Sap Se

Sap Ag

Daniel Oberle

Gunther Stuhec

Jens Lemcke

Stefan Scheidl

Christian Drumm

Sebastian Wennemers

Sharad Dixit

Eduard Stelle

Ganapathy Subramanian

Vivek Krishnamurthy Dornal

Chandrashekhar Putcha

Martin Knauer

Soenke Scharnhorst

Tirdad Rahmani

A smart home hub may receive sensor information from a plurality of smart home objects (e.g., a washing machine, an electricity meter, etc.). The smart home hub may also generate smart home events based on the received sensor information. The sensor information may be received via a plurality of different communication protocols. An analytics cloud platform with built-in smart home support may receive smart home information from the smart home hub via an analytic querying protocol adapted to handle big data. The analytics cloud platform may process the received smart home information using a multi-platform query library and automatically analyze the processed data using predictive analytics. The analytics cloud platform may then arrange to display analysis results via an analytics cloud user interface.

Built-in cloud analytics support for smart home

A smart home hub may receive sensor information from a plurality of smart home objects (e.g., a laundry washing machine, an electricity power meter, etc.). The smart home hub may also generate smart home events based on the received sensor information. A central management plane component associated with a cloud-native application runtime may communicate with the smart home hub. The system may receive smart home events from the hub and, responsive to the received, arrange to execute smart home functions via a cloud infrastructure. For example, a function may arrange to reduce the cost of operating a smart washing machine. As a result of execution of at least one smart home function, the system may transmit a smart home response to the hub causing at least one of the smart home objects to perform a task (e.g., the washing machine may automatically run when the cost of electricity is low).

Cloud-native application runtime support for smart home sensor functions

The present disclosure involves systems, software, and computer implemented methods for creating an extension field across system tenants in a customer landscape. One example method includes receiving a request to add an extension field to an entity used in a customer landscape. Entity-using system tenants that use the entity are determined. Event-pairing information is provided to each entity-using system tenant to enable receiving of extension field events. Field attribute values for a set of field attributes for the extension field are received. A field creation event is generated that instructs a system tenant receiving the field creation event to create a local field that has a target state of the extension field in that system tenant. The field creation event is sent to each entity-using system tenant.

Field extension hub for extension fields across a customer landscape

In an example embodiment, a graphical user interface-based software tool is provided that uses integrated process information and information of a technical infrastructure to provide automatically-analyze integrations. The tool displays a list of integrations available with one or more corresponding software products linked to the first process by the linkage, each integration in the list of integrations identifying a separate software product with a separate application program interface (API) from the first software product.

Tool for mapping software objects and application program interfaces

A system and method including the reception of an input of a set of textual terms including a subject matter parameter value and an indeterminate term parameter value; automatically determining, by a machine learning process, the subject matter parameter value is subsumed within a specified data model including the indeterminate term parameter value; automatically processing the indeterminate term parameter value to execute a combination of literal, systemic, historical perspective, and teleological interpretations thereof to generate an overall assessment that includes a non-arbitrary interpretation of the indeterminate term parameter value; and presenting a representation of the set of textual terms expanded to include the non-arbitrary interpretation of the indeterminate term parameter value.

Automated support for interpretation of terms

Techniques and solutions are described for facilitating the determination of whether a query, such as a database query, may raise a compliance issue, such as whether restricted information associated with a particular database field is provided as an output of the query. A compliance tool receives a query that includes a plurality of query operations. At least one database field associated with a potential compliance issue is received. For example, the at least one database field may be associated with restricted information. The plurality of query operations are analyzed to determine whether information associated with the at least one database field is an output of the query to provide compliance results. The compliance results are output to a user.

Automated query compliance analysis

A plurality of data sources accessed by a software program are automatically determined by a compliance tool. At least a portion of the data sources are automatically associated with one or more formalized compliance terms using the compliance tool. At least a portion of the data sources is analyzed by the compliance tool using at least one formalized compliance norm that include at least one of the one or more formalized compliance terms to provide compliance results. The compliance results are output to a user.

Automated software compliance analysis

In an example embodiment, a triple to store in a triple store is received from a first application, the triple defining data having a subject, predicate, and object. The triple is sent to a triple store interface configured to store the triple in an in-memory database management system. Later, a request from a second application to query the triple store is received. A cloud-based triple store query server is used to process the request and generate an in-memory database management system query to the in-memory database management system. Results of the in-memory database management system query are received from the in-memory database management system. The result is sent to the second application for display to a user.

In-memory cloud triple store

Techniques and solutions are described for facilitating the determination of whether a software program may raise a compliance issue, such as whether processes invoked by the software program may involve the sending of protected information. A compliance tool automatically determines a plurality of processes invoked by a program. A plurality of the invoked processes are compared with a process compliance library comprising at least one process. The comparing produces compliance results. The compliance results are output to a user.

In a computer system, an ontology is derived from an original data source, such as from an relational database. At any time, a user can relate useful information to an ontology node, for example, by entering a document annotation into the ontology. Later, the user can retrieve the information through the annotation and view the document together with the ontology. An ontology update scheme keeps the document annotation available even if changes in the original data would remove the corresponding portions of the ontology. In the ontology, the scheme moves the annotations from a first ontology node to a second ontology node, by following a pre-defined change pattern.

Updating ontology while maintaining document annotations

Methods and systems of transforming ontologies of semantic web languages are disclosed. A user may adjust configuration settings for a transformation of primitives of a Semantic Web ontology language into primitives of a software modeling language. The adjusted configuration settings may be stored on a storage device. The transformation of primitives of the Semantic Web ontology language into primitives of the software modeling language may be performed using the adjusted configuration settings stored on the storage device. The adjusted configuration settings stored on the storage device may be selected for use in a subsequent transformation of primitives of the Semantic Web ontology language into primitives of the software modeling language. In some embodiments, the semantic web language is Resource Description Framework Schema (RDFS). In some embodiments, the software modeling language is Ecore.

Adjustable transformations from semantic web languages

Methods and systems to generate derivative information sources, from original information sources, use an ontology that provides a logic-based representation formalism of each of a number of original information sources, the original information sources having heterogeneous representation formalisms. The original information sources are transformed to the ontology. A number of derivative information sources, corresponding to the original information sources, may be automatically generated from the ontology.

Ontology-based generation and integration of information sources in development platforms

Methods and computer program products relating to properties of data elements. A computer-implemented method for identifying a property of a data element includes receiving a request for at least one sought property of a data element. The data element is configured for use in semantically identifying information portions in electronic communications. The sought property is to be valid in a context defined by at least one context value indicated by the request. The method includes identifying the at least one sought property by evaluating at least one logical rule that defines at least one property of the data element as being valid for a specified context. The method includes generating an output in response to the request that indicates the at least one sought property.

Growing community of inventors

Durmersheim, Germany

Daniel Oberle