Abstract: A computer-implemented method for generic tenant data replication for disaster recovery, includes regularly checking, by a primary replication agent on a primary site, for changes to a primary change log on the primary site. Based on an insert of a new change record in a secondary change log on a secondary site, triggering a stored procedure from secondary stored procedures on the secondary site, that extracts a change operation and data from the secondary change log and performs the changed operation on a secondary tenant table on the secondary site. By a secondary replication agent on the secondary site and from the primary replication agent, receiving a call not containing a sequence id for a change record marked as completed in the secondary change log, and deleting the change record. Using a replication agents control plane, providing central monitoring for the primary replication agent and the secondary replication agent.

Abstract: A computer-implemented method for clean slate application transformation with transparent legacy system access, includes configuring an access and integrate legacy system (AILS) legacy data access module for access to a legacy application. Master data migration of a required subset of master data to a target application is triggered. Using the AILS legacy data access module and an AILS user interface (UI) widget in a target application UI, switching usage to the target application. Using the AILS legacy data access module and the AILS UI widget, closing usage of the legacy application for changes. The legacy application is decommissioned.

Abstract: A computer-implemented method for generic tenant data replication for disaster recovery, includes regularly checking, by a primary replication agent on a primary site, for changes to a primary change log on the primary site. Based on an insert of a new change record in a secondary change log on a secondary site, triggering a stored procedure from secondary stored procedures on the secondary site, that extracts a change operation and data from the secondary change log and performs the changed operation on a secondary tenant table on the secondary site. By a secondary replication agent on the secondary site and from the primary replication agent, receiving a call not containing a sequence id for a change record marked as completed in the secondary change log, and deleting the change record. Using a replication agents control plane, providing central monitoring for the primary replication agent and the secondary replication agent.

Abstract: The disclosure generally describes methods, software, and systems for to data access control for applications using large language models (LLM). A request to access attributes of an application object is received from a user. A LLM object access layer defining an access level of the user to each attribute of the application object is determined. The LLM object access layer is queried to determine accessible attributes of the attributes of the application object. A response is provided based on the accessible attributes of the application object to the user.

Abstract: A computer-implemented method for data configuration and export includes reading, by a data set export system (DSES), a configured export data set (EDS) definition (EDSD) instance. The DSES reads related metadata. The DSES reads keys of an application object or database table from a customer database. The DSES follows defined relations to more application objects to be exported. The DSES stores determined keys of application objects and database tables, where, for each application object and database table, the DSES writes identified keys to an export keys database table. The DSES exports a data set, where the data set includes specified attributes from customer data associated with export keys read by the DSES.

Abstract: A computer-implemented method includes identifying, using a spatial controller for application object graph (SCAOG) and a spatial computing system user interface (UI), an object type root node associated with a particular object type, where the object type root node has relations to all graph database object (GDO) instances in a graph database with a particular attribute and with a particular value. Fact sheets associated with the GDO instances with the particular attribute and with the particular value are displayed. A particular GDO instance is focused. Initially provided GDO types related to the particular object type are revised with revised GDO types of a particular relevance. The revised GDO types are focused. A graph traversal from the GDO type of revised GDO types to the GDO types related to the revised GDO types is executed. The revised GDO types are grouped based on results of the graph traversal.

Abstract: The disclosure generally describes methods, software, and systems for re-architecting an existing software product provided as a service. A user input specifying current databases used for an old software product version is received. The user input includes a definition of a target persistency layout including databases assigned for storing a new software product version, the old software product version including one or more database tables and the new software product version including an adjustment to the one or more database tables to generate one or more target database tables. Sizes of the one or more database tables are read from a catalog of a database. A migration assessment of the new software product version is generated based on the sizes of the one or more database tables. A migration test of the one or more database tables is executed using the migration assessment to generate migration test results.

