Abstract: Techniques and solutions are described for analyzing data collections to determine if they may be anomalous as compared with other data collections. For example, one or more values for data elements of a data collection may be unusually high or low, or may represent infrequently occurring values. Or, values of data elements in a data collection may not be anomalous when considered individually, but may be anomalous in combination. A machine learning model is trained with training data collections, where the training data collections include a plurality of data elements. An inference data collection, also having the data elements of the training data collections, is analyzed using the trained machine learning model to provide an anomaly score. The anomaly score can be based at least in part on feature anomaly scores, which indicate anomality of individual data elements of the inference data collection.

Abstract: Update elements allow logical data object instances, and change requests therefor, to be converted from a first schema to a second schema. For logical data object instances, a transformation function can add nodes to, or remove nodes from a hierarchy of the first schema, or rearrange them. Use of the transformation function can allow second schemas to be defined that change a node hierarchy, yet can be used with existing logical data object instances in the first schema. Changes to logical data object instances can be converted using a mapping or a transformation function, such as a transformation function useable to convert logical data object instances between the first and second schemas. The migration of change requests to the second schema can allow logical data object instances having previously scheduled change requests to be updated from the first schema to the second schema prior to execution of the change request.

Abstract: Update elements allow logical data object instances, and change requests therefor, to be converted from a first schema to a second schema. For logical data object instances, a transformation function can add nodes to, or remove nodes from a hierarchy of the first schema, or rearrange them. Use of the transformation function can allow second schemas to be defined that change a node hierarchy, yet can be used with existing logical data object instances in the first schema. Changes to logical data object instances can be converted using a mapping or a transformation function, such as a transformation function useable to convert logical data object instances between the first and second schemas. The migration of change requests to the second schema can allow logical data object instances having previously scheduled change requests to be updated from the first schema to the second schema prior to execution of the change request.