Methods, systems, and computer-readable media for generating a statistically covaried machine learning model for performance measurement of service providers. The method receives a configuration file that includes one or more parameters associated with a plurality of individuals and parses it to generate and executing the database query on input data to generate sets of tabulated data of individuals of the plurality of individuals. The method next determines one or more measures of service providers listed in the configuration file using two or more tabulated data of individuals from the sets of tabulated data of individuals. The method finally generates a covaried machine learning model by training a machine learning model by statistically covarying measures and using them as training data.

A Sort Lists instruction is provided to perform a sort and/or a merge operation. The instruction is an architected machine instruction of an instruction set architecture and is executed by a general-purpose processor of the computing environment. The executing includes sorting a plurality of input lists to obtain one or more sorted output lists, which are output.

Systems, methods and computer program products for high-performance, low latency start-up of large shared media files. A method for low latency startup with low defect playback commences upon identifying a first media file having a first format to be converted to a second media file having a second format. A scheduler divides the first media file into multiple partitions separated by partition boundaries. The method continues by converting the partitions into respective converted partitions that comport with the second format. Determinations as to the position of the partition boundaries is made based on measurable conditions present at a particular moment in time. Different formats receive different treatment based on the combination of characteristics of the first format, characteristics of the second format, as well as on characteristics of measurable conditions present at the moment in time just before conversion of a segment.

Disclosed herein are systems and methods for query processing with restrictions in a database clean room. In an embodiment, a system receives a query directed to a combination of a first source dataset from a first database account of a distributed database and a second source dataset from a second database account of the distributed database. The system generates an approved statements table that contains database statement language that can be executed against the combination of the first and second source datasets. Based on determining that the approved statements table includes the query, the system executes the query to produce results data, and stores the results data in the first database account.

Systems and methods for embodiments of artificial intelligence systems for identity management are disclosed. Embodiments of the identity management systems disclosed herein may support the creation, association, searching, or visualization of any relevant context to identity management assets for a variety of purposes, including for informing the identity management systems' manual or automated decisions, processes or workflows.

Compiled portions of code generated to perform a query plan at a query engine may be shared with other query engines. A data store, separate from the query engines, may store compiled portions of query code generated for different queries. If a query engine does not have a locally stored compiled portion of query code, then the separate data store may be accessed in order to obtain a compiled portion of query code, allowing reuse of compiled query code across different queries engines for queries directed to different databases.

First and second database accounts respectively share first and second source datasets with one another in a data clean room. The first database account stores a first approved-statements table that contains database statements that are permitted by the first database account to be executed against a combination of the shared first source dataset and the shared second source dataset. The second database account stores a second approved-statements table that contains database statements that are permitted by the second database account to be executed against the combination of the shared first source dataset and the shared second source dataset. One or more queries received from the first database account that are included in the second approved-statements table are processed, as are one or more queries received from the second database account that are included in the first approved-statements table.

A server in a cloud-based environment interfaces with storage devices that store shared content accessible by two or more users. Individual items within the shared content are associated with respective object metadata that is also stored in the cloud-based environment. Download requests initiate downloads of instances of a virtual file system module to two or more user devices associated with two or more users. The downloaded virtual file system modules capture local metadata that pertains to local object operations directed by the users over the shared content. Changed object metadata attributes are delivered to the server and to other user devices that are accessing the shared content. Peer-to-peer connections can be established between the two or more user devices. Object can be divided into smaller portions such that processing the individual smaller portions of a larger object reduces the likelihood of a conflict between user operations over the shared content.

A process for recommending pre-constructed queries in data analytics includes writing different records to a correlation data structure correlating different data classifications of data to different queries and, subsequent to the writing, establishing a communicative connection by a data analytics application to an underlying database. Thereafter, a data model for data in the database may be constructed in the data analytics application and at least one of the different queries may be selected in the correlation data structure that correlates to the classification of the data in the data model. Finally, the selected one of the different queries may be displayed in the data analytics application to an end user so as to provide an intelligent recommendation for the addition of the selected one of the different queries without requiring the end user to alone and without assistance discover the suitability of the selected one of the different queries.

Some embodiments provide a method for quantifying complexity of database queries that reference database objects in a database. The method identifies a database query that includes a reference to a set of one or more database objects in the database. The method identifies a set of properties associated with a set of components of the identified query, and computes, from the identified set of properties, a complexity indicator that represents a complexity expression of the database query.