Sap Se

Sap Ag

Joachim Gross

Jan Portisch

Sandra Bracholdt

Volker Saggau

Roman A Rapp

Frederik Thormaehlen

Martin Kaisermayr

Manfred Wanninger

Mahesh Sastry

Prasheel Kayal

Reshmi Sreekumar

Mohammed Reza

Srirama Suraparaju

Manimaran Mani

Anil Kumar K Naidu

A metastructure for representing and manipulating or analyzing a data model is provided herein. A source representation of the first data model may be received. The source representation may include a representation of one or more structural components of the first data model, respectively having identifiers and types. One or more instances of a metastructure schema object datatype may be generated based on the one or more structural components of the source representation. Generating a given instance of the metastructure schema object datatype for a given structural component may include assigning a first identifier of the given structural component to the first data member of the given instance, determining a first type associated with the given structural component, and assigning the first type to the second data member of the given instance. The one or more instances of the metastructure schema object datatype may be stored in association.

Matching metastructure for data modeling

Techniques and solutions are described for defining transformation specifications in a programming-language independent language and converting such specifications to one or more executable formats. The language can provide for defining rules and actions. Rules can refer to (e.g., be based at least in part on) data targets, such as attributes of a schema, whose identifiers are to be read or updated, or to other rules. Rules can be reused, and can recursively refer to one another, such that a large number of complex schema transformations can be accomplished using a series of first order logic statements. Actions can define what, and how, values will be changed when a predicate rule is satisfied. A transformation specification in the language can be parsed and selectively complied to one or more executable formats, including in programming languages such as the structured query language. Disclosed technologies can facilitate data transformations by non-technical users.

Logical, recursive definition of data transformations

Techniques and solutions are described for automatically refactoring code. In particular, disclosed technologies provide for automatically refactoring code that includes function calls, which can be to particular types of functions, such as external functions or functions that are not member functions of a class. For code to be refactored, an interface and a class are created. The class implements the interface. Method declarations corresponding to the function calls being refactored are added to the interface and the class. Code is added instantiating an object of the class. Implementations for the methods in the class can call the functions called by the source code being refactored, or can include code implementing the functionality of the called functions. Refactored code has function calls replaced with calls to corresponding methods of the interface.

Automatic source code refactoring

A business object model, which reflects data that is used during a given business transaction, is utilized to generate interfaces. This business object model facilitates commercial transactions by providing consistent interfaces that are suitable for use across industries, across businesses, and across different departments within a business during a business transaction. In some operations, software creates, updates, or otherwise processes information related to a cost model, a current account contract, and/or a collateral constellation business object.

Managing consistent interfaces for business objects across heterogeneous systems

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Altrip, Germany

Joachim Gross