Web mining
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This document discusses different types of web mining including web usage mining, web content mining, and web structure mining. It provides examples of web usage mining including tracking user browsing behavior and usage patterns to target customers and enhance experiences. Web structure mining aims to discover the link structure of websites and identify related pages. It describes techniques like PageRank and HITS algorithm. The document also provides a practical example of analyzing usage data according to demographics and a challenges section discussing issues around web scale and diversity.
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Web mining
This document outlines a presentation on web mining. It begins with an introduction comparing data mining and web mining, noting that web mining extracts information from the world wide web. It then discusses the reasons for and types of web mining, including web content, structure, and usage mining. The document also covers the architecture and applications of web mining, challenges, and provides recommendations.
Web Usage Pattern
This document discusses web usage mining. It begins by defining web mining and its three categories: web content mining, web structure mining, and web usage mining. The main focus is on web usage mining, which involves discovering user navigation patterns and predicting user behavior. The key processes of web usage mining are preprocessing raw data, pattern discovery using algorithms, and pattern analysis. Pattern discovery techniques discussed include statistical analysis, clustering, classification, association rules, and sequential patterns. Potential applications are personalized recommendations, system improvements, and business intelligence. The document concludes by discussing future research directions such as usage mining on the semantic web and analyzing discovered patterns.
Web mining
This document presents an overview of web mining techniques. It discusses how web mining uses data mining algorithms to extract useful information from the web. The document classifies web mining into three categories: web structure mining, web content mining, and web usage mining. It provides examples and explanations of techniques for each category such as document classification, clustering, association rule mining, and sequential pattern mining. The document also discusses opportunities and challenges of web mining as well as sources of web usage data like server logs.
Web mining (1)
This document discusses web mining and its various types. Web mining involves using data mining techniques to discover useful information from web documents and usage patterns. It can involve content mining of text, images, video and audio to extract useful information. It also includes structure mining, which analyzes the hyperlink structure between documents and within documents. Additionally, web usage mining analyzes log files from web servers and applications to discover interesting usage patterns. The document outlines the differences between traditional data mining and web mining. It provides examples of applications of web mining such as information retrieval, network management and e-commerce.
Web mining
This document discusses web mining and its various types and applications. It defines web mining as the extraction of useful information from web documents and services. There are three main types of web mining: web content mining analyzes the content of web pages, web structure mining examines the link structure between pages, and web usage mining studies user access patterns by analyzing server logs. The challenges and pros of web mining are also covered, along with its applications in areas like e-commerce, business intelligence, and knowledge management.
Web mining slides
This document discusses web mining and outlines its goals, types, and techniques. Web mining involves examining data from the world wide web and includes web content mining, web structure mining, and web usage mining. Content mining analyzes web page contents, structure mining analyzes hyperlink structures, and usage mining analyzes web server logs and user browsing patterns. Common techniques discussed include page ranking algorithms, focused crawlers, usage pattern discovery, and preprocessing of web server logs.
Web usage mining
This document provides an overview of web usage mining. It discusses that web usage mining applies data mining techniques to discover usage patterns from web data. The data can be collected at the server, client, or proxy level. The goals are to analyze user behavioral patterns and profiles, and understand how to better serve web applications. The process involves preprocessing data, pattern discovery using methods like statistical analysis and clustering, and pattern analysis including filtering patterns. Web usage mining can benefit applications like personalized marketing and increasing profitability.
Web mining
Presentation on Web Mining Or Data Mining. Data mining is the process of discovering insightful, interesting, and novel patterns, as well as descriptive, understandable and predictive models from large-scale data. data analysis aims to explore the numeric and categorical attributes of the data individually or jointly to extract key characteristics of the data sample via statistics that give information about the centrality, dispersion, and so on.
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This document discusses web usage mining. It begins by defining web mining and its three categories: web content mining, web structure mining, and web usage mining. The main focus is on web usage mining, which involves discovering user navigation patterns and predicting user behavior. The key processes of web usage mining are preprocessing raw data, pattern discovery using algorithms, and pattern analysis. Pattern discovery techniques discussed include statistical analysis, clustering, classification, association rules, and sequential patterns. Potential applications are personalized recommendations, system improvements, and business intelligence. The document concludes by discussing future research directions such as usage mining on the semantic web and analyzing discovered patterns.
