Abstract: Described are examples for querying a database that stores semantic data in a compressed linked tabular representation. An ordered list of relational operators can be extracted from a language-agnostic representation of a query for semantic data from the database. The language-agnostic representation of the query can be translated, based on the ordered list of relational operators, into a database query of a query language syntax that is supported by the database, and the database query can be executed on the database without decompressing the data stored in the compressed linked tabular representation.

Abstract: Learned workload synthesis is disclosed. In an aspect of the present disclosure, a time series dataset corresponding to a target workload is received. A set of performance characteristics is determined from the time series dataset. A call is provided to a prediction model to determine a candidate query sequence based on the determined set of performance characteristics. A synthetic workload is generated based on the determined candidate query sequence. A synthetic workload is generated based on the determined candidate query sequence. A first similarity between a first performance profile of the synthetic workload and a second performance profile of the target workload meets a workload performance threshold condition. A performance insight is determined based on the synthetic workload. In a further aspect, the prediction model is trained to predict performance profiles based on workload profiles generated by executing benchmark queries using hardware and/or software configurations.