大数据研究(英文)

基于大数据的用户行为分析与预测研究（英文中文双语版优质文档）I. IntroductionWith the development of Internet technology, people are increasingly inseparable from the Internet, and more and more information is recorded, which is called big data. The mining and analysis of big data is crucial to the decision-making and development of enterprises, and user behavior data is a very important part of it. User behavior data refers to various data generated by users when using products or services, including user access records, click records, purchase records, etc. These data reflect users' needs, interests, and behavioral habits for products or services. Said that these data can be used for user behavior analysis and prediction.2. User Behavior AnalysisUser behavior analysis is to obtain the rules and trends of user behavior through statistics, analysis and mining of user behavior data, so as to provide a basis for enterprise decision-making. User behavior analysis mainly includes the following aspects:1. User interest analysisUser interest analysis refers to the analysis of user behavior data to understand the user's points of interest, so as to recommend personalized products or services for enterprises. Specifically, by analyzing the user's search records, purchase records, browsing records, etc., the user's points of interest and preferences can be obtained, and products or services that better meet the user's needs can be recommended for enterprises.2. User behavior path analysisUser behavior path analysis refers to the analysis of user behavior data to understand the behavior path of users when using products or services, so as to provide better products or services for enterprises. Specifically, by analyzing the user's click records, browsing records, and purchase records in products or services, we can understand the user's behavior path and provide a basis for companies to improve products or services.3. User churn analysisUser churn analysis refers to the analysis of user behavior data to understand the reasons for user churn when using products or services, so as to provide enterprises with directions for improvement. Specifically, through the analysis of users' usage records, access records, evaluation records, etc. in products or services, we can understand the reasons for user loss and provide companies with improvement plans.3. User Behavior PredictionUser behavior prediction refers to the analysis and mining of user behavior data to obtain the future behavior trend of users, so as to provide decision-making basis for enterprises. User behavior prediction mainly includes the following aspects:1. User purchase predictionUser purchase prediction refers to the analysis and mining of behavior data such as user purchase records and browsing records to obtain the user's future purchase trends, so as to formulate better marketing strategies for enterprises. Specifically, by analyzing users' browsing records, click records, purchase records, etc. in products or services, we can understand users' purchasing preferences, purchasing power, purchasing cycle, etc., and provide personalized recommendations and marketing solutions for enterprises.2. User Churn PredictionUser churn prediction refers to the analysis and mining of user behavior data to obtain the trend and reasons for possible user loss in the future, so as to provide preventive measures for enterprises. Specifically, by analyzing the user's usage records, access records, evaluation records, etc. in products or services, we can understand the user's usage habits, satisfaction, etc., and provide personalized services and improvement plans for enterprises.3. User Conversion PredictionUser conversion prediction refers to the analysis and mining of user behavior data to obtain possible conversion trends of users in the future, so as to provide better conversion strategies for enterprises. Specifically, by analyzing users' usage records, browsing records, and click records in products or services, we can understand users' points of interest, conversion intentions, etc., and provide personalized conversion recommendations and services for enterprises.4. Application of Big Data Technology in User Behavior Analysis and PredictionBig data technology is the key to user behavior analysis and prediction. Big data technology can obtain the rules and trends of user behavior through the rapid processing and analysis of massive data, and improve the accuracy and efficiency of user behavior analysis and prediction. Big data technology mainly includes the following aspects:1. Data collectionData acquisition is the first step in big data analysis. Data collection needs to collect various behavioral data generated by users when using products or services, including user access records, click records, purchase records, etc. Data collection can be achieved through various channels, including websites, apps, social media, etc.2. Data storageData storage is an important part of big data analysis. Big data analysis needs to process massive amounts of data, so it needs to store massive amounts of data. Data storage can be implemented in various ways, including relational databases, NoSQL databases, distributed file systems, etc.3. Data cleaningData cleaning is a necessary step in big data analysis. Data cleaning requires deduplication, screening, conversion and other processing of the collected data to ensure the quality and accuracy of the data. Data cleaning can be achieved using various techniques, including ETL tools, data mining tools, etc.4. Data analysisData analysis is the core of big data technology. Data analysis requires statistics, analysis, mining and other processing of the collected data, so as to obtain the rules and trends of user behavior. Data analysis can be achieved using various techniques, including data mining, machine learning, deep learning, etc.5. Visual AnalysisVisual analysis is an important means of big data analysis. Visual analysis can present the analysis results in the form of charts, graphs, etc., and intuitively display the results of data analysis. Visual analysis can be implemented using various tools, including Tableau, Power BI, etc.6. Model buildingModel building is an important part of big data analysis. Model building needs to establish a model based on the collected data and analysis results, verify and optimize it, so as to obtain the prediction results of user behavior. Model building can be achieved using various techniques, including regression analysis, decision trees, neural networks, etc.In short, big data technology has played an important role in user behavior analysis and prediction, providing enterprises with more accurate and finer data analysis and prediction capabilities, thereby helping enterprises better understand user needs, optimize services, improve user experience and Market Competitiveness.一、引言随着互联网技术的发展，人们越来越离不开网络，越来越多的信息被记录下来，这些数据被称为大数据。

