Metaqueries

Metaqueries are queries whose operands are themselves queries or data about other queries, rather than direct access to the primary domain data. They operate on metadata, provenance, query plans, execution statistics, or the results of other queries. In practice, a metaquery is used to inspect, compare, optimize, or orchestrate data retrieval tasks by reasoning about the queries that produce data rather than the data itself.

In database systems, metaqueries often involve system catalogs or information schemas to retrieve statistics, execution plans, indexes, or query histories. They support performance tuning and governance by answering questions such as which queries consumed the most resources, which indexes were used, or how often a particular join strategy was chosen. Example metaqueries include listing the top five longest-running queries in a given period or identifying missing or stale statistics for a table.

In data integration and knowledge graphs, metaqueries may query data provenance, lineage, quality metrics, or schema information. They enable adaptive query planning, source selection, and cross-source auditing by providing visibility into where data originated, how it was transformed, and how reliable it is.

In AI and natural language processing, metaqueries can refer to queries about the reasoning process, model outputs, or evaluation prompts. They support meta-reasoning, prompt analysis, model auditing, and iterative improvement of querying or inference strategies.

Key design considerations for metaqueries include performance impact, security and access control, consistency of meta-data, and governance over how introspection queries affect the underlying data systems.