Abstract: Example embodiments involve a metrics collection system for collecting software usage metrics from one or more client devices at deployments. A computer, such as a server configured to execute the metrics collection system, collects software usage metrics (e.g., as a metrics submission from a client device) of the software product at the deployment, identifies a metrics type of the software usage metrics collected, assigns the software usage metrics to a metrics category, and calculates and updates a metrics score of the metrics category, based on the software usage metrics collected.

Abstract: One or more processors examine source code of one or more software packages that produce output messages and identify, in the source code, one or more call expressions that each represent a logging call. The one or more processors generate a number of search patterns for parsing output messages produced by the one or more software packages, wherein each of the search patterns is based on one or more arguments of a corresponding call expression of the one or more call expressions. The one or more processors further reduce the number of search patterns to be applied to the output messages produced by the one or more software packages to identify log entries among the output messages.

Abstract: One or more processors store rules for performing rules-based cleaning operations on a plurality of datasets, wherein each rule comprises one or more functions to be executed against a dataset during the rules-based cleaning operations, the one or more functions each having one or more associated conditions and actions, wherein the one or more actions are performed on the dataset responsive to the one or more associated conditions being satisfied. The one or more processors further apply the rules to each of the plurality of datasets to perform the rules-based cleaning operations. To apply the rules to a given dataset, the one or more processors identify an ordered list of the one or more functions to be executed with respect to the given dataset during the rules-based cleaning operations and determine, for each of the one or more functions, whether the given dataset satisfies one or more conditions associated with a respective function of the one or more functions.

Abstract: Data item deltas are generated for each of M updates of a plurality of updates, wherein M is greater than or equal to one, and a first first-level combined delta is generated representing N updates of the plurality of updates, wherein N is greater than M, and the N updates comprise the M updates and O=N?M other updates. A first second-level combined delta is generated representing J updates of the plurality of updates, wherein J is greater than N, and the J updates comprise the N updates and K other updates of the plurality of updates, wherein K=J?N. The deltas, the first first-level combined delta and the first second-level combined delta are stored for enabling subsequent reading of at least part of the data by accessing the data item, the first first-level combined delta and the first second-level combined delta.

Abstract: A data analysis system receives a data string comprising a natural language query pertaining to a dataset, wherein the dataset is associated with a data object model comprising a plurality of objects, and parses the data string to identify a plurality of individual words within the data string. The data analysis system identifies, based on the plurality of individual words, one or more objects of the plurality of objects, wherein the one or more objects are associated with the natural language query in the data string. The data analysis system further determines one or more artifacts that are based on the dataset, wherein each of the one or more artifacts is associated with one of the one or more objects and provides a response to the natural language query.