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This document presents an overview of web mining techniques. It discusses how web mining uses data mining algorithms to extract useful information from the web. The document classifies web mining into three categories: web structure mining, web content mining, and web usage mining. It provides examples and explanations of techniques for each category such as document classification, clustering, association rule mining, and sequential pattern mining. The document also discusses opportunities and challenges of web mining as well as sources of web usage data like server logs.
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This document discusses web mining and its various types. Web mining involves using data mining techniques to discover useful information from web documents and usage patterns. It can involve content mining of text, images, video and audio to extract useful information. It also includes structure mining, which analyzes the hyperlink structure between documents and within documents. Additionally, web usage mining analyzes log files from web servers and applications to discover interesting usage patterns. The document outlines the differences between traditional data mining and web mining. It provides examples of applications of web mining such as information retrieval, network management and e-commerce.
Web mining
This document discusses web mining and its various types and applications. It defines web mining as the extraction of useful information from web documents and services. There are three main types of web mining: web content mining analyzes the content of web pages, web structure mining examines the link structure between pages, and web usage mining studies user access patterns by analyzing server logs. The challenges and pros of web mining are also covered, along with its applications in areas like e-commerce, business intelligence, and knowledge management.
Web mining slides
This document discusses web mining and outlines its goals, types, and techniques. Web mining involves examining data from the world wide web and includes web content mining, web structure mining, and web usage mining. Content mining analyzes web page contents, structure mining analyzes hyperlink structures, and usage mining analyzes web server logs and user browsing patterns. Common techniques discussed include page ranking algorithms, focused crawlers, usage pattern discovery, and preprocessing of web server logs.
Web usage mining
This document provides an overview of web usage mining. It discusses that web usage mining applies data mining techniques to discover usage patterns from web data. The data can be collected at the server, client, or proxy level. The goals are to analyze user behavioral patterns and profiles, and understand how to better serve web applications. The process involves preprocessing data, pattern discovery using methods like statistical analysis and clustering, and pattern analysis including filtering patterns. Web usage mining can benefit applications like personalized marketing and increasing profitability.
Web mining
Presentation on Web Mining Or Data Mining. Data mining is the process of discovering insightful, interesting, and novel patterns, as well as descriptive, understandable and predictive models from large-scale data. data analysis aims to explore the numeric and categorical attributes of the data individually or jointly to extract key characteristics of the data sample via statistics that give information about the centrality, dispersion, and so on.
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This document provides an overview of web mining and summarizes key concepts. It begins with definitions of data mining and web mining. The document then discusses three categories of web mining: web content mining, web usage mining, and web structure mining. Various matrix expressions used to represent web data are also introduced, including document-keyword co-occurrence matrices, adjacent matrices, and usage matrices. Finally, two common similarity functions - Pearson correlation coefficient and cosine similarity - are outlined.
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The document discusses web content mining. It covers topics such as web content data structure including unstructured, semi-structured, and structured data. It also discusses techniques used for web content mining such as classification, clustering, and association. Finally, it provides examples of applications such as structured data extraction, sentiment analysis of reviews, and targeted advertising.
WEB MINING.
Web mining is the application of data mining techniques to extract knowledge from web data. There are three types of web mining: web usage mining analyzes server logs to learn about user behavior; web structure mining analyzes the hyperlink structure between websites; and web content mining analyzes the contents of web pages. Web mining has various applications in areas like e-commerce, advertising, search engines, and CRM to improve business decisions by understanding customer behavior and targeting customers. It allows businesses to increase sales, optimize websites, and gain marketing intelligence.
Web mining
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This document discusses personal web usage mining, which involves analyzing individual user's web browsing and navigation data recorded on the client side, rather than server side web logs. It proposes recording both remote activities sent to web servers as well as local on-desktop activities in an activity log. This log, along with cached web pages, would be stored and processed in a data warehouse to facilitate data mining and the development of tools and applications to understand users' interests and enhance their web experience.
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This document provides an overview of web mining. It defines web mining as using data mining techniques to automatically discover and extract information from web documents and services. It discusses the differences between web mining and data mining, and covers the main topics in web mining including web graph analysis, structured data extraction, and web advertising. It also describes the different approaches of web content mining, web structure mining, and web usage mining.
