记
制作:@天天向Qian前
大数据时代 的思维变革
02更杂
不是精确性 而是混杂性
大数据时代
追求大量数据,允许不精确的数据
小数据时代
因信息量少,对数据精确性更苛刻
随着数据量的增加,数据错误率也增加,格式也存在不一致 只有5%的数据是结构化且适用传统统计方法,95%的数据是非结构化。
睡眠活动数据库与睡眠模式预测
GPS感应器,判断环境因素对哮喘病的影响
《大数据时代》读书笔记
制作:@天天向Qian前
大数据时代 的商业变革
02 价值
取之不尽,用 之不竭的创新
真实价值
隐藏在冰山之下
数据价值
不会随使用次数而减少,可以重复挖掘
数据的潜在价值主要通过前3种方式释放:
01.数据再利用 04 . 数据的折旧值
大数据是合理决策的有力武器
应用不当
会变成损害民众利益的工具
大数据时代,告知与许可、模糊化和匿名化三大隐私保护策略都失效! 挣脱大数据的困境,是大数据时代人类共同的战争!
面临的风险
我们的生活处处受到监视
人们可能因为将做而受惩罚 想象中XX“苍井老湿”也要受罚?
“互联网+”大数据背景下的汉语时政术语翻译
“互联网+”大数据背景下的汉语时政术语翻译作者:廖倩殷兰崔月张娟来源:《校园英语·上旬》2021年第10期【摘要】研究以“互联网+”大数据为依托,探讨汉语时政术语英译策略问题。
研究通过自建汉语时政术语双语语料库,采用量化对比与描述性分析的方法,概括出其英译路径和策略。
研究发现,汉语时政术语翻译应根据英汉语言中术语含义、术语构成、術语修辞的使用三个方面采取不同的翻译策略,便于目的语读者的理解。
【关键词】互联网+;大数据;语料库;汉语时政术语;英译策略【Abstract】Based on “internet plus” and big data, the research concentrates on the discussion of the English translation strategy of Chinese political terms. Under the condition of building a bilingual corpus of Chinese political terms, this paper summarizes the translation strategies of Chinese political terms by means of quantitative comparison and descriptive analysis. By researching, it concludes that the translation of Chinese political terms should adopt different strategies according to three different aspects in meaning, form and rhetoric in both English language and Chinese language, so as to the target language reader can understand.【Key words】Internet plus; big data; corpus; Chinese political terms; English translation strategies【作者简介】廖倩,女,江西科技师范大学外国语学院本科翻译专业;殷兰,女,江西科技师范大学外国语学院本科翻译专业;崔月,女,江西科技师范大学本科翻译专业;张娟,女,江西科技师范大学外国语学院本科英语师范专业。
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Era of Big Data is a woman's age; women in the gene can accumulate and deal with big data/ women are born to accumulate and deal with big data.Many men and children, in fact, have been wondering about this special ability of women. Like, as a child, just as soon as you entered the house your Mother said immediately in a suspicious tone: “Liu zhijun, you didn’t do well in the exam today, did you.” Another example, you just have a glance at the mobile phone, your wife laughs: “Does Er gou the next door ask you to play games?” One more ex ample, when you close the door and make a phone call, your girlfriend will cry: “Who are shot in bed?”They are sometimes right, sometimes wrong. However, On the whole, the accuracy rate is higher than chance level. When they are wrong, men would sneer women always give way to foolish fancies; when they are right, men would say women are sensitive animal maybe with more acute sensory organs.Anyway, that is a guess. It has already scared man that overall accuracy rate is higher than the random level. In order to adapt to this point, the male also developed a very strong skills against reconnaissance. This part is beyond the scope of this article, so no more details about it.Some studies, such as Hanna Holmes’s paper, have indi cated that the white matter of the female’s brain is higher than that of the male. So they have very strong imagination of connecting things together. Some recent studies have shown that women are better than men in the "date" memory. That is the reason why they are able to remember all the birthdays, anniversaries, and even some of the great day of unimportant friends.No matter whether these results are true or not, I am afraid that this is not women's most outstanding ability. Women's most remarkable ability is a long-term tracking of some seemingly unimportant data to form their own baseline and pattern. Once the patterns of these data points are significantly different from the baseline she is familiar with, she knows something unusual. In their daily life, women do not consider the difference between causality and correlation. They believe in the principle: "There must be something wrong out of something unusual."People who talk about big data often take Lin Biao as an example. Lin Biao recorded some detailed and unimportant data after a battle. Such as seized guns, the proportion of rifles and pistols, the age levels of war prisoners, seized grain, whether they are sorghum or millet, etc., all of which were unavoidably recorded in the book. Others laughed at him. But later, he determined where the enemy headquarters were according to these data.What women do is almost the same. A girl A has a secret crush on boy B, but she usually doesn’t contact him directly. Two days later, I asked her if she wanted to ask him to have dinner together. She said he was playing. I wondered “how do you know that?” She said that boy B usually is on the line Gmail at 8:00 am, away status at8:30am, for he goes out to buy coffee and breakfast, on line again at 9:00am, busy status, for he is at work, away again at12:30am for lunch, on line for whole evenings, maybe for reading or playing games. His buddy C is on line at10:00 am, still online till 2:00am next day. He is a boy who gets up late and stays up late. His buddy D is on line for the most of the day. However, the most important pattern is that there are 2-3 days per week, during which they would be offline or away for 3-4 hours together. Conclusion: they are playing together.I am convinced completely. I said “you are so great; this is big data” Some people say that it is really boring and why not ask directly? Of course it doesn’t matter to ask some little things in life. But is it an outstanding ability to get answers to things with big data, which are not appropriate to ask in social situations?Recently there are several articles on predicting people’ IQ and interest, etc. through data mining the regularity of people showing praise in social networks. In fact, women often do such kind of things. Which girl dares to say that she never Google all guests before parties and never thoroughly check the blog, microblogging and Facebook of both the other party and relatives and friends at the start of a relationship?I did it anyway. In the Information Age, I do such a thing without the slightest sense of shame and never consider it as waste of time. Making friends and getting into a relationship are more important than buying a house and a car, which have a more profound impact, so it is very important to do background investigation, especially of strangers.It is far away from the point, now let’s back to how your mother found you did not do wellin the test, how your wife knew you wanted to go out to play games, how your girlfriend suspected that you had Mistress. Every day, they observe what you look at, how many seconds you keep looking, how long you spend on washing your face and brushing your teeth, how often you have a shave, where you put your slippers and how much you talk at the dinner table.Someday, you gaze at cell phone longer than before, squeeze the toothpaste to the edge of the pool suddenly, have an unexpected shave on a common occasion, put the slippers neatly, have no word at the table and enter another room soon and quietly after dinner, meanwhile closing the door lightly.All these patterns together means "there must be something wrong out of something unusual." When you, as a child, have a small mind, your mother is always the first to guess. She always said proud ly: I gave birth to you, so how can’t I know what you are thinking about? In fact, the real trick is not because she gave birth to you but because she loves you. She has been watching you carefully and mentally recording all kinds of your biological signals, finally achieving such a marvelous degree. No sensor and algorithm can reach the level of the mother. But I do hope there will be sensor and algorithm close to the mother's intimacy in the future to bring people real convenience in the data Era.I can’t think any further and want to go to bed immediately. Let me conclude it in two sentencesFirst, women should believe in their ability of observing things in minute detail and big data capacity and use them to deal with meaningful things of higher level. I am sure they will have more powerful competitiveness in this age.Second, Mom, I love you!。