Multi-dimensional hierarchical data structures for analysis and comprehension using large language models

A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for providing analytics over multi-dimensional, hierarchical data using large language models (LLMs), the operations comprising:

• retrieving data from a data store, the data being in a storage format comprising multiple rows, the data comprising multiple dimensions in a hierarchy, at least one dimension having a sub-hierarchy, each dimension recorded in a row of the multiple rows;
• converting the data from the storage format to an analytics format by processing the data through preprocessing to convert the storage format to an intermediate data structure, a forward pass to traverse the intermediate data structure row-by-row and provide an intermediate text dataset, and a backwards pass to provide the analytics format from the intermediate text dataset, the analytics format comprising a set of nodes, each node representing a row of a dimension of the multiple dimensions, a set of hierarchy characters, one or more hierarchy characters separating two or more nodes to represent a hierarchical relationship between nodes as a parent node and child node relationship, and an attribute delimiter to separate attribute values of a node;
• generating a prompt that references the data in the analytics format;
• transmitting the prompt to a LLM system;
• and
• receiving a response to the prompt from the LLM system.

A system, comprising:

• a computing device;
• and
• a computer-readable storage device coupled to the computing device and having instructions stored thereon which, when executed by the computing device, cause the computing device to perform operations for providing analytics over multi-dimensional, hierarchical data using large language models (LLMs), the operations comprising:
• retrieving data from a data store, the data being in a storage format comprising multiple rows, the data comprising multiple dimensions in a hierarchy, at least one dimension having a sub-hierarchy, each dimension recorded in a row of the multiple rows;
• converting the data from the storage format to an analytics format by processing the data through preprocessing to convert the storage format to an intermediate data structure, a forward pass to traverse the intermediate data structure row-by-row and provide an intermediate text dataset, and a backwards pass to provide the analytics format from the intermediate text dataset, the analytics format comprising a set of nodes, each node representing a row of a dimension of the multiple dimensions, a set of hierarchy characters, one or more hierarchy characters separating two or more nodes to represent a hierarchical relationship between nodes as a parent node and child node relationship, and an attribute delimiter to separate attribute values of a node;
• generating a prompt that references the data in the analytics format;
• transmitting the prompt to a LLM system;
• and
• receiving a response to the prompt from the LLM system.