常见的大数据术语表（中英文对照版）A聚合（Aggregation） - 搜索、合并、显示数据的过程算法（Algorithms） - 可以完成某种数据分析的数学公式分析法（Analytics） - 用于发现数据的内在涵义异常检测（Anomaly detection） - 在数据集中搜索与预期模式或行为不匹配的数据项。

除了"Anomalies",用来表示异常的词有以下几种：outliers, exceptions, surprises, contaminants.他们通常可提供关键的可执行信息匿名化（Anonymization） - 使数据匿名，即移除所有与个人隐私相关的数据应用（Application） - 实现某种特定功能的计算机软件人工智能（Artificial Intelligence） - 研发智能机器和智能软件，这些智能设备能够感知周遭的环境，并根据要求作出相应的反应，甚至能自我学习B行为分析法（Behavioural Analytics） - 这种分析法是根据用户的行为如"怎么做"，"为什么这么做"，以及"做了什么"来得出结论，而不是仅仅针对人物和时间的一门分析学科，它着眼于数据中的人性化模式大数据科学家（Big Data Scientist） - 能够设计大数据算法使得大数据变得有用的人大数据创业公司（Big data startup） - 指研发最新大数据技术的新兴公司生物测定术（Biometrics） - 根据个人的特征进行身份识别B字节（BB: Brontobytes） - 约等于1000 YB（Yottabytes），相当于未来数字化宇宙的大小。

1 B字节包含了27个0！商业智能（Business Intelligence） - 是一系列理论、方法学和过程，使得数据更容易被理解C分类分析（Classification analysis） - 从数据中获得重要的相关性信息的系统化过程；这类数据也被称为元数据（meta data），是描述数据的数据云计算（Cloud computing） - 构建在网络上的分布式计算系统，数据是存储于机房外的（即云端）聚类分析（Clustering analysis） - 它是将相似的对象聚合在一起，每类相似的对象组合成一个聚类（也叫作簇）的过程。

我对大数据的看法英文作文English: Big data has completely transformed the way businesses operate and make decisions. The immense volume, velocity, and variety of data generated every second provide companies with valuable insights into consumer behavior, market trends, and operational efficiency. With the right tools and technologies, organizations can analyze this data to improve their products and services, identify new business opportunities, and optimize their processes. Big data also plays a crucial role in sectors such as healthcare, finance, and transportation, enabling better decision-making, personalized customer experiences, and predictive analytics. However, the utilization of big data raises concerns about data privacy, security, and ethical uses. As more data is collected and analyzed, it is important for companies to prioritize the protection of sensitive information and adhere to regulations to ensure the responsible handling of data. Overall, big data has the potential to drive innovation, improve decision-making, and enhance the efficiency of various industries, but it is essential to address the challenges associated with its use.中文翻译: 大数据彻底改变了企业运营和决策的方式。

