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 redirected 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: 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: Systems and methods include monitoring of one or more software applications to determine a value of a first metric associated with instances of a first process, the first process including steps executed by the one or more software applications, determination that the value of the first metric has exceeded a threshold associated with the first process in a first number of ongoing instances of the first process, determination that the first number is greater than a first count limit associated with the first process, and, in response to the determination that the first number is greater than the first count limit, sending of an error message to a user associated with each of the ongoing instances of the first process.

Abstract: Systems and methods include monitoring of one or more software applications to determine a value of a first metric associated with instances of a first process, the first process including steps executed by the one or more software applications, determination that the value of the first metric has exceeded a threshold associated with the first process in a first number of ongoing instances of the first process, determination that the first number is greater than a first count limit associated with the first process, and, in response to the determination that the first number is greater than the first count limit, sending of an error message to a user associated with each of the ongoing instances of the first process.

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: A request is received to schedule a new software process. Description data associated with the new software process is retrieved. A workload resource prediction is requested and received for the new software process. A landscape directory is analyzed to determine a computing host in a managed landscape on which to load the new software process. The new software process is executed on the computing host.

Abstract: Methods, systems, and computer-readable storage media for receiving, from a first component and by a second component in a cloud platform, a call, a token, and a first client certificate, determining, by the second component, a first client identifier associated with the first component, and determining, by the second component, that the first client identifier is included in a manifest of the token, the manifest defining at least a portion of a communication path between components within the cloud platform, and in response: executing functionality responsive to the call.

Abstract: Methods and systems are used for achieving interface design consistency across micro services. As an example, a user interface (UI) training request including at least a set of reference objects is received, the set of reference objects including at least a set of reference UIs. A user interface behavior reference model (UIBRM) is trained to generate a trained UIBRM by analyzing reference UI displays rendered on a browser in response to interactions with the set of reference UIs. A UI displays assessment request including at least a set of development objects is received, the set of development objects including at least a set of development UIs. A UI displays assessment is performed to generate an assessment of development UI displays by comparing the trained UIBRM to the development UI displays rendered on the browser in response to interactions with at least a subset of the set of development UIs.

Abstract: Methods, systems, and computer-readable storage media for providing a VPDL within a data exploration system, storing enterprise-provided data in the VPDL, the enterprise-provided data including enterprise data from an enterprise system and data lake data from an enterprise data lake, importing, from an external data source, external data, automatically identifying associations between a sub-set of the enterprise-provided data and a sub-set of the external data and storing correlation data in the VPDL in response to an association, and reading at least a portion of the enterprise-provided data, at least a portion of the external data, and at least a portion of the correlation data, the data exploration tool being configured to generate one or more of visualizations and analytics by processing the at least a portion of the enterprise-provided data, the at least a portion of the external data, and the at least a portion of the correlation data.

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: Various examples are directed to systems and methods for executing a computer-automated process using trained machine learning (ML) models. A computing system may access first event data describing a first event. The computing system may execute a first ML model to determine an ML characterization of the first event using the first event data. The computing system may also apply a first rule set to the first event data to generate a rule characterization of the first event. The computing system may determine an output characterization of the first event based at least in part on the rule characterization of the first event and determine to deactivate the first rule set based at least in part on the ML characterization of the first event.

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: 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: Methods, systems, and computer-readable storage media for receiving, by an IEE and from a target service, user action metadata and target information, the user action metadata representing an action of a user to the target service, determining that an integration recommendation is to be issued to the source service at least partially in response to the user action metadata, the integration recommendation recommending a service integration of the target service with the source service; and automatically executing, by the IEE, integration of the target service with the source service by calling an extension framework to generate an extension, the extension being installed in the source service, and calling a configuration framework to configure the source service having the extension installed therein, wherein, in response to the service integration, a user interface of the source service is populated with one or more fields of a user interface of the target service.

Abstract: A request is received to create a graph database from one or more relational databases. For each relational database, data objects in the relational database are identified. For each data object, a graph data object corresponding to the data object is created. The graph data object is linked to the data object. A set of associated data objects in the relational database are determined, and for each associated data object, an associated graph data object is created if a graph data object corresponding to the data object does not exist. For each created graph data object, a graph data relation object is created that represents a relationship between the graph data object and the associated graph data object. Created graph data objects, associated graph data objects, and graph data relation objects are stored in the graph database. The graph database is provided to one or more applications.

Abstract: Methods, systems, and computer-readable storage media for receiving, from a first component and by a second component in a cloud platform, a call, a token, and a first client certificate, determining, by the second component, a first client identifier associated with the first component, and determining, by the second component, that the first client identifier is included in a manifest of the token, the manifest defining at least a portion of a communication path between components within the cloud platform, and in response: executing functionality responsive to the call.

Abstract: Embodiments implement a survey and result analysis cycle combining user experience and software operations data. A central survey engine receives from a survey designer, a configuration package specifying one or more of the following survey attributes: survey questions; operational data relevant to the survey for collection; rules; a target user group; and a survey triggering event. In response, the survey engine collects applicable operational data from software being evaluated, determines the actual users to be targeted by the survey, and promulgates the survey. Feedback from the survey is received and stored as a package including both the experience data (e.g., survey questions/responses) and operational data (e.g., specific operational data collected from the software that is relevant to the survey questions). This package is sent to a vendor to assist in analyzing the experience of the user of the software, and also to potentially devise valuable questions for a follow-up survey.

Abstract: Systems and methods include reception of first data representing each of a plurality of calls transmitted between services of a service landscape, the first data representing each call comprising a respective value of an identifier, identification of a first plurality of the plurality of calls as comprising a same value of the identifier, determination, for each outbound call of the first plurality of calls, of a hypothesis associating the outbound call with a time delay relative to a preceding inbound call of the first plurality of calls, reception of second data representing each of a second plurality of calls transmitted between the services of the service landscape while a latency between two services of the service landscape is increased, the second data representing each of the second plurality of calls comprising a second same value of the identifier, determination of an expected timing of each outbound call of the second plurality of calls based on the hypotheses and the increased latency, comparison

Abstract: 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). The system is also 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.