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This document discusses web mining and algorithms for analyzing link structure on the web. It defines web mining as the process of discovering useful information from web data. There are three categories of web mining: web content mining, web structure mining, and web usage mining. Two important algorithms for analyzing hyperlink structure are HITS and PageRank. HITS identifies authoritative and hub pages, while PageRank calculates the importance of pages based on the number and quality of inbound links. The document provides details on how these algorithms work and potential applications.
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This document provides an overview of web mining, which involves applying data mining techniques to discover patterns from data on the world wide web. It begins by defining web mining and presenting a taxonomy that distinguishes between web content mining and web usage mining. Web content mining involves discovering information from web sources, while web usage mining involves analyzing user browsing patterns. The document then surveys research on pattern discovery techniques applied to web transactions, analyzing discovered patterns, and architectures for web usage mining systems. It concludes by outlining open research directions in areas like data preprocessing, the mining process, and analyzing mined knowledge.
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The document discusses data mining and web mining. It defines data mining as extracting knowledge from large amounts of data, and notes that web mining applies data mining techniques to extract knowledge from web data, including web documents, hyperlinks, and usage logs. Web mining consists of three types: content mining, structure mining, and usage mining. Structure mining involves discovering structure information from the web, either at the intra-page or inter-page level by analyzing the links between pages. Web structure mining can help understand the relationships between different parts of a website.
Web Mining Presentation Final
The document presents an overview of web mining, which is defined as the application of data mining techniques to extract knowledge from web data, including web content, structure, and usage data. It discusses approaches to web content mining such as using databases or intelligent agents. Specific problems addressed in web content mining are also outlined, such as data extraction, schema matching, and opinion extraction. The document then describes approaches to web content mining including using multilevel databases, web query systems, and information filtering/categorization agents.
Web mining
The document discusses web mining including extracting information from web documents and services. It describes challenges of web mining like noisy and dynamic web data. It then outlines the data flow system including developing dimension tables for date, time of day, visitor, object, referrer, status, browser, and operating system. Finally, it specifies reports on statistics of visits, most/least popular pages, visitor locations, frequent visitors, top referrers/keywords, and most used browsers and operating systems.
Web mining
The document discusses web mining, which involves applying data mining techniques to discover useful information and patterns from web data. It covers the types of web data, various applications of web mining, challenges, and different techniques used. These include classification, clustering, association rule mining. It also discusses how web mining can be used to solve search engine problems and how cloud computing provides a new approach for web mining through software as a service.
Web mining
it gives you short and brief information about web mining and its different types.it will be helpful to understand web mining and its appliction well. it contains particular topic well with some slides
5463 26 web mining
This document discusses different types of web mining techniques. It begins by defining web mining as the application of data mining techniques to discover and extract information from web data. The three main types of web mining are discussed as web content mining, web structure mining, and web usage mining. Web content mining involves mining the actual contents within web pages and documents. Web structure mining mines the hyperlink structure of websites to determine how web pages are linked together. Web usage mining mines web server logs to discover user browsing patterns and behaviors.
Web mining
Web mining applies data mining techniques to web documents and services to extract knowledge. It aims to make the web more useful and profitable by increasing efficiency of interaction. Web mining includes web usage mining, web structure mining, and web content mining to discover useful information from web contents, links, and usage data. Analysis of web server logs can reveal patterns like popular pages and how users navigate a site. This information can then be used to improve site performance and design, detect intrusions, predict user behavior, and enhance personalization.
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This document summarizes a survey on web usage mining techniques presented by Mr. Abdul Rahaman Wahab Sait from Shaqra University in Saudi Arabia and Dr. Meyappan from Alagappa University in India. It introduces web usage mining and its role in determining customer behavior patterns from web data to help e-businesses. It then describes common web usage mining techniques like association rules, clustering, and classification. The document reviews past research applying clustering and classification algorithms to web usage mining and concludes that these techniques can help create intelligent websites, but further study is still needed to develop new automated techniques.