The development and tendency of Big DataAbstract: "Big Data" is the most popular IT word after the "Internet of things" and "Cloud computing". From the source, development, status quo and tendency of big data, we can understand every aspect of it. Big data is one of the most important technologies around the world and every country has their own way to develop the technology.Key words: big data; IT; technology1 The source of big dataDespite the famous futurist Toffler propose the conception of “Big Data” in 1980, for a long time, because the primary stage is still in the development of IT industry and uses of information sources, “Big Data” is not get enough attention by the people in that age[1].2 The development of big dataUntil the financial crisis in 2008 force the IBM ( multi-national corporation of IT industry) proposing conception of “Smart City”and vigorously promote Internet of Things and Cloud computing so that information data has been in a massive growth meanwhile the need for the technology is very urgent. Under this condition, some American data processing companies have focused on developing large-scale concurrent processing system, then the “Big Data”technology become available sooner and Hadoop mass data concurrent processing system has received wide attention. Since 2010, IT giants have proposed their products in big data area. Big companies such as EMC、HP、IBM、Microsoft all purchase other manufacturer relating to big data in order to achieve technical integration[1]. Based on this, we can learn how important the big data strategy is. Development of big data thanks to some big IT companies such as Google、Amazon、China mobile、Alibaba and so on, because they need a optimization way to store and analysis data. Besides, there are also demands of health systems、geographic space remote sensing and digital media[2].3 The status quo of big dataNowadays America is in the lead of big data technology and market application. USA federal government announced a “Big Data’s research and development” plan in March,2012, which involved six federal government department the National Science Foundation, Health Research Institute, Department of Energy, Department of Defense, Advanced Research Projects Agency and Geological Survey in order to improve the ability to extract information and viewpoint of big data[1]. Thus, it can speed science and engineering discovery up, and it is a major move to push some research institutions making innovations.The federal government put big data development into a strategy place, which hasa big impact on every country. At present, many big European institutions is still at the primary stage to use big data and seriously lack technology about big data. Most improvements and technology of big data are come from America. Therefore, there are kind of challenges of Europe to keep in step with the development of big data. But, in the financial service industry especially investment banking in London is one of the earliest industries in Europe. The experiment and technology of big data is as good as the giant institution of America. And, the investment of big data has been maintained promising efforts. January 2013, British government announced 1.89 million pound will be invested in big data and calculation of energy saving technology in earth observation and health care[3].Japanese government timely takes the challenge of big data strategy. July 2013, Japan’s communications ministry proposed a synthesize strategy called “Energy ICT of Japan” which focused on big data application. June 2013, the abe cabinet formally announced the new IT strategy----“The announcement of creating the most advanced IT country”. This announcement comprehensively expounded that Japanese new IT national strategy is with the core of developing opening public data and big data in 2013 to 2020[4].Big data has also drawn attention of China government.《Guiding opinions of the State Council on promoting the healthy and orderly development of the Internet of things》promote to quicken the core technology including sensor network、intelligent terminal、big data processing、intelligent analysis and service integration. December 2012, the national development and reform commission add data analysis software into special guide, in the beginning of 2013 ministry of science and technology announced that big data research is one of the most important content of “973 program”[1]. This program requests that we need to research the expression, measure and semantic understanding of multi-source heterogeneous data, research modeling theory and computational model, promote hardware and software system architecture by energy optimal distributed storage and processing, analysis the relationship of complexity、calculability and treatment efficiency[1]. Above all, we can provide theory evidence for setting up scientific system of big data.4 The tendency of big data4.1 See the future by big dataIn the beginning of 2008, Alibaba found that the whole number of sellers were on a slippery slope by mining analyzing user-behavior data meanwhile the procurement to Europe and America was also glide. They accurately predicting the trend of world economic trade unfold half year earlier so they avoid the financial crisis[2]. Document [3] cite an example which turned out can predict a cholera one year earlier by mining and analysis the data of storm, drought and other natural disaster[3].4.2 Great changes and business opportunitiesWith the approval of big data values, giants of every industry all spend more money in big data industry. Then great changes and business opportunity comes[4].In hardware industry, big data are facing the challenges of manage, storage and real-time analysis. Big data will have an important impact of chip and storage industry,besides, some new industry will be created because of big data[4].In software and service area, the urgent demand of fast data processing will bring great boom to data mining and business intelligence industry.The hidden value of big data can create a lot of new companies, new products, new technology and new projects[2].4.3 Development direction of big dataThe storage technology of big data is relational database at primary. But due to the canonical design, friendly query language, efficient ability dealing with online affair, Big data dominate the market a long term. However, its strict design pattern, it ensures consistency to give up function, its poor expansibility these problems are exposed in big data analysis. Then, NoSQL data storage model and Bigtable propsed by Google start to be in fashion[5].Big data analysis technology which uses MapReduce technological frame proposed by Google is used to deal with large scale concurrent batch transaction. Using file system to store unstructured data is not lost function but also win the expansilility. Later, there are big data analysis platform like HA VEn proposed by HP and Fusion Insight proposed by Huawei . Beyond doubt, this situation will be continued, new technology and measures will come out such as next generation data warehouse, Hadoop distribute and so on[6].ConclusionThis paper we analysis the development and tendency of big data. Based on this, we know that the big data is still at a primary stage, there are too many problems need to deal with. But the commercial value and market value of big data are the direction of development to information age.忽略此处..[1] Li Chunwei, Development report of China’s E-Commerce enterprises, Beijing , 2013,pp.268-270[2] Li Fen, Zhu Zhixiang, Liu Shenghui, The development status and the problems of large data, Journal of Xi’an University of Posts and Telecommunications, 18 volume, pp. 102-103,sep.2013 [3] Kira Radinsky, Eric Horivtz, Mining the Web to Predict Future Events[C]//Proceedings of the 6th ACM International Conference on Web Search and Data Mining, WSDM 2013: New York: Association for Computing Machinery,2013,pp.255-264[4] Chapman A, Allen M D, Blaustein B. It’s About the Data: Provenance as a Toll for Assessing Data Fitness[C]//Proc of the 4th USENIX Workshop on the Theory and Practice of Provenance, Berkely, CA: USENIX Association, 2012:8[5] Li Ruiqin, Zheng Janguo, Big data Research: Status quo, Problems and Tendency[J],Network Application,Shanghai,1994,pp.107-108[6] Meng Xiaofeng, Wang Huiju, Du Xiaoyong, Big Daya Analysis: Competition and Survival of RDBMS and ManReduce[J], Journal of software, 2012,23(1): 32-45。

