Pagerank
•Download as PPT, PDF•
0 likes•206 views
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Report
Share
Report
Share
![Contents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
INTRODUCCION A LA FINANZA
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It then provides an overview of PageRank and how it models web pages as probabilities in a random walk model. The document also summarizes how PageRank is implemented and computed, and concludes by discussing applications and limitations of the algorithm.
PAGERANKS
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Pagerank
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Pagerank
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
PAGERANK
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Pagerank
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Pagerank
This document discusses the PageRank algorithm developed by Larry Page for ranking web pages. It introduces the motivation for developing PageRank to bring order to the unstructured web based on link analysis. It then describes the random surfer model that PageRank is based on and how it approximates page importance based on the quantity and quality of backlinks. The document concludes by discussing applications of PageRank and some issues with the algorithm, such as its inability to account for user intent in queries.
Fb101 transcript
This document is a transcript from a Facebook 101 training video. It begins with an introduction to the differences between a Facebook profile and Facebook page. A profile is for personal use and communication with friends and family, while a page is for businesses to engage with customers. It then provides tips for optimizing a business on Facebook, including using friends lists on a profile to privately message contacts for business purposes while maintaining an 80% personal/20% business content ratio. The transcript demonstrates how to set up public and private friends lists and use privacy settings.
Recommended
INTRODUCCION A LA FINANZA
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It then provides an overview of PageRank and how it models web pages as probabilities in a random walk model. The document also summarizes how PageRank is implemented and computed, and concludes by discussing applications and limitations of the algorithm.
PAGERANKS
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Pagerank
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Pagerank
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
PAGERANK
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Pagerank
The document introduces PageRank, an algorithm developed by Larry Page for ranking web pages. It discusses the motivation for developing PageRank to bring order to the unstructured web. It provides an overview of how PageRank models the random surfing behavior of users to determine the importance of pages based on both the number and quality of backlinks. It also summarizes some implementations and applications of PageRank, as well as limitations and areas for further refinement.
Pagerank
This document discusses the PageRank algorithm developed by Larry Page for ranking web pages. It introduces the motivation for developing PageRank to bring order to the unstructured web based on link analysis. It then describes the random surfer model that PageRank is based on and how it approximates page importance based on the quantity and quality of backlinks. The document concludes by discussing applications of PageRank and some issues with the algorithm, such as its inability to account for user intent in queries.
Fb101 transcript
This document is a transcript from a Facebook 101 training video. It begins with an introduction to the differences between a Facebook profile and Facebook page. A profile is for personal use and communication with friends and family, while a page is for businesses to engage with customers. It then provides tips for optimizing a business on Facebook, including using friends lists on a profile to privately message contacts for business purposes while maintaining an 80% personal/20% business content ratio. The transcript demonstrates how to set up public and private friends lists and use privacy settings.
Page rank by university of michagain.ppt
1. PageRank is an algorithm developed by Larry Page and Sergey Brin to rate the importance of web pages by analyzing the link structure of the web. It assumes that more important pages are likely to receive more links from other pages.
2. PageRank models a user randomly clicking on links, getting bored, and randomly jumping to another page, with pages that are frequently visited in this random walk receiving a higher PageRank.
3. PageRank was a key part of the original Google search engine, which used it to help sort search results from most to least relevant. It remains an important algorithm for search engines today.
Ranking Web Pages
The document discusses the history and development of web search engines. It describes how early search engines in 1994 indexed around 100,000 pages while Google grew to index over 8 billion pages by 2005. It also explains the basic components and ranking algorithms of search engines, including PageRank, which calculates the importance of pages based on both the number and quality of inbound links.
Pagerank
This document discusses two algorithms for ranking web pages based on link structure: SALSA and PageRank. SALSA is a stochastic approach that analyzes random walks on graphs derived from link structures to identify authoritative and hub pages. It defines two Markov chains, one for hubs and one for authorities. PageRank is also based on random walks, and ranks pages highly if the sum of ranks of backlinking pages is high, accounting for both number of backlinks and importance of backlinking pages. Both algorithms bring order to the web by using link information to rank pages globally.
Pagerank
This document discusses two algorithms for ranking web pages based on link structure: SALSA and PageRank. SALSA is a stochastic approach that analyzes random walks on graphs derived from link structures to identify authoritative and hub pages. It defines hub and authority matrices to model the transitions between these types of pages. PageRank is also based on random walks, and ranks pages globally based on the number and importance of backlinks, as determined by the PageRank scores of the referring pages. It presents PageRank as modeling the probability distribution of a random surfer's transitions between pages. Both algorithms bring order to the web by using its graph structure and link-based relationships between pages.
