Sap Se

Sap Ag

Volker Driesen

Peter Eberlein

Martin Mayer

Juergen Specht

Steffen Meissner

Uwe Schlarb

Ulrich Auer

Andrey Engelko

Arne Harren

Ralf-Juergen Hauck

Welf N Walter

Immo-Gert Birn

Wieland Hoprich

Kerstin Hoeft

Christian Stork

Nicolai Jordt

Lars-Eric Biewald

Heiko Konrad

Christoph Luettge

Andreas Jahr

Torsten Ziegler

Andrea Neufeld

Wulf Kruempelmann

Peter Schreiber

Levke Bentzien

Thomas Ullrich

Stefan A Baeuerle

Martin Hartig

Bernhard Thimmel

Olaf Meincke

Matthias Lehr

Thomas Brodkorb

Karsten Fanghaenel

Christiane Valentin

Andre Haferkorn

Markus Gebhard

Christian Lutter

Matthias Mittelstein

Erwin Burkhardt

Frank Brunswig

Markus Cherdron

Thomas Vogt

Andreas Hoffner

Oleg Koutyrine

Tobias Karpstein

Frank-Martin Haas

Stefan Beck

Tim Back

Joerg Schmidt

Iouri Loukachev

Matthias Jensen

Fabrizio Muscarella

Anton Georgiev

Lars Spielberg

Alexander Lingg

Georg Storz

Florian Thomas

Wolfgang Degenhardt

Jürgen Specht

Peter Muessig

Lazar Borissov

Andreas Zimmermann

Roy Abitbol

Miki Ben-Zeev

Franklin Herbas

Ralph Radermacher

Silke Arians

Thomas Saelinger

Frank Heine

Ivan Mihalev

Dencho Mitev

Welf Walter

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. An updated adjustment to the one or more database tables is provided based on the migration test results.

Refactoring workbench with migration support

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.

Low-development system for dynamic information queries

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.

Role management system based on an integrated role recommendation engine

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.

Interactively extending machine-learning-generated rules and recommendations

Methods, systems, and computer-readable storage media for restoring microservice persistencies. A disruptive event potentially affecting a computing system functionality and data synchronization of one or more applications and one or more microservices is determined. Each application and microservice stores respective data records in separate data tables. A restoration of the computing system functionality is determined. A synchronization process is initiated by using a synchronization identification engine. Data record synchronization is verified across a plurality of data tables, by matching record identifiers according to a call mapping between the applications and the microservices. The data record synchronization is restored across the plurality of data tables. An access to the applications and the microservices is reactivated.

Consistent restore across microservice persistencies

Systems and methods include determination of a plurality of artifacts of a first data schema and a first view schema of a first database version to be updated in a second database version, determination of target structures of a plurality of runtime objects for the second database version based on the artifacts, and loading of current structures of the runtime objects into a runtime buffer of a first work process connected to the first view schema. While the first work process serves first incoming requests using the runtime buffer and the first view schema, the runtime objects are updated to the determined target structures in the first data schema and in a second view schema of the second database version, a second work process is connected to the second view schema, and the second work process serves second incoming requests, the second view schema and the updated first data schema.

Downtime-minimized database version deployment

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.

Computer-automated processing with rule-supplemented machine learning

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.

Deriving and augmenting access control for data lakes

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.

Schema stack management system for managing extensions through upgrades

Methods, systems, and computer-readable storage media for providing a first access schema in a view layer of the database system, the first access schema including first database objects and projection views, each projection view corresponding to a second database object of second database objects in a data persistency, transitioning access of sessions of an application from the data persistency to the first access schema, executing at least a portion of the maintenance procedure in the database system, the at least a portion of the maintenance procedure changing one or more second database objects within the data persistency, creating a second access schema, and transitioning access of the sessions of the application from the first access schema to the second access schema, transitioning access of the sessions of the application from the second access schema to the data persistency, and removing the second access schema from the database system.

Using view layer in database system for zero downtime upgrade

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.

Holistic measurement of microservice-based application resource consumption

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.

Document initiated intelligent workflow

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.

Automatic creation and synchronization of graph database objects

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 versions of the rows of the database table, and re-direction of incoming user requests to the second application server executing the second version of the application.

Management of two application versions in one database table

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.

Sensitivity in supervised machine learning with experience data

Growing community of inventors

Heidelberg, Germany

Volker Driesen