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.。

大数据英文版Title: The Significance and Impact of Big DataIntroduction:In today's digital age, the term "Big Data" has gained significant attention and importance. Big Data refers to the vast amount of structured and unstructured data that is generated and collected from various sources. It has revolutionized industries across the globe, providing valuable insights and opportunities for businesses, governments, and individuals. This article will delve into the significance and impact of Big Data, exploring five major points and their respective sub-points.Body:1. Enhanced Decision Making:1.1 Improved Accuracy: Big Data enables organizations to make more accurate decisions by analyzing large volumes of data and identifying patterns and trends.1.2 Real-time Analysis: With Big Data, real-time analysis becomes possible, allowing businesses to respond swiftly to changing market dynamics and customer preferences.1.3 Predictive Analytics: Big Data empowers organizations to predict future trends and outcomes, enabling them to make proactive decisions and gain a competitive edge.2. Improved Customer Insights:2.1 Personalization: Big Data helps businesses gain a better understanding of their customers by analyzing their preferences, behavior, and demographics, enabling personalized marketing campaigns and product recommendations.2.2 Enhanced Customer Experience: By leveraging Big Data, organizations can provide a seamless and personalized customer experience, leading to increased customer satisfaction and loyalty.2.3 Targeted Marketing: Big Data enables businesses to target specific customer segments more effectively, resulting in higher conversion rates and improved marketing ROI.3. Cost Reduction and Efficiency:3.1 Operational Efficiency: Big Data analytics helps identify inefficiencies in business processes, enabling organizations to streamline operations and reduce costs.3.2 Resource Optimization: By analyzing data, businesses can optimize resource allocation, minimizing waste and improving overall efficiency.3.3 Fraud Detection: Big Data analytics plays a crucial role in detecting fraudulent activities, reducing financial losses, and enhancing security measures.4. Innovation and New Opportunities:4.1 Product Development: Big Data provides valuable insights into customer needs and preferences, facilitating the development of innovative products and services.4.2 Market Expansion: By analyzing Big Data, organizations can identify new market opportunities and expand their customer base.4.3 Competitive Advantage: Big Data enables businesses to gain a competitive advantage by uncovering market trends, consumer sentiments, and competitor strategies.5. Healthcare and Scientific Advancements:5.1 Disease Prevention and Treatment: Big Data analytics helps identify disease patterns, predict outbreaks, and develop effective prevention and treatment strategies.5.2 Drug Discovery: Big Data plays a vital role in accelerating drug discovery processes by analyzing vast amounts of genetic and clinical data.5.3 Precision Medicine: By analyzing individual patient data, Big Data facilitates personalized treatment plans, improving patient outcomes and reducing healthcare costs.Conclusion:In conclusion, Big Data has emerged as a game-changer in various industries, revolutionizing decision-making processes, customer insights, cost reduction, innovation, and advancements in healthcare and science. Its significance and impact are undeniable, providing organizations with valuable opportunities to gain a competitive edge, improve efficiency, and drive growth. As we continue to generate and collect massive amounts of data, harnessing the power of Big Data will remain crucial for success in the digital era.。