Pagerank
This document discusses two algorithms for ranking web pages based on link structure: SALSA and PageRank. SALSA is a stochastic approach that analyzes random walks on graphs derived from link structures to identify authoritative and hub pages. It defines hub and authority matrices to model the transitions between these types of pages. PageRank is also based on random walks, and ranks pages highly if the sum of ranks of backlinking pages is high, modeling a random surfer. Both algorithms bring order to the web by using link structures, but SALSA focuses on specific communities while PageRank provides a global ranking.
Enhancement in Weighted PageRank Algorithm Using VOL
1) The document proposes enhancing the Weighted PageRank algorithm by incorporating visits of links (VOL) to calculate page rank. It takes into account both the number of visits of inlinks and outlinks of pages.
2) A new algorithm, called Enhanced Weighted PageRank using VOL (EWPR VOL), is presented. It calculates page popularity based on the number of visits of inlinks (WVOLin) and outlinks (WVOLout).
3) The EWPR VOL algorithm is demonstrated using a sample web graph to calculate page rank values for pages A, B and C based on the number of visits of their inlinks and outlinks.
Pagerank
This document provides an overview of a project to build a page ranking tool. It discusses the objective to provide efficient search results by determining the page rank of web pages. It covers topics like how page rank is calculated using a formula, web crawlers, determining page rank for each page using a damping factor and algorithm, the project modules, and related problems like rank sinks and dangling links.
Page Rank
PageRank is a method for ranking web pages based on the graph of the web. It takes advantage of the link structure to determine the importance or "rank" of each page. PageRank assumes that more important pages are likely to receive more links from other pages. It calculates a PageRank score through an iterative process that distributes weight from highly ranked pages to the pages they link to. The algorithm accounts for pages with no outgoing links and pages that link only to each other to avoid "rank sinks".
Pagerank (1)
SALSA and PageRank are algorithms that analyze the link structure of web pages to determine authoritative pages. SALSA models the web as two Markov chains, with one for hubs (pages that link to many others) and one for authorities (pages that many point to). It computes hub and authority scores for pages. PageRank also models the web as a graph, and computes page ranks based on the concept of a random surfer clicking on links. It factors in both the number of links pages have as well as the rank of pages that link to them. Both algorithms bring order to the web by using its link structure, but SALSA computes separate scores while PageRank computes a single rank value.
Pagerank
SALSA and PageRank are algorithms that analyze the link structure of web pages to determine authoritative pages. SALSA models the web as two Markov chains, with pages as either hubs or authorities. It computes hub and authority scores to find the principal community of related pages. PageRank also models the web as a graph, and defines a page's rank based on the ranks of pages that link to it, including a random surfer model to avoid rank sinks. Both leverage random walks to determine important pages based on the web's link structure.
Mind map of terminologies used in context of Generative AI
Mind map of common terms used in context of Generative AI.
Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Communications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Pushing the limits of ePRTC: 100ns holdover for 100 days
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Microsoft - Power Platform_G.Aspiotis.pdf
Revolutionizing Application Development
with AI-powered low-code, presentation by George Aspiotis, Sr. Partner Development Manager, Microsoft
Infrastructure Challenges in Scaling RAG with Custom AI models
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
More Related Content
Similar to Pagerank
Page rank by university of michagain.ppt
1. PageRank is an algorithm developed by Larry Page and Sergey Brin to rate the importance of web pages by analyzing the link structure of the web. It assumes that more important pages are likely to receive more links from other pages.
2. PageRank models a user randomly clicking on links, getting bored, and randomly jumping to another page, with pages that are frequently visited in this random walk receiving a higher PageRank.
3. PageRank was a key part of the original Google search engine, which used it to help sort search results from most to least relevant. It remains an important algorithm for search engines today.
Ranking Web Pages
The document discusses the history and development of web search engines. It describes how early search engines in 1994 indexed around 100,000 pages while Google grew to index over 8 billion pages by 2005. It also explains the basic components and ranking algorithms of search engines, including PageRank, which calculates the importance of pages based on both the number and quality of inbound links.
Pagerank
This document discusses two algorithms for ranking web pages based on link structure: SALSA and PageRank. SALSA is a stochastic approach that analyzes random walks on graphs derived from link structures to identify authoritative and hub pages. It defines two Markov chains, one for hubs and one for authorities. PageRank is also based on random walks, and ranks pages highly if the sum of ranks of backlinking pages is high, accounting for both number of backlinks and importance of backlinking pages. Both algorithms bring order to the web by using link information to rank pages globally.