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: A computer-implemented method includes adding, using a label management system (LMS), an object-specific multi-value label extension field to an application object (AO) associated with a software application. Using the LMS and to an AO user interface (UI) associated with the AO, a multi-value label UI extension field is added. Using the LMS, a label value table and associated label value maintenance UI is created to maintain label values. Using the LMS, a mapping table is created between the object-specific multi-value label extension field of the AO and the label value table. Using the LMS and the AO UI, label values assigned to the AO are read using foreign key retrieval from the mapping table and the label value table.

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: A computer-implemented method includes processing, by an Intelligent Root Cause Analyzer (IRCA), an error message. The error message is categorized by an Error Categorizer of the IRCA as an Error Category. A code change related to the Error Category is searched for by the IRCA. An ID of an impacted application or service and a Change Category is received by a Code Repository and Build and Pipeline. The Error Category and the Change Category are compared by the IRCA. A Service Dependency Graph is searched by a Dependency Reader of the IRCA for services that are called by the impacted application or service. A Monitoring system is queried by the IRCA for potentially different error messages and derived Error Categories from the services called by the impacted application or service.

Abstract: The disclosure generally describes methods, software, and systems for to data access control for applications using large language models (LLM). A request to access attributes of an application object is received from a user. A LLM object access layer defining an access level of the user to each attribute of the application object is determined. The LLM object access layer is queried to determine accessible attributes of the attributes of the application object. A response is provided based on the accessible attributes of the application object to the user.

Abstract: Using a data analysis activity (DAA) definition, a DAA associated with a software application is triggered. An instance selector query is executed to generate a set of instance values as input for a data query. A data query to generate a data set is executed using instance values of the set of instance values. Using the data set, an instruction for an artificial intelligence (AI) engine is computed. A result based on the instruction for an AI engine is received from the AI engine. The result based on the instruction for an AI engine is stored into an AI Result History Store. Prior results from earlier DAA executions is read from the AI Result History Store. A notification to a defined target audience is sent using the software application.

Abstract: A computer-implemented method includes customizing artifacts of a large language model (LLM). A generative artificial intelligence (AI) (genAI) function of a software application that interfaces with the LLM and is instrumented for customer-side management is activated, as a genAI application. A verification test is activated to determine whether interactions between the LLM and genAI application generate expected results. Based on a result of the verification test, either adjusting the artifacts of the LLM or updating the genAI function of the software application. A new verification test is defined to determine whether interactions between the LLM and genAI application generate expected results. The new verification test is activated.

Abstract: The disclosure generally describes methods, software, and systems for re-architecting an existing software product provided as a service. A user input specifying current databases used for an old software product version is received. The user input includes a definition of a target persistency layout including databases assigned for storing a new software product version, the old software product version including one or more database tables and the new software product version including an adjustment to the one or more database tables to generate one or more target database tables. Sizes of the one or more database tables are read from a catalog of a database. A migration assessment of the new software product version is generated based on the sizes of the one or more database tables. A migration test of the one or more database tables is executed using the migration assessment to generate migration test results.

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: 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: In a computer-implemented method queries, a task executor queries for which tasks of a list of tasks are relevant for a software application installation. The task executor queries for an up-to-date sequence of relevant tasks. The task executor executes the relevant tasks as a completion sequence of tasks used to compute an individual order valuation matrix organized with task identifications in columns and rows. The task executor sends the individual order valuation matrix to a task ordering service. The task executor provides which tasks of a list of tasks are relevant for the software application installation. The task executor receives, from the task ordering service, a newly up-to-date sequence of tasks based on a holistic order valuation matrix.

Abstract: Using a data analysis activity (DAA) definition, a DAA associated with a software application is triggered. An instance selector query is executed to generate a set of instance values as input for a data query. A data query to generate a data set is executed using instance values of the set of instance values. Using the data set, an instruction for an artificial intelligence (AI) engine is computed. A result based on the instruction for an AI engine is received from the AI engine. The result based on the instruction for an AI engine is stored into an AI Result History Store. Prior results from earlier DAA executions is read from the AI Result History Store. A notification to a defined target audience is sent using the software application.

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.