Sap Ag

Ekkehard Lange

Ralf Handl

Michael Hladik

Christoph Meinel

Dirk Endesfelder

Frank Andreas Raebiger

Roland Kolata

Bernd Hartmann

Jens Becher

Michael Hild

Andreas S Borchardt

Calculations to be performed on a massive data set may be delegated to a plurality of local servers for parallel processing. Each local server advances through a subset of database objects to determine if the objects are relevant to the calculation. If so, the objects' data is used as an operand to the calculation to generate intermediate results at the local server. The intermediate results of the various local servers are transmitted to a central server, which generates final results and transmits them back to the local servers. The local servers write the final results into each object in its respective subset, where relevant. The method is efficient because each object in the database is opened at most twice. Opening and closing database objects often involve transfers from bulk storage and, therefore, are some of the most time-consuming operations that computer systems perform.

Distributed processing system for calculations based on objects from massive databases

In a banking application system including numerous banking applications, a data management framework allows for the generalized storage and subsequent access of result data. Through the application of the framework to the raw processing data generated by one of the banking applications, the result data is stored in a general storage location. Through this framework, the data is then readily accessible by all other banking applications. This framework includes a header field, characteristics data and key figure data. This removes proprietary restrictions on the data itself and thus makes it available to the other applications, increasing cross-system efficiency by allowing other applications to access and subsequently use the result data.

Framework for integrated storage of banking application data

Growing community of inventors

Karlsruhe, Germany

Ekkehard Lange