Pagerank
This document discusses two algorithms for ranking web pages based on link structure: SALSA and PageRank. SALSA is a stochastic approach that analyzes random walks on graphs derived from link structures to identify authoritative and hub pages. It defines hub and authority matrices to model the transitions between these types of pages. PageRank is also based on random walks, and ranks pages globally based on the number and importance of backlinks, as determined by the PageRank scores of the referring pages. It presents PageRank as modeling the probability distribution of a random surfer's transitions between pages. Both algorithms bring order to the web by using its graph structure and link-based relationships between pages.
Pagerank
This document discusses two algorithms for ranking web pages based on link structure: SALSA and PageRank. SALSA is a stochastic approach that analyzes random walks on graphs derived from link structures to identify authoritative and hub pages. It defines hub and authority matrices to model the transitions between these types of pages. PageRank is also based on random walks, and ranks pages highly if the sum of ranks of backlinking pages is high, modeling a random surfer. Both algorithms bring order to the web by using link structures, but SALSA focuses on specific communities while PageRank provides a global ranking.
Enhancement in Weighted PageRank Algorithm Using VOL
1) The document proposes enhancing the Weighted PageRank algorithm by incorporating visits of links (VOL) to calculate page rank. It takes into account both the number of visits of inlinks and outlinks of pages.
2) A new algorithm, called Enhanced Weighted PageRank using VOL (EWPR VOL), is presented. It calculates page popularity based on the number of visits of inlinks (WVOLin) and outlinks (WVOLout).
3) The EWPR VOL algorithm is demonstrated using a sample web graph to calculate page rank values for pages A, B and C based on the number of visits of their inlinks and outlinks.
Pagerank
This document provides an overview of a project to build a page ranking tool. It discusses the objective to provide efficient search results by determining the page rank of web pages. It covers topics like how page rank is calculated using a formula, web crawlers, determining page rank for each page using a damping factor and algorithm, the project modules, and related problems like rank sinks and dangling links.
Page Rank
PageRank is a method for ranking web pages based on the graph of the web. It takes advantage of the link structure to determine the importance or "rank" of each page. PageRank assumes that more important pages are likely to receive more links from other pages. It calculates a PageRank score through an iterative process that distributes weight from highly ranked pages to the pages they link to. The algorithm accounts for pages with no outgoing links and pages that link only to each other to avoid "rank sinks".
Pagerank (1)
SALSA and PageRank are algorithms that analyze the link structure of web pages to determine authoritative pages. SALSA models the web as two Markov chains, with one for hubs (pages that link to many others) and one for authorities (pages that many point to). It computes hub and authority scores for pages. PageRank also models the web as a graph, and computes page ranks based on the concept of a random surfer clicking on links. It factors in both the number of links pages have as well as the rank of pages that link to them. Both algorithms bring order to the web by using its link structure, but SALSA computes separate scores while PageRank computes a single rank value.
Pagerank
SALSA and PageRank are algorithms that analyze the link structure of web pages to determine authoritative pages. SALSA models the web as two Markov chains, with pages as either hubs or authorities. It computes hub and authority scores to find the principal community of related pages. PageRank also models the web as a graph, and defines a page's rank based on the ranks of pages that link to it, including a random surfer model to avoid rank sinks. Both leverage random walks to determine important pages based on the web's link structure.
Similar to Pagerank (10)
Enhancement in Weighted PageRank Algorithm Using VOL
Enhancement in Weighted PageRank Algorithm Using VOL
Recently uploaded
Mind map of terminologies used in context of Generative AI
Mind map of common terms used in context of Generative AI.
Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Communications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Pushing the limits of ePRTC: 100ns holdover for 100 days
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Microsoft - Power Platform_G.Aspiotis.pdf
Revolutionizing Application Development
with AI-powered low-code, presentation by George Aspiotis, Sr. Partner Development Manager, Microsoft
Infrastructure Challenges in Scaling RAG with Custom AI models
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Video Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Full-RAG: A modern architecture for hyper-personalization
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Recently uploaded (20)
Mind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AI
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Infrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI models
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
Full-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalization
Pagerank
- 1. The PageRank Citation Ranking: Bringing Order to the Web Larry Page etc. Stanford University Presented by Guoqiang Su & Wei Li
- 20. Evaluation I
- 21. Evaluation II