Abstract: Implementations described herein relate to systems and methods for generating responses to natural language input. The system deconstructs a natural language query into a plurality of fragments, which are then analyzed to identify relevant query operators. This interpretation is mediated by domain knowledge encapsulated in synthetic questions generated through an automated offline training process. A structured query tree is constructed using the identified operators and executed on a data source to retrieve an output result. To ensure structural integrity, the system utilizes deterministic grounding via hash-based predictors and performs a structural exploration of join-graph reachability to synthesize a topologically ordered join tree. The resulting data is then used to generate a tailored response, which may include inferred visualization elements, detected anomalies, or natural language reports.

Abstract: Methods for optimization in query plans are performed by computing systems via a query optimizer advisor. A query optimizer advisor (QO-Advisor) is configured to steer a query plan optimizer towards more efficient plan choices by providing rule hints to improve navigation of the search space for each query in formulation of its query plan. The QO-Advisor receives historical information of a distributed data processing system as an input, and then generates a set of rule hint pairs based on the historical information. The QO-Advisor provides the set of rule hint pairs to a query plan optimizer, which then optimizes a query plan of an incoming query through application of a rule hint pair in the set. This application is based at least on a characteristic of the incoming query matching a portion of the rule hint pair.