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。

有关大数据的英语作文英文回答：Big Data: Opportunities and Challenges。

Big data refers to the vast and complex data sets that are generated in the digital age. It has become an essential tool for businesses and organizations across a wide range of industries, as it provides valuable insights that can improve decision-making, optimize operations, and drive innovation.Opportunities presented by big data:Improved decision-making: Big data analytics can help organizations identify patterns and trends in complex data sets, which can improve the accuracy and effectiveness of their decision-making processes.Optimized operations: Big data can be used to monitorand analyze operational processes, identify inefficiencies, and develop strategies to improve efficiency and reduce costs.Enhanced customer experiences: By collecting and analyzing data on customer behavior, businesses can gain a deeper understanding of their customers' needs and preferences, which can help them create more personalized and relevant products and services.New products and services: Big data can be used to identify new opportunities for products and services, as well as to develop more innovative offerings that meet the changing needs of customers.Improved risk management: Big data can help organizations identify and mitigate risks by providing insights into potential threats and vulnerabilities.Challenges associated with big data:Data privacy and security: The vast amounts of datacollected and stored by big data systems raise concerns about data privacy and security. Organizations must take appropriate measures to protect sensitive data from unauthorized access and misuse.Data quality and integrity: The quality and integrity of big data can impact the reliability and accuracy of the insights derived from it. It is essential to implement robust data quality management practices to ensure the accuracy and consistency of the data.Data analysis complexity: Big data sets are often complex and difficult to analyze, requiring specialized skills and technologies. Organizations may need to invest in data scientists and data analysts to effectively interpret and derive insights from big data.Data storage and management: Storing and managing large volumes of big data can be challenging and expensive. Organizations must implement scalable and cost-effective storage and management solutions.Ethical considerations: The use of big data raises ethical considerations, such as the potential for discrimination and bias in decision-making. Organizations must use big data responsibly and in a manner that aligns with ethical principles.Conclusion:Big data presents both opportunities and challenges for businesses and organizations. By harnessing the power of big data, organizations can gain valuable insights, optimize operations, and drive innovation. However, it is important to address the challenges associated with big data, such as data privacy and security, data quality and integrity, and ethical considerations. By implementing appropriate data governance and management practices, organizations can unlock the full potential of big data while mitigating the associated risks.中文回答：大数据，机遇与挑战。

关于大数据的英文作文500词英文回答：Big data refers to the massive amount of structured and unstructured data that is generated from various sources such as social media, sensors, and transactions. It has become a buzzword in recent years as it has the potential to revolutionize industries and bring about significant changes in our daily lives.One of the key benefits of big data is its ability to provide valuable insights and predictions. By analyzing large datasets, businesses can gain a deeper understanding of customer behavior, market trends, and operational efficiency. For example, online retailers can use big data to analyze customer browsing and purchase history to personalize product recommendations. This not only improves customer satisfaction but also increases sales.Big data also plays a crucial role in healthcare. Byanalyzing patient records, medical professionals can identify patterns and trends that can help in early disease detection and prevention. This can lead to better treatment outcomes and potentially save lives. Additionally, big data can be used to track the spread of infectious diseases and develop effective strategies for containment.Another area where big data is making a significant impact is transportation. By analyzing data from sensors, cameras, and GPS devices, cities can optimize traffic flow, reduce congestion, and improve public transportation systems. For instance, predictive analytics can be used to anticipate traffic jams and reroute vehicles accordingly, saving commuters valuable time and reducing carbon emissions.Furthermore, big data has transformed the field of finance. Banks and financial institutions can use data analytics to detect fraudulent activities and assess creditworthiness. This not only protects customers from identity theft and financial fraud but also helps businesses make informed lending decisions.中文回答：大数据是指从各种来源如社交媒体、传感器和交易中产生的海量结构化和非结构化数据。

