Abstract: In an implementation, a request to compute a new machine-learning model for storage in a data lake is received by a role management system (RMS). A role history data is read by the RMS from a role management application and trace history data from a trace application. Metadata read from a plurality of persistencies is stored into the data lake as metadata history. The historic data and metadata history is read by a role recommendation engine (RRE) integrated into the RMS. The RRE, computes the new machine-learning model.

Abstract: In an implementation, one or more rules associated with a DO from a rules database is read by a rule user interface (UI) plug-in associated with a data object (DO) maintenance UI. The one or more rules for the DO to fields associated with the DO on the DO maintenance UI are related by the rule UI plug-in. The rule UI plug-in, using the related one or more rules, auto-populates and validates received values for the fields associated with the DO on the DO maintenance UI. The rule UI plug-in determines that one or more violations of the one or more rules has occurred and displays an additional UI with mutually exclusive options for mitigating the determined one or more violations of the one or more rules. A new rule is saved into the rules database.

Abstract: Implementations include a schema stack management system that enables zero-downtime during execution of maintenance procedures on application systems having schema stacks including one or more customer-provided schema extensions.

Abstract: Systems and methods include configuration of a first database view to read from common versions of rows of a database table or from rows of the database table associated with a first version of the application, the database table storing application content, configuration of a first application server to write to the common versions of rows of the database table, configuration of a second database view to read from common versions of rows of the database table or from rows of the database table associated with a second version of the application, configuration of a second application server to write to rows of the database table associated with the second version of the application, modification of the database table to include rows associated with the second version of the application while the first application server executes the first version of the application and incoming user requests are directed to the first application server, configuration of the second application server to write to common version

Abstract: Systems and methods include configuration of a first database view to read from common versions of rows of a database table or from rows of the database table associated with a first version of the application, the database table storing application content, configuration of a first application server to write to the common versions of rows of the database table, configuration of a second database view to read from common versions of rows of the database table or from rows of the database table associated with a second version of the application, configuration of a second application server to write to rows of the database table associated with the second version of the application, modification of the database table to include rows associated with the second version of the application while the first application server executes the first version of the application and incoming user requests are directed to the first application server, configuration of the second application server to write to common version

Abstract: In an example embodiment, a process is introduced into a machine learned model where additional results are output by the machine learned model in addition to those results that would be obtained through use of the trained model itself. In some example embodiments, these additional results may be random or semi-random to introduce results that might otherwise not have been recommended by the machine learned model. By introducing such additional results in a controlled way, it becomes possible to reduce biases caused by a self-reinforcing feedback loop while still presenting users with accurate machine learned model results.

Abstract: Systems and methods include determination of first constraints on columns of database tables of a test tenant system based on metadata associated with the columns of database tables, determination of second constraints on the columns of database tables based on data stored in the columns of database tables of the test tenant system, identification of inconsistent data stored in columns of database tables of a plurality of tenant systems based on the first constraints and the second constraints, adding of records to the database tables of the test tenant system based on the identified inconsistent data, and execution of a test based on the database tables of the test tenant system including the added records.

Abstract: Implementations include querying metadata of data objects to define a sub-set of data objects, each data object in the sub-set of data objects including a generic text field and/or an attachment field, and, for each data object in the sub-set of data objects, processing historical data of a data object to identify a set of data types, the historical data stored within a field of a table of a database system, providing a recommendation for a first extension corresponding to a first data type, and receiving user input indicating acceptance of the recommendation for the first extension and, in response, automatically providing extension code that is executable to add a field extension to the table and to modify a UI of an application for input of values corresponding to the first data type, and executing the extension code to deploy the extension and to modify the UI.

Abstract: Systems and methods include determination of a plurality of call map clusters, each call map cluster associated with a respective type of external call, two or more of a plurality of services, and a service call model modelling, for each of the two or more of the plurality of services, the issuance of outgoing service calls in response to received service calls. A bot is deployed proximate to each of the plurality of services, and each deployed bot is associated with a respective configuration to issue outgoing service calls in response to received service calls as modelled by the service call models for each of one or more services proximate to the deployed bot.

Abstract: Implementations include setting a session variable of each of a plurality of tenants to a first timestamp, importing, after the first timestamp, a first set of content to a shared container within a database system, during importing, each tenant in the plurality of tenants accessing pre-import data stored in the shared container based on the session variable being set to the first timestamp, and after importing the first set of content to the shared container, un-setting, at a second timestamp, the session variable of each of the plurality of tenants from the first timestamp, after the second timestamp, each tenant in the plurality of tenants accessing post-import data stored in the shared container.

