Abstract: In an implementation, providing phased configuration changes with fallback option includes creating, using a context manager (CM), a new context. Using the CM, deploying a new configuration-variant. Using the CM, the new context is assigned for use by users connecting to a database schema of the new context. Using the CM, a determination is made to phase out use of the new configuration-variant. The new context is cleaned up.

Abstract: A computer-implemented method includes translating into a routing configuration, tenant-specific preferences for primary and secondary datacenter locations. A service mesh is set up for communication between services within and across the primary and secondary datacenter locations. Service persistencies with endpoints in datacenter locations are used to configure replication agents between the service persistencies. Using service endpoints, configuring Virtual Services that implement the service mesh. An Ingress Gateway is configured to route end user requests into the service mesh to a first service instance in the tenant-selected primary datacenter. According to the tenant-specific preferences, data replication is configured to copy data to redundant storage. Using endpoints of persistent storage replication agents for each service persistence in the tenant-selected primary datacenter, configuring persistent storage replication agents for each service persistence in the tenant-selected primary datacenter.

Abstract: In a computer-implemented method for a context-aware personal application memory (PAM), using an Application Memory Interface (AMIF) and to create captured data from one or more software applications, data related to user actions with the one or more software applications is captured. Using the AMIF and to create enhanced data, the captured data is enhanced with metadata, data, and semantic relations. Using the AMIF and to create filtered data, the enhanced data is filtered. The filtered data is sent by the AMIF to a personal application memory (PAM).

Abstract: In an implementation of a computer-implemented method: to create extracted data records, an extract filter is instructed to extract relevant data records from log messages of two runs of a software pipeline. To create diff records using the extracted data records, a diff filter is instructed to compare and identify differences in messages between the two runs, where the diff records are amended with labeled data status information of a software pipeline run the extracted data records have been taken from. A recommendation engine is instructed to execute a machine-learning model training with the diff records. The recommendation engine is called to analyze the diff records for a failure-indicator. A determination is made that a failure causing the failure-indicator has been corrected in a later run of the software pipeline. A change is identified in a configuration or version of a software application associated with a correction. A failure-indicator-solution combination is generated.

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: 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: A generic command request, including a command and command input data from the client computing device is received by a command line interface (CLI) backend and from a client CLI on a client computing device. A platform service for the command is determined by the CLI backend and based on command metadata associated with the command. The command input data is mapped by the CLI backend based on the command metadata associated with the command to a platform service application programming interface (API) associated with the platform service. The platform service API is called by the CLI backend based on the mapping. A client-side script defined for the generic command request together with response data received from the platform service API is returned by the CLI backend and to the client CLI for execution in a local script engine.

Abstract: A verification of an upgraded software system is determined to be unsuccessful. A recording of inbound and outbound calls is stopped in the upgraded software system. Following a roll back of the upgraded software system, starting a previous software system version is started. Inbound external communications are temporarily blocked for the previous software system version. Outbound external communication are redirected. The recording is replayed to re-create data in the previous software system version. External communication requests are replied to with responses from the recording. Number ranges for are checked completeness.

Abstract: In an implementation, a trigger generator module of a graph business object (GBO) factory creates trigger code. After replicating changes to an active business graph, the GBO factory: 1) for a GBO/graph business relation (GBR) create or delete action, executing trigger code of a specified trigger for a respective GBO/GBR type or 2) for a GBO/GBR update action of GBO/GBR attributes, executing trigger code of a specified trigger for a respective GBO/GBR type and attribute. Creating a temporary recommendation node (TRN) is created with the trigger code and using the GBO factory.

Abstract: Systems and methods include reception of a first service call from a first service at a second service, generation, at the second service, of a response to the first service call, determination, at the second service, of an amount of hardware resources consumed by the second service during generation of the response to the first service call, generation, at the second service, of a first resource consumption token identifying the amount of hardware resources consumed by the second service during generation of the response to the first service call, determination of a first identifier of a type of hardware on which the second service is executing, and return of the response, the first resource consumption token and the first identifier of the type of hardware to the first service.

Abstract: In an example embodiment, a solution is provided that allows a user to submit a document. Information can be obtained from the document using optical character recognition (OCR) or other techniques. This information can then be used to identify one or more workflows that pertain to the document. The one or more workflows may be ranked using machine learning techniques and presented to the user. Once the user selects a desired workflow, the information obtained from the document can then be used to automatically complete at least a portion of the workflow, for example by prefilling one or more fields in a form.

Abstract: A secure side car (SSC) manager reads development artifacts and an application configuration file. Stubs and skeletons are created for inter-process communication between a main application and one or more SSC library containers, the stubs and skeletons based on SSC configurations specified in the application configuration file. Main application code is compiled for the main application. An application container image is created for the main application and a SSC image for each of the one or more SSC library containers. An application container corresponding to the application container image and a SSC library container corresponding to each SSC image or each of the one or more SSC library containers is deployed. A service mesh proxy, which controls network access for libraries within each SSC library container, is configured.

Abstract: In an example embodiment, a solution is provided that allows a user to submit a document. Information can be obtained from the document using optical character recognition (OCR) or other techniques. This information can then be used to identify one or more workflows that pertain to the document. The one or more workflows may be ranked using machine learning techniques and presented to the user. Once the user selects a desired workflow, the information obtained from the document can then be used to automatically complete at least a portion of the workflow, for example by prefilling one or more fields in a form.

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: In an implementation, a mesh assembler receives a request for a new cloud-computing application tenant, where the request includes an assembly profile. The mesh assembler calls a deployment manager for applications and services that need to be custom deployed according to the assembly profile. The mesh assembler calls a routing configurator, which looks up services with certain attributes and configures services with certain attributes into a service mesh.

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: Systems and methods include reception of a first service call from a first service at a second service, generation, at the second service, of a response to the first service call, determination, at the second service, of an amount of hardware resources consumed by the second service during generation of the response to the first service call, generation, at the second service, of a first resource consumption token identifying the amount of hardware resources consumed by the second service during generation of the response to the first service call, determination of a first identifier of a type of hardware on which the second service is executing, and return of the response, the first resource consumption token and the first identifier of the type of hardware to the first service.

Abstract: In an implementation, a shutdown coordinator sends an instruction to a router to no longer assign new sessions to an application instance, where the application instance is to be shut down. The shutdown coordinator sends a request to the application instance to export associated sessions for handover to a new application instance. The shutdown coordinator receives an indication from the application instance that no further requests can be processed by the application instance since all associated sessions have been exported to an external session storage. The shutdown coordinator instructs the router to redirect requests to the application instance to the new application instance. The shutdown coordinator shuts down the application instance. The shutdown coordinator deletes remaining sessions of all associated sessions from the external session storage after a defined timeout period.

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, a shutdown coordinator sends an instruction to a router to no longer assign new sessions to an application instance, where the application instance is to be shut down. The shutdown coordinator sends a request to the application instance to export associated sessions for handover to a new application instance. The shutdown coordinator receives an indication from the application instance that no further requests can be processed by the application instance since all associated sessions have been exported to an external session storage. The shutdown coordinator instructs the router to redirect requests to the application instance to the new application instance. The shutdown coordinator shuts down the application instance. The shutdown coordinator deletes remaining sessions of all associated sessions from the external session storage after a defined timeout period.