📅 2025-02-17 — Session: Developed Graph and Data Processing Frameworks
🕒 00:00–23:55
🏷️ Labels: Graph Traversal, Neo4J, Data Processing, Web Crawling, Ai Workflows
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore and develop frameworks for graph traversal, property graph schema design, and data processing pipelines, particularly focusing on Neo4j and web crawling strategies.
Key Activities
- Discussed the use of graph traversal queries using SPARQL and Cypher, emphasizing their roles in embedding-based and property graph models.
- Outlined a property graph schema for managing contact networks, detailing node and edge schemas and AI workflow integration.
- Planned a scalable infrastructure for tracking interactions and relationships, focusing on automation and AI-assisted data curation.
- Developed a data processing pipeline for Neo4j, including steps for data ingestion, entity extraction, and loading into a graph database.
- Explored strategies for effective web crawling, including scope definition and filtering mechanisms.
- Reviewed a list of online resources related to the Facultad de Ciencias Exactas y Naturales (UBA).
- Reflected on optimizing text processing with RAKE and balancing detail in concept mapping.
Achievements
- Established foundational frameworks for graph database management and data processing.
- Integrated AI workflows into graph schema designs.
- Developed strategies for efficient web crawling and text processing.
Pending Tasks
- Further development and testing of the data processing pipeline for Neo4j.
- Implementation of the outlined web crawling strategies in a practical setting.