Abstract: Various examples are directed to systems and methods for configuring an application that uses a plurality of microservices. A distributed configuration deploy service (DCDS) may generate a first set of microservice configuration parameter values for a first microservice of the plurality of microservices based at least in part on a first microservice configuration model for the first microservice and at least in part on a second configuration model for a second microservice of the plurality of microservices. A configuration application programming interface (API) may determine that the first set of microservice configuration parameter values do not conflict with first live data at the first microservice. The configuration API may also lock the first microservice; apply the first set of microservice configuration parameter values to the first microservice; and release the locking of the first microservice.

Abstract: In an example embodiment, access to a data set in a data lake can be specified using several approaches, based on the metadata and information attached. The metadata may be replicated from the original data source of the underlying data, and additional metadata may be modeled and stored to construct linkage information between data types. This linkage information may be used to automatically grant access to users to additional objects that are linked to objects that the user has explicit access to.

Abstract: Computer-implemented systems and computer-implemented methods include the following. A request to train a machine-learning (ML) model is received at a training broker. Anonymized data for training the model is obtained by the training broker from each individual data source of a plurality of data sources. The anonymized data is accessed through a data science schema being provided by anonymization of sensitive information of production data from each individual data source. Access to the anonymized data is provided to a data vendor for training the ML model using the anonymized data.

Abstract: Systems and methods include determination of first constraints on columns of database tables of a test tenant system based on metadata associated with the columns of database tables, determination of second constraints on the columns of database tables based on data stored in the columns of database tables of the test tenant system, identification of inconsistent data stored in columns of database tables of a plurality of tenant systems based on the first constraints and the second constraints, adding of records to the database tables of the test tenant system based on the identified inconsistent data, and execution of a test based on the database tables of the test tenant system including the added records.

Abstract: Systems and methods include reception of an instruction to initiate an inquiry associated with a first instance of a first object type, querying of a system for object metadata defining a plurality of fields of the first object type, generation of a first user interface presenting one or more of the plurality of fields, reception of user input to the first user interface, the user input specifying a first one or more fields of the presented one or more of the plurality of fields, generation of the inquiry specifying the first one or more fields and the first instance, transmission of the inquiry, reception of a response to the inquiry including data associated with the first one or more fields and the first instance, and instructing of a system to update the data associated with the first instance of the first object type based on the received data associated with the first one or more fields and the first instance.

Abstract: In an example embodiment, first a system is provided that enables tests created by a second entity to run with first entity data and configurations. A test module is able to assess test results with dynamic input (e.g., rule-based assessments). Second, a system is provided to run and test these test modules on the first entity system with first entity configuration and let the first entity define the test input and interpret the test results. This is not performed on all available tests, but only on the tests that are related to the change to be implemented.

Abstract: Implementations include setting a session variable of each of a plurality of tenants to a first timestamp, importing, after the first timestamp, a first set of content to a shared container within a database system, during importing, each tenant in the plurality of tenants accessing pre-import data stored in the shared container based on the session variable being set to the first timestamp, and after importing the first set of content to the shared container, un-setting, at a second timestamp, the session variable of each of the plurality of tenants from the first timestamp, after the second timestamp, each tenant in the plurality of tenants accessing post-import data stored in the shared container.

Abstract: Systems and methods include execution of a first application associated with a first access schema and a first data model in a data schema of a database system. While the first application is receiving incoming user requests, the first data model in the data schema is extended based on a second data model of a second application, a second access schema of the second application is prepared in the database system, data of the first data model in the data schema is migrated to the second data model, data of the first data model in the data schema is replicated to the second data model while the first application is receiving incoming user requests, the second application is executed, the incoming user requests are re-directed to be received by the executing second application, and the replication of data of the first data model in the data schema to the second data model is stopped.

Abstract: Systems and methods include reception of a first request to check code associated with a first service for compliance with one or more criteria, determination of a plurality of code components associated with the first service, execution of a code check of each of the plurality of code components, generation of a first service compliance statement associated with the first service based on results of the executed code checks, determination of a definition of the first product from a product repository, the definition listing a plurality of services on which the product depends, the plurality of services including the first service, identification of a compliance statement associated with each of the plurality services, and determination of a product compliance statement based on each of the identified compliance statements.

Abstract: In an example embodiment, access to a data set in a data lake can be specified using several approaches, based on the metadata and information attached. The metadata may be replicated from the original data source of the underlying data, and additional metadata may be modeled and stored to construct linkage information between data types. This linkage information may be used to automatically grant access to users to additional objects that are linked to objects that the user has explicit access to.