写一篇关于大数据专业的英语作文英文回答：What is Big Data Analytics?In recent years, the term "big data" has become increasingly prevalent in both academic and business circles. But what exactly is big data?Big data refers to extremely large datasets that are often too large to be processed using traditional data processing techniques. These datasets can be structured, semi-structured, or unstructured, and they may come from a variety of sources, such as social media, weblogs, sensors, and financial transactions.The size and complexity of big data poses significant challenges for organizations that want to harness its value. However, big data also offers tremendous opportunities for businesses that can successfully manage and analyze thesedatasets.Benefits of Big Data Analytics.There are many benefits to big data analytics, including:Improved decision-making: Big data analytics can help organizations make better decisions by providing them with insights into their customers, markets, and operations.Increased revenue: Big data analytics can help organizations increase revenue by identifying new opportunities and optimizing their marketing and sales strategies.Improved customer service: Big data analytics can help organizations improve customer service by providing them with a better understanding of their customers' needs and preferences.Reduced costs: Big data analytics can helporganizations reduce costs by optimizing their operations and identifying areas of waste.Enhanced innovation: Big data analytics can help organizations enhance innovation by providing them with new insights into their products, services, and processes.Challenges of Big Data Analytics.While big data analytics offers many benefits, there are also some challenges associated with it. These challenges include:Data storage: Big data datasets are often too large to be stored on traditional hard drives. Organizations must invest in specialized storage solutions, such as cloud storage, to manage these datasets.Data processing: Big data datasets require specialized processing techniques to extract meaningful insights. Organizations must invest in powerful computing resources and software to perform big data analytics.Data security: Big data datasets are often sensitive and must be protected from unauthorized access. Organizations must implement robust security measures to protect these datasets.Data privacy: Big data datasets often contain personal information about individuals. Organizations must comply with privacy regulations to protect this information.Data quality: Big data datasets are often noisy and incomplete. Organizations must invest in data cleaning and data quality management to improve the accuracy of their analytics results.The Future of Big Data Analytics.Big data analytics is still a relatively new field, but it is growing rapidly. As organizations become morefamiliar with the benefits of big data, they will increasingly adopt big data analytics solutions.The future of big data analytics is bright. As technology continues to develop, big data analytics will become more powerful and accessible. This will enable organizations to harness the value of big data to improve their decision-making, increase revenue, improve customer service, reduce costs, and enhance innovation.中文回答：什么是大数据分析？近年来，“大数据”一词在学术界和商业界越来越流行。

Today, I would like to tell you that big data is useful.As we know the core of big data is predicting, to predict what will happen and the risk of what has happen.I believe you will agree with me after these examples.1.理解客户、满足客户服务需求using big data to understand customers’ favorites and meet their demand.Like famous sailor called target, they analyze data and predict when parents want a baby.WAL-MART predict which products will be sold better. the government can understand the preferences of voters比如美国的著名零售商Target通过大数据的分析，精准得预测到客户在什么时候想要小孩。

沃尔玛则更加精准的预测哪个产品会大卖，政府也能了解到选民的偏好。

2.大数据可以为我们省钱using big data can save money for us.3.大数据正在改善我们的生活improving our life.我们可以利用穿戴的装备(如智能手表或者智能手环)生成最新的数据，这让我们可以根据我们热量的消耗以及睡眠模式来进行追踪。

而且还利用利用大数据分析来寻找属于我们的爱情，大多数时候交友网站就是大数据应用工具来帮助需要的人匹配合适的对象。

We can use equipment (such as smart watches or smart bracelet) to generate the latest data, which tells us our calories consumed and sleep quality.And it can also use data analysis to find our love, because most of the dating sites is a big data application to help people to match whom you may like.4.提高医疗和研发To predict decease.According to the searching system, the government could predict decease. doctors may have a prediction of the discomfort of the baby's body. And with data analysis, scientists can decode the whole DNA within several minutes.大数据分析应用的计算能力可以让我们能够在几分钟内就可以解码整个DNA。

