{"id":26460821,"url":"https://github.com/jerryblessed/skyrouteai","last_synced_at":"2026-05-19T11:08:25.998Z","repository":{"id":281692170,"uuid":"945205567","full_name":"Jerryblessed/skyrouteai","owner":"Jerryblessed","description":null,"archived":false,"fork":false,"pushed_at":"2025-03-10T17:06:50.000Z","size":20,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-10T17:33:41.235Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Jerryblessed.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-03-08T22:31:13.000Z","updated_at":"2025-03-10T17:06:53.000Z","dependencies_parsed_at":"2025-03-10T17:45:06.334Z","dependency_job_id":null,"html_url":"https://github.com/Jerryblessed/skyrouteai","commit_stats":null,"previous_names":["jerryblessed/skyrouteai"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Jerryblessed%2Fskyrouteai","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Jerryblessed%2Fskyrouteai/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Jerryblessed%2Fskyrouteai/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Jerryblessed%2Fskyrouteai/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Jerryblessed","download_url":"https://codeload.github.com/Jerryblessed/skyrouteai/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244345093,"owners_count":20438241,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-03-19T03:16:48.123Z","updated_at":"2026-05-19T11:08:20.964Z","avatar_url":"https://github.com/Jerryblessed.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# SkyRoute AI - Flight Optimization System\n\n[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Jerryblessed/fraudShieldgraphai/blob/main/fdetect.ipynb)\n\n## 🚀 Inspiration\n\nAir travel optimization is crucial for efficiency, sustainability, and cost-effectiveness. We wanted to build a system that simplifies route planning, enhances decision-making, and integrates AI for smarter travel insights. Inspired by real-world airline challenges, SkyRoute AI leverages graph-based analytics to optimize flight paths dynamically.\n\n## ✈️ What it does\n\nSkyRoute AI processes flight and airport data using graph-based network analysis, allowing users to:\n\n- Visualize flight routes and airport connections in ArangoDB.\n- Find the shortest and most efficient routes between airports.\n- Compute PageRank to determine the most influential airports.\n- Use AI-powered NLP to AQL translation for querying flight data effortlessly.\n- Integrate real-time graph visualization to display network structures.\n\n## 🏗️ Architectural Diagram\n\n```\n+--------------------+      +---------------------+\n|  User Interface   | ---\u003e |  Flask API Server   |\n+--------------------+      +---------------------+\n            |                      |\n            v                      v\n+---------------------+      +--------------------+\n|    Azure OpenAI    |      |    ArangoDB       |\n| (NLP to AQL Query) |      | (Graph Storage)  |\n+---------------------+      +--------------------+\n            |                      |\n            v                      v\n+--------------------+      +--------------------+\n|      NetworkX     |      |      cuGraph      |\n| (Graph Analysis)  |      |  (GPU Acceleration)|\n+--------------------+      +--------------------+\n```\n\n## 💻 How we built it\n\n- **NetworkX \u0026 cuGraph** for graph-based computation and shortest path analysis.\n- **ArangoDB** for storing and visualizing flight network data.\n- **Azure OpenAI (GPT-4o)** for NLP-based AQL query generation.\n- **Flask API \u0026 Web Interface** for user interaction and query execution.\n- **Matplotlib \u0026 DataFrames** for plotting and analyzing flight route data.\n- **LangChain** for AI-driven query interpretations.\n\n## 📥 Dataset\n\nThe dataset is sourced from [ArangoDB Example Datasets](https://github.com/arangodb/example-datasets/tree/master/Data%20Loader).\n\n## ⚠️ Security Notice\n\nWe have intentionally left API keys and database credentials in the code for demonstration purposes. However, in a production environment, sensitive information should be securely stored using environment variables or secret management tools.\n\n## 🚫 Challenges we ran into\n\n- Optimizing large-scale graph processing efficiently with GPU acceleration.\n- Converting natural language queries to accurate AQL statements.\n- Ensuring real-time data visualization while handling complex computations.\n- Managing ArangoDB integration with NetworkX and cuGraph.\n\n## 🎉 Accomplishments that we're proud of\n\n- Successfully integrated AI-powered NLP with AQL for intuitive user queries.\n- Achieved high-speed graph processing using GPU acceleration with cuGraph.\n- Built an interactive flight network visualization inside ArangoDB.\n- Developed a fully functional Flask-based API for flight route optimization.\n\n## 📝 What we learned\n\n- The power of graph databases for real-world travel optimization.\n- The efficiency gains from using cuGraph for GPU-accelerated graph operations.\n- How NLP-driven AI can simplify complex query languages like AQL.\n- The importance of data visualization in interpreting large datasets.\n\n## 🌟 What's next for SkyRoute AI\n\n- Real-time flight tracking integration for live travel updates.\n- Machine learning-based demand forecasting for optimized flight schedules.\n- Multi-modal transport integration (e.g., trains, buses) for seamless travel planning.\n- User-friendly web dashboard for airline companies and travelers.\n- Cloud-based API services for external applications and travel platforms.\n\n## 🛠️ Built With\n\n- **Python**\n- **NetworkX \u0026 cuGraph**\n- **ArangoDB**\n- **Azure OpenAI \u0026 LangChain**\n- **Flask \u0026 Matplotlib**\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjerryblessed%2Fskyrouteai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjerryblessed%2Fskyrouteai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjerryblessed%2Fskyrouteai/lists"}