Web mining
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Web mining (structure mining)
This document discusses web structure mining and related concepts. It defines web mining as applying data mining techniques to discover patterns from the web using web content, structure, and usage data. Web structure mining analyzes the hyperlinks between pages to discover useful information. Key aspects covered include the bow-tie model of the web graph, measures of in-degree and out-degree, Google's PageRank algorithm, the HITS algorithm for identifying hub and authority pages, and using link structure for applications like ranking pages and finding related information.
Web mining
Web mining involves applying data mining techniques to automatically discover and extract information from web documents and services. It has three main types: web content mining, which extracts useful information from web document contents; web structure mining, which analyzes the hyperlink structure of websites; and web usage mining, which involves discovering patterns from user interactions on websites. Popular algorithms for web mining include PageRank for web structure mining and HITS for determining both hub and authority pages.
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This document provides an overview of web mining and summarizes key concepts. It begins with definitions of data mining and web mining. The document then discusses three categories of web mining: web content mining, web usage mining, and web structure mining. Various matrix expressions used to represent web data are also introduced, including document-keyword co-occurrence matrices, adjacent matrices, and usage matrices. Finally, two common similarity functions - Pearson correlation coefficient and cosine similarity - are outlined.
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Web mining tools based on content mining,usage mining and structure mining. Tools : Tableau,R, Octoparse , Scrapy, Hits and Pagerank algo. also included.
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The document discusses web content mining. It covers topics such as web content data structure including unstructured, semi-structured, and structured data. It also discusses techniques used for web content mining such as classification, clustering, and association. Finally, it provides examples of applications such as structured data extraction, sentiment analysis of reviews, and targeted advertising.
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Web mining is the application of data mining techniques to extract knowledge from web data. There are three types of web mining: web usage mining analyzes server logs to learn about user behavior; web structure mining analyzes the hyperlink structure between websites; and web content mining analyzes the contents of web pages. Web mining has various applications in areas like e-commerce, advertising, search engines, and CRM to improve business decisions by understanding customer behavior and targeting customers. It allows businesses to increase sales, optimize websites, and gain marketing intelligence.
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Web mining is the application of data mining techniques to discover patterns from the World Wide Web. As the name proposes, this is information gathered by mining the web
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This document discusses personal web usage mining, which involves analyzing individual user's web browsing and navigation data recorded on the client side, rather than server side web logs. It proposes recording both remote activities sent to web servers as well as local on-desktop activities in an activity log. This log, along with cached web pages, would be stored and processed in a data warehouse to facilitate data mining and the development of tools and applications to understand users' interests and enhance their web experience.
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Web content mining mines data from web pages including text, images, audio, video, metadata and hyperlinks. It examines the content of web pages and search results to extract useful information. Web content mining helps understand customer behavior, evaluate website performance, and boost business through research. It can classify data into structured, unstructured, semi-structured and multimedia types and applies techniques such as information extraction, topic tracking, summarization, categorization and clustering to analyze the data.
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This document provides an overview of web mining. It defines web mining as using data mining techniques to automatically discover and extract information from web documents and services. It discusses the differences between web mining and data mining, and covers the main topics in web mining including web graph analysis, structured data extraction, and web advertising. It also describes the different approaches of web content mining, web structure mining, and web usage mining.
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This document discusses web mining and algorithms for analyzing link structure on the web. It defines web mining as the process of discovering useful information from web data. There are three categories of web mining: web content mining, web structure mining, and web usage mining. Two important algorithms for analyzing hyperlink structure are HITS and PageRank. HITS identifies authoritative and hub pages, while PageRank calculates the importance of pages based on the number and quality of inbound links. The document provides details on how these algorithms work and potential applications.
Webmining ppt
This document provides an overview of web mining, which involves applying data mining techniques to discover patterns from data on the world wide web. It begins by defining web mining and presenting a taxonomy that distinguishes between web content mining and web usage mining. Web content mining involves discovering information from web sources, while web usage mining involves analyzing user browsing patterns. The document then surveys research on pattern discovery techniques applied to web transactions, analyzing discovered patterns, and architectures for web usage mining systems. It concludes by outlining open research directions in areas like data preprocessing, the mining process, and analyzing mined knowledge.
