I am working on a platform to help user to enrich their data by AI. so that AI can understand their Data more, especially for ChatGPT. Also it's easy to host a data and publish a MCP for ChatGPT.

The challenge is how ChatGPT can understand your "query" or say "prompts"? Raw data is not good enough - so I try to use a term called "AI Understanding Score" to measure it: https://senify.ai/ai-understanding-score. I think this index will help user to build more context so that AI can know more and answer with correct result.

This is very early work without every detail considered, really would like to have your feedback and suggestions.

You can have a try with some MCP services here: https://senify.ai/mcp-services

Thanks.

well, I would like to say the OLAP Cube is just re-rising now. There are 1000+ companies deployed Apache Kylin (OLAP Engine for Big Data) in the past 5 years, for 100+B rows, for 100+ concurrent users...many different use cases are based that technology...it works very well with BI tools and so friendly to analysts who are using such "old fashion" every day over the decade (how hard for them to be Data Scientists?) check more here: http://kylin.apache.org/community/poweredby.html

+1 for Apache Kylin, it's a great project and awesome open source community. If anyone is curious about what modern OLAP is capable of, check it out.

If you want an alternative take on where OLAP is today and what it is capable of, I usually recommend this article: https://kyligence.io/blog/olap-analytics-is-dead-really/

There are huge problems with that piece. For starters, OLAP != OLAP cube. Columnar databases and OLAP cubes are both designed for OLAP workloads. They are simply different architectures. Therefore, it is impossible to argue that 'OLAP is dead' — it cannot be dead, because OLAP is simply a type of database usage.

At this point you might say, "oh, OLAP cubes refer to an abstraction, it can be implemented using columnar stores!" — and I would point you to 40 years worth of academic research that stretches back to the early 80s. The OLAP cube or data cube refers to a specific type of data structure. It just so happens that vendors like to use the term 'OLAP cube' even when they are using a columnar engine under the hood, because it sells well.

the document will coming soon, there are many stuff we have moving from internal wiki to kyliy.io now.

The Cube File is stored on HBase now, it could be extend to otehr KV value system.