Abstract: Classification of erroneous cell data includes using at least one transformation function, the at least one transformation function determined based on correlations of observed cell data to correct call data, to automatically generate training examples that correlate erroneous data values to correct data values as informed by the at least one transformation function; augmenting an initial training set of labeled training examples with the generated training examples to produce an augmented training set; and training a machine learning model using the augmented training set to classify observed cell data based on a comparison between the observed cell data and data that the machine learning model predicts.

Abstract: Classification of erroneous cell data includes performing unsupervised pre-training of a machine learning model to learn a bidirectional encoder representation of data cells, obtaining an initial training set, with labeled training examples that correlate observed cell data to correct cell data, for training the machine learning model to classify cell data, automatically augmenting the initial training set to produce an augmented training set, where the augmenting includes identifying patterns in the labeled training examples, generating transformation functions, and using the transformation functions, learning an augmentation strategy and automatically generating additional training examples correlating erroneous data values to correct data values, and training the machine learning model using the augmented training set.

Abstract: A DBMS training subsystem trains a DBMS workload-manager model with training data identifying resources used to execute previous DBMS data-access requests. The subsystem integrates each request's high-level features and compile-time operations into a vector and clusters similar vectors into templates. The requests are divided into workloads each represented by a training histogram that describes the distribution of templates associated with the workload and identifies the total amounts and types of resources consumed when executing the entire workload.

Abstract: A DBMS training subsystem trains a DBMS workload-manager model with training data identifying resources used to execute previous DBMS data-access requests. The subsystem integrates each request's high-level features and compile-time operations into a vector and clusters similar vectors into templates. The requests are divided into workloads each represented by a training histogram that describes the distribution of templates associated with the workload and identifies the total amounts and types of resources consumed when executing the entire workload.

Abstract: Classification of erroneous cell data includes performing unsupervised pre-training of a machine learning model to learn a bidirectional encoder representation of data cells, obtaining an initial training set, with labeled training examples that correlate observed cell data to correct cell data, for training the machine learning model to classify cell data, automatically augmenting the initial training set to produce an augmented training set, where the augmenting includes identifying patterns in the labeled training examples, generating transformation functions, and using the transformation functions, learning an augmentation strategy and automatically generating additional training examples correlating erroneous data values to correct data values, and training the machine learning model using the augmented training set.

Abstract: A computer-implemented method includes receiving an artifact and a problem pattern, transforming the artifact into an abstracted artifact structure, and transforming the problem pattern into a query. The query is matched against the abstracted artifact structure. Any matched portions of the abstracted artifact structure are related back to corresponding result portions of the artifact. The corresponding result portions of the artifact are returned. The method may be embodied in a corresponding computer system or computer program product.

Abstract: A computer-implemented method includes receiving an artifact and a problem pattern, transforming the artifact into an abstracted artifact structure, and transforming the problem pattern into a query. The query is matched against the abstracted artifact structure. Any matched portions of the abstracted artifact structure are related back to corresponding result portions of the artifact. The corresponding result portions of the artifact are returned. The method may be embodied in a corresponding computer system or computer program product.

Abstract: A computer-implemented method includes receiving an artefact and a problem pattern, transforming the artefact into an abstracted artefact structure, and transforming the problem pattern into a query. The query is matched against the abstracted artefact structure. Any matched portions of the abstracted artefact structure are related back to corresponding result portions of the artefact. The corresponding result portions of the artefact are returned. The method may be embodied in a corresponding computer system or computer program product.

Abstract: A computer-implemented method includes receiving an artefact and a problem pattern, transforming the artefact into an abstracted artefact structure, and transforming the problem pattern into a query. The query is matched against the abstracted artefact structure. Any matched portions of the abstracted artefact structure are related back to corresponding result portions of the artefact. The corresponding result portions of the artefact are returned. The method may be embodied in a corresponding computer system or computer program product.