Web mining
The document discusses data mining and web mining. It defines data mining as extracting knowledge from large amounts of data, and notes that web mining applies data mining techniques to extract knowledge from web data, including web documents, hyperlinks, and usage logs. Web mining consists of three types: content mining, structure mining, and usage mining. Structure mining involves discovering structure information from the web, either at the intra-page or inter-page level by analyzing the links between pages. Web structure mining can help understand the relationships between different parts of a website.
Web Mining Presentation Final
The document presents an overview of web mining, which is defined as the application of data mining techniques to extract knowledge from web data, including web content, structure, and usage data. It discusses approaches to web content mining such as using databases or intelligent agents. Specific problems addressed in web content mining are also outlined, such as data extraction, schema matching, and opinion extraction. The document then describes approaches to web content mining including using multilevel databases, web query systems, and information filtering/categorization agents.
Web mining
The document discusses web mining including extracting information from web documents and services. It describes challenges of web mining like noisy and dynamic web data. It then outlines the data flow system including developing dimension tables for date, time of day, visitor, object, referrer, status, browser, and operating system. Finally, it specifies reports on statistics of visits, most/least popular pages, visitor locations, frequent visitors, top referrers/keywords, and most used browsers and operating systems.
Web mining
The document discusses web mining, which involves applying data mining techniques to discover useful information and patterns from web data. It covers the types of web data, various applications of web mining, challenges, and different techniques used. These include classification, clustering, association rule mining. It also discusses how web mining can be used to solve search engine problems and how cloud computing provides a new approach for web mining through software as a service.
Web mining
it gives you short and brief information about web mining and its different types.it will be helpful to understand web mining and its appliction well. it contains particular topic well with some slides
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This document discusses different types of web mining techniques. It begins by defining web mining as the application of data mining techniques to discover and extract information from web data. The three main types of web mining are discussed as web content mining, web structure mining, and web usage mining. Web content mining involves mining the actual contents within web pages and documents. Web structure mining mines the hyperlink structure of websites to determine how web pages are linked together. Web usage mining mines web server logs to discover user browsing patterns and behaviors.
Web mining
Web mining applies data mining techniques to web documents and services to extract knowledge. It aims to make the web more useful and profitable by increasing efficiency of interaction. Web mining includes web usage mining, web structure mining, and web content mining to discover useful information from web contents, links, and usage data. Analysis of web server logs can reveal patterns like popular pages and how users navigate a site. This information can then be used to improve site performance and design, detect intrusions, predict user behavior, and enhance personalization.
A survey on web usage mining techniques
This document summarizes a survey on web usage mining techniques presented by Mr. Abdul Rahaman Wahab Sait from Shaqra University in Saudi Arabia and Dr. Meyappan from Alagappa University in India. It introduces web usage mining and its role in determining customer behavior patterns from web data to help e-businesses. It then describes common web usage mining techniques like association rules, clustering, and classification. The document reviews past research applying clustering and classification algorithms to web usage mining and concludes that these techniques can help create intelligent websites, but further study is still needed to develop new automated techniques.
Web mining
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This document discusses web structure mining and related concepts. It defines web mining as applying data mining techniques to discover patterns from the web using web content, structure, and usage data. Web structure mining analyzes the hyperlinks between pages to discover useful information. Key aspects covered include the bow-tie model of the web graph, measures of in-degree and out-degree, Google's PageRank algorithm, the HITS algorithm for identifying hub and authority pages, and using link structure for applications like ranking pages and finding related information.
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This document discusses web mining and its taxonomy. Web mining is a type of data mining that involves discovering patterns from web data. There are three types of web mining: web content mining which extracts information from web documents, web structure mining which analyzes the link structure between pages, and web usage mining which examines log files to understand user behavior. Web mining has advantages like identifying criminal activity and improving searches, but also disadvantages like invading privacy and requiring large amounts of data. It has applications in areas like e-banking, search engines, auctions, e-learning, and e-commerce.
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More from Vijay Yadav
Decision tree presentation
This document discusses decision trees, which are supervised learning algorithms used for both classification and regression. It describes key decision tree concepts like decision nodes, leaves, splitting, and pruning. It also outlines different decision tree algorithms (ID3, C4.5, CART), attribute selection measures like Gini index and information gain, and the basic steps for implementing a decision tree in a programming language.
