Abstract: Data values of metrics for a plurality of functional entities are aggregated, the aggregating producing aggregated values for the respective metrics. A set of the aggregated values is produced for the respective metrics. Based on the set of aggregated values, an interactive visualization of the metrics is generated, the interactive visualization including visual indicators based on the aggregated values for the respective metrics across a plurality of time intervals. The interactive visualization is selectable to focus on a portion of the interactive visualization.

Abstract: A method for modifying an original data object in a database using a single command is disclosed. The single command is first validated. Once the command has been validated a ghost data object is created using the new definition. The ghost data object is populated with data from the original object. The transaction log records from the original data object are applied to the ghost data object. The original data object is then locked for exclusive access. The final transaction log records from the original data object are applied to the ghost data object. In the final step the database switches to the new data object. Dependent objects are automatically transformed as part of the same operation.

Abstract: There is provided a computer-implemented method of aggregating data. An exemplary method comprises receiving an aggregation scheme and generating numerous first aggregations by aggregating data at a first level of granularity. The data may be associated with a time and stored in a first table. Further, generating the numerous first aggregations may be based on the time and the aggregation scheme. The exemplary method further comprises generating a second aggregation by aggregating the first aggregations at a second level of granularity based on the aggregation scheme. The second level of granularity may comprise the first level of granularity.

Abstract: A method for modifying an original data object in a database using a single command is disclosed. The single command is first validated. Once the command has been validated a ghost data object is created using the new definition. The ghost data object is populated with data from the original object. The transaction log records from the original data object are applied to the ghost data object. The original data object is then locked for exclusive access. The final transaction log records from the original data object are applied to the ghost data object. In the final step the database switches to the new data object. Dependent objects are automatically transformed as part of the same operation.