大数据英文论文大数据分析是指对规模巨大的数据进行分析。

大数据可以概括为4个V，数据量大(Volume)、速度快(Velocity)、类型多(Variety)、真实性(Veracity)。

大数据作为时下最火热的IT行业的词汇，随之而来的数据仓库、数据安全、数据分析、数据挖掘等等围绕大数据的商业价值的利用逐渐成为行业人士争相追捧的利润焦点。

随着大数据时代的来临，大数据分析也应运而生。

大数据应用与案例分析1. 大数据应用案例之：医疗行业Seton Healthcare是采用IBM最新沃森技术医疗保健内容分析预测的首个客户。

该技术允许企业找到大量病人相关的临床医疗信息，通过大数据处理，更好地分析病人的信息。

在加拿大多伦多的一家医院，针对早产婴儿，每秒钟有超过3000次的数据读取。

通过这些数据分析，医院能够提前知道哪些早产儿出现问题并且有针对性地采取措施，避免早产婴儿夭折。

它让更多的创业者更方便地开发产品，比如通过社交网络来收集数据的健康类App。

也许未来数年后，它们搜集的数据能让医生给你的诊断变得更为精确，比方说不是通用的成人每日三次一次一片，而是检测到你的血液中药剂已经代谢完成会自动提醒你再次服药。

2. 大数据应用案例之：能源行业智能电网现在欧洲已经做到了终端，也就是所谓的智能电表。

在德国，为了鼓励利用太阳能，会在家庭安装太阳能，除了卖电给你，当你的太阳能有多余电的时候还可以买回来。

通过电网收集每隔五分钟或十分钟收集一次数据，收集来的这些数据可以用来预测客户的用电习惯等，从而推断出在未来2~3个月时间里，整个电网大概需要多少电。

有了这个预测后，就可以向发电或者供电企业购买一定数量的电。

因为电有点像期货一样，如果提前买就会比较便宜，买现货就比较贵。

通过这个预测后，可以降低采购成本。

维斯塔斯风力系统，依靠的是BigInsights软件和IBM超级计算机，然后对气象数据进行分析，找出安装风力涡轮机和整个风电场最佳的地点。

关于大数据的学术英文文献Big Data: Challenges and Opportunities in the Digital Age.Introduction.In the contemporary digital era, the advent of big data has revolutionized various aspects of human society. Big data refers to vast and complex datasets generated at an unprecedented rate from diverse sources, including social media platforms, sensor networks, and scientific research. While big data holds immense potential for transformative insights, it also poses significant challenges and opportunities that require thoughtful consideration. This article aims to elucidate the key challenges and opportunities associated with big data, providing a comprehensive overview of its impact and future implications.Challenges of Big Data.1. Data Volume and Variety: Big data datasets are characterized by their enormous size and heterogeneity. Dealing with such immense volumes and diverse types of data requires specialized infrastructure, computational capabilities, and data management techniques.2. Data Velocity: The continuous influx of data from various sources necessitates real-time analysis and decision-making. The rapid pace at which data is generated poses challenges for data processing, storage, andefficient access.3. Data Veracity: The credibility and accuracy of big data can be a concern due to the potential for noise, biases, and inconsistencies in data sources. Ensuring data quality and reliability is crucial for meaningful analysis and decision-making.4. Data Privacy and Security: The vast amounts of data collected and processed raise concerns about privacy and security. Sensitive data must be protected fromunauthorized access, misuse, or breaches. Balancing data utility with privacy considerations is a key challenge.5. Skills Gap: The analysis and interpretation of big data require specialized skills and expertise in data science, statistics, and machine learning. There is a growing need for skilled professionals who can effectively harness big data for valuable insights.Opportunities of Big Data.1. Improved Decision-Making: Big data analytics enables organizations to make informed decisions based on comprehensive data-driven insights. Data analysis can reveal patterns, trends, and correlations that would be difficult to identify manually.2. Personalized Experiences: Big data allows companies to tailor products, services, and marketing strategies to individual customer needs. By understanding customer preferences and behaviors through data analysis, businesses can provide personalized experiences that enhancesatisfaction and loyalty.3. Scientific Discovery and Innovation: Big data enables advancements in various scientific fields,including medicine, genomics, and climate modeling. The vast datasets facilitate the identification of complex relationships, patterns, and anomalies that can lead to breakthroughs and new discoveries.4. Economic Growth and Productivity: Big data-driven insights can improve operational efficiency, optimize supply chains, and create new economic opportunities. By leveraging data to streamline processes, reduce costs, and identify growth areas, businesses can enhance their competitiveness and contribute to economic development.5. Societal Benefits: Big data has the potential to address societal challenges such as crime prevention, disease control, and disaster management. Data analysis can empower governments and organizations to make evidence-based decisions that benefit society.Conclusion.Big data presents both challenges and opportunities in the digital age. The challenges of data volume, velocity, veracity, privacy, and skills gap must be addressed to harness the full potential of big data. However, the opportunities for improved decision-making, personalized experiences, scientific discoveries, economic growth, and societal benefits are significant. By investing in infrastructure, developing expertise, and establishing robust data governance frameworks, organizations and individuals can effectively navigate the challenges and realize the transformative power of big data. As thedigital landscape continues to evolve, big data will undoubtedly play an increasingly important role in shaping the future of human society and technological advancement.。