Summary of Knowledge creating company article
The document discusses knowledge creating companies and how they operate differently than Western companies. Knowledge creating companies view themselves as living organisms rather than machines, with a shared identity and purpose. They focus on consistently creating and disseminating new knowledge throughout the organization. This knowledge includes both explicit knowledge that can be easily articulated, as well as tacit knowledge which is highly personal and difficult to communicate. Knowledge is created through processes of externalization, internalization, and using metaphors to express tacit knowledge. Redundancy and ambiguity can also help spark new knowledge creation during times of crisis. The newly created knowledge must embody and further the company's strategic goals and vision.
Hospital and-medical-store-management-system
The document proposes automating traditional paper-based hospital and medical store record management. It aims to reduce human effort, integrate management in software, and avoid losing information. The proposed system would handle real-life hospital scenarios like patient details, history, appointments, billing, room availability, and the pharmacy. It would be accessed by administrators and hospital staff, and developed using C# and MySQL after collecting data through surveys, observations, analysis, and publications.
Software reuse slide
This document presents a generic cost model for software reuse that accounts for a variety of factors. It considers different investment cycles, economic functions, cost factors, organizational viewpoints, and hypotheses. The model evaluates reuse decisions at the corporate, domain engineering, application engineering, and component engineering levels. It measures costs, benefits, and savings over time to calculate net present value, return on investment, and other economic metrics. By accounting for many variables, the model aims to help organizations thoroughly evaluate the costs and benefits of software reuse initiatives.
File system performance
Disk performance is critical for overall system performance as disks are much slower than other components. Two key techniques for improving disk performance are block caching and block read ahead. Block caching stores frequently accessed disk blocks in faster RAM. Block read ahead preemptively loads sequential blocks from disk into cache before they are requested. Example file systems discussed include CD-ROM (using ISO 9660), MS-DOS (using FAT16), and UNIX (using a hierarchical structure with everything treated as a file).
Client server
Clients are PCs or workstations that rely on servers for resources like files, devices and processing power. Servers are powerful computers dedicated to managing disk drives, printers or network traffic. In a client-server architecture, server software accepts requests from client software and returns the results, with a two-tier architecture having a single server supporting an unlimited number of thin or fat clients, while a three-tier architecture separates processes across three logical layers executed on different processors.
Cricket database
This document summarizes a cricket database project that allows users to view, add, update, and remove player profiles. The project uses HTML and CSS for the front end and a backend database to store player data including team, country, birthplace, role, averages, economy, batting style, and more. There is a need for the system to provide public information on players. It could benefit school cricket clubs to organize teams and understand players.
Full text and relational search
The document discusses full-text search and relational search. It notes that full-text search involves indexing documents and building a list of search terms, while relational search allows for more accurate and relevant searches of complex business data by accounting for relationships between database elements. However, relational search is more difficult due to the complicated nature of enterprise data and the need for 100% accuracy and fast search times when working with business-critical information.
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Recently uploaded
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理
毕业原版【微信:41543339】【(Coventry毕业证书)考文垂大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
原版一模一样【微信：741003700 】【(uts毕业证书)悉尼科技大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness in Kansas City, MO
To: Tom Tresser
From: Roger Warren
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Analysis insight about a Flyball dog competition team's performance
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
The Building Blocks of QuestDB, a Time Series Database
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Intelligence supported media monitoring in veterinary medicine
Media monitoring in veterinary medicien
The Ipsos - AI - Monitor 2024 Report.pdf
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
University of New South Wales degree offer diploma Transcript
澳洲UNSW毕业证书制作新南威尔士大学假文凭定制Q微168899991做UNSW留信网教留服认证海牙认证改UNSW成绩单GPA做UNSW假学位证假文凭高仿毕业证申请新南威尔士大学University of New South Wales degree offer diploma Transcript
Learn SQL from basic queries to Advance queries
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
毕业原版【微信:176555708】【(UCSF毕业证书)旧金山分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
State of Artificial intelligence Report 2023
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
machine learning
apache nifi
apache pulsar
apache kafka
apache flink
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
学校原件一模一样【微信：741003700 】《(英国UCA毕业证书)创意艺术大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Recently uploaded (20)
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
Analysis insight about a Flyball dog competition team's performance
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Web mining
- 1. WEB MINING Shree Ram Rauniyar (072-BCT-536) Suman Kharti (072-BCT-542) Ujjwal Gewali (072-BCT-546) Vijay Yadav (072-BCT-547)
- 2. What is data mining ? 2 Data mining is defined as a process used to extract usable data from a larger set of any raw data.
