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

Florian Thomas

Alexander Lingg

Georg Storz

Wolfgang Degenhardt

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

Jürgen Specht

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

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.

Generation of inconsistent testing data

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.

Selective recommendation and deployment of extensions in low-code approach

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.

Landscape model verification system

Growing community of inventors

Heidelberg, Germany

Volker Driesen