大数据用英文怎么说Big data is a term that refers to extremely large and complex data sets that cannot be easily managed, processed, or analyzed using traditional methods. In recent years, big data has become a critical element in various fields and industries, providing valuable insights and driving innovation.In the realm of technology and data analysis, the term "big data" is widely recognized and frequently used in English-speaking countries. It has become a commonly accepted phrase, representing the vast amount of structured and unstructured data that is generated and collected on a daily basis.While the term "big data" is widely used, there are also alternative phrases and expressions that can be used to convey the same meaning. These alternative expressions highlight different aspects of big data and may be more suitable depending on the context.One commonly used phrase is "large-scale data analysis." This phrase emphasizes the scale and magnitude of the data being analyzed. It conveys the idea that the data sets are not just large in size, but are also complex and require specialized tools and techniques for analysis.Another expression that can be used is "massive data" or "gigantic data." These terms focus on the sheer volume of data that is being processed. They emphasize the need for advanced technologies and algorithms to handle the immense amount of data in an efficient and effective manner.In addition to these phrases, there are also domain-specific terms that are used in various industries. For example, in the healthcare sector, the term"clinical big data" is often used to describe the large amount of patient-related information that is collected and analyzed for medical research and treatment purposes. Similarly, in the field of finance, the term "financial big data" is used to refer to the vast amount of financial transactions and market data that is analyzed for investment and risk management purposes.In recent years, the field of big data has experienced significant growth and evolution. As technology continues to advance, new terms and expressions are constantly being introduced to describe the various aspects of big data. For example, the terms "data science" and "data analytics" are often used to describe the process of extracting valuable insights from large data sets.In conclusion, the term "big data" is commonly used to describe large and complex data sets in English-speaking countries. However, alternative phrases such as "large-scale data analysis" or "massive data" can also be used to convey the same meaning. Additionally, domain-specific terms may be used in specific industries to describe the unique characteristics of the data being analyzed. As the field of big data continues to evolve, new terms and expressions are constantly being introduced to describe the various aspects of this rapidly growing field.。

对大数据的看法英语作文英文回答：Big data is a term that refers to the large volume of structured and unstructured data that organizations generate on a daily basis. It has become increasingly important in today's digital age as more and more data is being produced and collected. Big data has the potential to revolutionize industries and transform the way businesses operate.One of the key benefits of big data is its ability to provide valuable insights and analysis. By analyzing large datasets, organizations can gain a deeper understanding of customer behavior, market trends, and business operations. For example, a retail company can analyze customer purchase data to identify patterns and preferences, which can then be used to personalize marketing campaigns and improve customer satisfaction.Another advantage of big data is its potential to drive innovation and improve decision-making. With access to vast amounts of data, organizations can identify new opportunities, optimize processes, and make more informed decisions. For instance, a healthcare provider can analyze patient data to identify patterns and trends in disease outbreaks, leading to faster and more effective responses.Furthermore, big data has the power to enhanceefficiency and productivity. By automating data collection and analysis processes, organizations can save time and resources. For example, a manufacturing company can use sensors and data analytics to monitor equipment performance and predict maintenance needs, reducing downtime and improving overall efficiency.However, big data also presents challenges and concerns. One of the main challenges is data privacy and security. With the increasing amount of data being collected, thereis a risk of data breaches and unauthorized access. Organizations must invest in robust security measures to protect sensitive data and ensure compliance with dataprotection regulations.Another challenge is data quality and accuracy. With such large volumes of data, there is a possibility oferrors and inconsistencies. Organizations need to implement data cleansing and validation processes to ensure the accuracy and reliability of the data they analyze.In conclusion, big data has the potential to revolutionize industries and transform the way businesses operate. It provides valuable insights, drives innovation, and enhances efficiency. However, it also presents challenges such as data privacy and quality. Organizations must carefully manage and analyze big data to harness its full potential and mitigate risks.中文回答：大数据是指组织每天产生的大量结构化和非结构化数据。