Abstract: A method provides for classifying data fields of a dataset. A classifier configured for determining confidence values for a plurality of data classes for the data fields may be applied. Using the confidence values, data class candidates may be identified. Data fields may be determined for which a plurality of data class candidates is identifiable. Using previous user-selected data class assignments, a probability may be determined for the data class candidates that the respective data class candidate is a data class to which the respective data field is to be assigned. The data fields may be classified using the probabilities to select for the data fields a data class from the data class candidates. The dataset may be provided with metadata identifying for the data fields the data classes to which the respective data fields are assigned.

Abstract: Techniques are described relating to automatic data standardization in a managed services domain of a cloud computing environment. An associated computer-implemented method includes receiving a dataset during a data onboarding procedure and classifying datapoints within the dataset. The method further includes applying a machine learning data standardization model to each classified datapoint within the dataset and deriving a proposed set of data standardization rules for the dataset based upon any standardization modification determined consequent to model application. Optionally, the method includes presenting the proposed set of data standardization rules for client review and, responsive to acceptance of the proposed set of data standardization rules, applying the proposed set of data standardization rules to the dataset. The method further includes, responsive to acceptance of the proposed set of data standardization rules, updating the machine learning data standardization model accordingly.

Abstract: A database of deployed configurations, as well as attempted configurations that failed is maintained and used as reference to compare against configurations of attempted software deployments. Upon detecting a failed deployment, disclosed embodiments search the database for working configurations that most closely resemble the failed configuration, and rank the configurations based on various criteria. Disclosed embodiments may then automatically select a highest ranked working configuration, and perform an automatic upgrade of the necessary components to create a working configuration.

Abstract: Techniques are described relating to automatic data standardization in a managed services domain of a cloud computing environment. An associated computer-implemented method includes receiving a dataset during a data onboarding procedure and classifying datapoints within the dataset. The method further includes applying a machine learning data standardization model to each classified datapoint within the dataset and deriving a proposed set of data standardization rules for the dataset based upon any standardization modification determined consequent to model application. Optionally, the method includes presenting the proposed set of data standardization rules for client review and, responsive to acceptance of the proposed set of data standardization rules, applying the proposed set of data standardization rules to the dataset. The method further includes, responsive to acceptance of the proposed set of data standardization rules, updating the machine learning data standardization model accordingly.

Abstract: A method includes: receiving, by a computer device, resource request for a data integration job, wherein the resource request is received from a job executor module and defines processes of the data integration job; allocating, by the computer device, containers for the processes of the data integration job; launching, by the computer device, a respective wrapper script on each respective one of the containers after allocating the respective one of the containers; and transmitting, by the computer device and in response to the allocating, node details to the job executor module. In embodiments, the wrapper script running on the container is configured to repeatedly check a predefined location for process commands from a job executor. After the resource manager allocates all the containers for a data integration job according to a resource request, the job executor writes the process commands to the predefined location.

Abstract: The invention relates to an approach for data quality assessment for data analytics, the approach comprising providing a data set, the data set comprising multiple data fields, predicting by a first trained machine learning model at least one usage type of the data set using characteristics of the data fields as input, for each usage type of the at least one usage type, determining a usage specific data quality score of each of the predicted usage types, and using of the data set based on the at least one usage type and associated data quality score.

Abstract: A set of resources required to process a data integration job is determined. In response to determining that the set of resources is not available, queue occupation, for each queue in the computing environment, is predicted. Queue occupation is a workload of queue resources for a future time based on a previous workload. A best queue is selected based on the predicted queue occupation. The best queue is the queue or queues in the computing environment available to be assigned to process the data integration job without preemption. The data integration job is processed using the best queue. It is determined whether a preemption event occurred causing the removal of resources from the best queue. A checkpoint is created in response to determining that a preemption event occurred. The checkpoint indicates the last successful operation completed and provides a point where processing can resume when resources become available.

Abstract: Improving allocation of network resources by receiving node names for resource allocation, checking a bookmark file of bad nodes for the received node names, selecting good nodes from the received nodes for command execution, sending commands to selected good nodes, identifying bad nodes during command execution; and adding the identified bad nodes to the bookmark file.

Abstract: Data classification includes tracking classification of columns of data into data classes of a collection of classes available for classifying the columns, obtaining a target column of data, of a target dataset, to be classified into a data class of the collection of candidate classes, and classifying the target column of data into a data class of the collection of classes based on historical data classification characteristics provided by the tracking. The classifying includes selecting a group of candidate data classes of the collection of classes to compare to value(s) of the target column, the selecting excludes at least some candidate data classes of the collection from comparison to the value(s), and establishing a priority between the candidate data classes of the group of candidate classes in comparing the value(s) of the target column of data to the selected group of candidate classes.

Abstract: Data classification includes tracking classification of columns of data into data classes of a collection of classes available for classifying the columns, obtaining a target column of data, of a target dataset, to be classified into a data class of the collection of candidate classes, and classifying the target column of data into a data class of the collection of classes based on historical data classification characteristics provided by the tracking. The classifying includes selecting a group of candidate data classes of the collection of classes to compare to value(s) of the target column, the selecting excludes at least some candidate data classes of the collection from comparison to the value(s), and establishing a priority between the candidate data classes of the group of candidate classes in comparing the value(s) of the target column of data to the selected group of candidate classes.

Abstract: Improving allocation of network resources by receiving node names for resource allocation, checking a bookmark file of bad nodes for the received node names, selecting good nodes from the received nodes for command execution, sending commands to selected good nodes, identifying bad nodes during command execution; and adding the identified bad nodes to the bookmark file.

Abstract: A method provides for classifying data fields of a dataset. A classifier configured for determining confidence values for a plurality of data classes for the data fields may be applied. Using the confidence values, data class candidates may be identified. Data fields may be determined for which a plurality of data class candidates is identifiable. Using previous user-selected data class assignments, a probability may be determined for the data class candidates that the respective data class candidate is a data class to which the respective data field is to be assigned. The data fields may be classified using the probabilities to select for the data fields a data class from the data class candidates. The dataset may be provided with metadata identifying for the data fields the data classes to which the respective data fields are assigned.

Abstract: A set of resources required to process a data integration job is determined. In response to determining that the set of resources is not available, queue occupation, for each queue in the computing environment, is predicted. Queue occupation is a workload of queue resources for a future time based on a previous workload. A best queue is selected based on the predicted queue occupation. The best queue is the queue or queues in the computing environment available to be assigned to process the data integration job without preemption. The data integration job is processed using the best queue. It is determined whether a preemption event occurred causing the removal of resources from the best queue. A checkpoint is created in response to determining that a preemption event occurred. The checkpoint indicates the last successful operation completed and provides a point where processing can resume when resources become available.

Abstract: A method includes: receiving, by a computer device, resource request for a data integration job, wherein the resource request is received from a job executor module and defines processes of the data integration job; allocating, by the computer device, containers for the processes of the data integration job; launching, by the computer device, a respective wrapper script on each respective one of the containers after allocating the respective one of the containers; and transmitting, by the computer device and in response to the allocating, node details to the job executor module. In embodiments, the wrapper script running on the container is configured to repeatedly check a predefined location for process commands from a job executor. After the resource manager allocates all the containers for a data integration job according to a resource request, the job executor writes the process commands to the predefined location.