Respect for RushDB
Dear Artemiy Vereshinskiy,
My respect for your work, in my opinion, it is truly moving in the right direction toward generating a new unicorn in Graph Data Modeling and Graph Databases through your revolutionary Labeled Meta Property Graph (LMPG) database.
My dear colleagues, I believe Artemiy Vereshinskiy has presented an outstanding and original perspective on LPGs that bridges the gap with RDF graphs. According to the documentation, LMPG is a property-first graph model where RushDB hyper-properties are treated as nodes that connect records through a combination of field names and data types.
I am genuinely impressed, and you certainly have my full attention and interest in seeing how this model will evolve. I have already explored and seen enough through your RushDB project to be intrigued, and I will definitely experiment further and write more about it in due time.
Once again, congratulations, and thank you for publishing and sharing this work with all of us.
Repost Artemiy LinkedIn Article
https://www.linkedin.com/posts/onepx_hyperproperties-agent-graphs-activity-7381413677335351296-G-hH
Data is the new gold - but can you turn chaos into gold with one API call?
RushDB (LMPG) + OpenAI Agents + Neo4j
Over the past month, I’ve been intentionally stress-testing the limits of LMPG - and to find them, I built an analyst agent that autonomously explores data, performs any selection or aggregation, and operates directly on raw, unnormalized data stored in RushDB, powered by Neo4j
This is where the Labeled Meta-Property Graph (LMPG) model and its concept of Hyper-Properties truly shine: the agent can independently discover available labels, fields, and value ranges, build aggregates and filters using a dynamic ontology, and return results in any desired format - without any predefined schema or ETL pipeline.
Agents don’t need schemas - they just need graphs !
💬 On the same journey? Drop a comment - I’ll DM you with more insights and early access to the data platform.
Comment
The web can change radically once an intuitive graph data model will emerge, one that will allow every user/company to share a customized knowledge graph of entities with content, relationships and true semantics automatically. At that point, existing linked open data could be seamlessly redefined, transformed into a more expressive and standardized structure