Graph databases
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The document discusses a project analyzing social networks using a graph database. It introduces the three project members and defines what a graph is - a collection of nodes and relationships. It then discusses properties of graph databases, why the Neo4j graph database was chosen, its Cypher query language, and compares graph and relational databases. Execution times are faster for graph databases on large connected datasets. Applications discussed include product recommendation and finding shortest paths.
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Mapping ERD to Relational
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SQL CREATE TABLE
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SQL INSERT INTO
SQL INSERT INTO SELECT
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Neo4j GraphDay Seattle- Sept19- in the enterprise
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During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
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.
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!
Introduction of Cybersecurity with OSS at Code Europe 2024
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
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
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.Recently uploaded (20)
WeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation Techniques
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
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Programming Foundation Models with DSPy - Meetup Slides
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UiPath Test Automation using UiPath Test Suite series, part 6
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National Security Agency - NSA mobile device best practices
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
Graph databases
- 1. Project Mentor: Mrs. Manisha Gahirwal Project Members: Rahul Anwani(03) Akshaykumar Oswal(19) Srinivas Ravi(48) Social Networking Analysis using Graph Database
- 2. Social Network is a highly connected data
- 3. What is Graph? A graph is a collection of vertices and edges. It is a set of fundamental units called NODES and the RELATIONSHIPS that connect these nodes. Data is stored in both Nodes as well as Relationships
- 5. Properties of Graph Databases 1. The underlying storage 2. The processing engine
- 6. Why Neo4j? • reliable • durable and fast • massively scalable • highly-available • expressive • fast • simple
- 7. Cypher Query Language • declarative graph query language • what to retrieve • Expressions • Identifiers • Operators • Pattern • Labels Building blocks of CQL
- 8. Structure • MATCH • RETURN • WHERE • LIMIT • ORDER BY • CREATE • REMOVE • DELETE
- 9. Relational Database System Graph Database System Less connected data More connected data No graph visualization. Results available in only tabular form. Graph visualization as well as tabular representation. Queries don’t model real life so well. Queries modelled around real life. SQL is slower for very large connected datasets Cypher Query Language is faster for very large connected datasets. Relational Vs Graph Databases
- 10. Comparison of Graph and Relational Databases
- 12. Comparison of execution for Neo4j and RDBMS Fig: Databases with sizes
- 13. Comparison of execution for Neo4j and RDBMS Fig: Structural query results in millisec
- 14. Comparison of execution for Neo4j and RDBMS
- 15. Applications of Graph Database
- 17. Shortest Path