Miller’s Law states that the human brain can hold about 7 ± 2 pieces of information in working memory. Thus effective data analysis requires efficient “chunking” of information—spatial, temporal, hierarchical, and relational. The speaker will draw on decades of experience in data analysis and modeling to demonstrate “chunking” strategies using graph schema, data capture, analysis, and visualization with examples. One specific example will illustrate using LLM to process documents into a knowledge graph stored in Neo4j, then applying chunking to reveal insights.
with Weidong Yang
Get certified with GraphAcademy: https://dev.neo4j.com/learngraph
Neo4j AuraDB https://dev.neo4j.com/auradb
Knowledge Graph Builder https://dev.neo4j.com/KGBuilder
Neo4j GenAI https://dev.neo4j.com/graphrag