- 3. What is web mining ? 3 Web mining is an application of data mining techniques to find information patterns from the web data.
- 4. “ TYPES OF WEB MINING 44
- 5. 1. Web usage mining 2. Web content mining 3. Web structure mining
- 7. 7 Discover interesting usage patterns from Web data. Identity or origin of Web users with their browsing behavior. Web Server Data: Collect IP address, page reference and access time etc. Application Server Data: Significant in E-commerce applications e.g. track and log business events Introduction
- 8. Advantages of web Usage Mining 8 ▰ Target potential customers. ▰ Enhance the web surfing experience through personalization. ▰ Identify potential advertisement locations. ▰ Better customer relationship. ▰ Building a strategy for digital marketing. ▰ Identify crime.
- 9. Disadvantages of web Usage Mining 9 ▰ Invasion of privacy. ▰ Trading of personal data.
- 10. Applications 10 ▰ E-Banking ▰ Search Engine ▰ Online Auction ▰ E-Learning ▰ E-commerce
- 11. How much is your data worth ? 11 ▰ $84 - $251 per email address. ▰ Answering a survey : $1 - $5 ▰ Providing feedback over a period of 2-4 weeks on usage of product: $50 - $100 ▰ Allowing insurance company to track your speed, location and acceleration: $100 - $300 depending on car ▰ Facebook earns $45 - $190 and Google earns $5000 per user per year.
- 12. Continue… 12 This 5000 dollars in US means 625 beers per year Or, 352 pizzas (and yes, we are talking about good stuff) So, this is the price we pay unknowingly for using google (per year).
- 13. Some companies making billions through web usage mining
- 14. PRACTICAL EXAMPLE 14 of web usage mining
- 15. Practical example of web usage mining 15 Usage according to country (All data taken from Youtube)
- 16. 16 Usage according to age group
- 17. Usage according to OS & device type 17
- 18. Geographies, traffic sources, gender 18
- 20. 20 Discover the link structure of hyperlink. Mine structure i.e. links, graphs of the web. Identity the web pages are either linked by information or direct link connection. Purpose is to produce structure summary of websites and similar web pages. Introduction
- 21. 21 PageRank CLEVER Techniques for web structure mining
- 22. 22 Quality of pages Interesting Web Structure Web Page Classification Finding related pages Detection of duplicate page Helps in
- 23. 23 Is mathematical formula that judges value of pages Is probability distribution used to represent likelihood that a person randomly clicking on links Developed by Larry Page and Sergey Brin in 1997 PageRank
- 24. 24 PR(A) = (1‐d) + d (PR(T1)/C(T1) + … + PR(Tn)/C(Tn)) D= damping factor(0.85) page A has pages T1…Tn which point to it C(A) = number of links going out of page A Work??
- 25. 25 ▰ The quantity and quality of inbound linking pages. ▰ The number of outbound links on each linking page. ▰ The PageRank of each linking page. Three factors used in PageRank
- 26. 26 System developed by IBM Authoritative Pages: Contain valuable information about pages Hub Pages: Contain links to highly important pages Uses HITS(Hyperlink Induces Topic Search) algorithm CLEVER
- 27. 27 A good hub page is one that points to many good authority pages. Many in links: authority Many out links: hub A good authority page is one that is pointed by many good hub pages. Hubs and Authorities
- 28. PRACTICAL EXAMPLE 28 of web structure mining
- 29. “
- 30. Challenges
- 31. Web is too huge -for finding relevant information Complexity of web pages -support for multimedia files -not just text search
- 32. Diversity in usage Relevancy of information -read tags?? titles ?? urls?? Complex queries and page indexing
- 33. Key problem in information retrieval is how to improve correlation of information.
- 34. Pros and cons Pros Commercial applications Security Efficient search Check authenticity Cons Privacy issues Trading personal data Denial of services based on personal attributes