Sap Ag

Sap Se

Thomas Zurek

Klaus Nagel

Stefan Dipper

Stefan Unnebrink

Franz Faerber

Guenter Radestock

Erich Marschall

Peter John

Christian M Bartholomae

In one embodiment the present invention includes a computer-implemented method comprising receiving a first query in a local computer system to access data stored on a remote computer system the local computer system. Data elements of the remote data are associated with data elements of the local data. The query is transformed into multiple queries. A second query is sent to the remote computer system to retrieve remote data. A first data result is received and stored on the local computer system and incorporated into a third query. The third query is executed against the local data and the first data result to produce a result. Sensitive data may be stored locally in a dimension table of a star schema and non-sensitive data may be stored remotely in a fact table and another dimension table of the star schema, for example.

Systems and methods for secure access of data

This disclosure provides various software and systems for efficiently maintaining aggregates. In one aspect, a system performing a computer implemented method identifies a first change to transactional data in a business application and automatically adds an aggregate record to an aggregate data structure based on the first change. The system then identifies a second change to the transactional data in the business application, the second change comprising an update to key figures associated with the first change. The system can prohibit an update to the aggregate record in the aggregate data structure based on the second change and automatically adds a second aggregate record to the aggregate data structure based on the second change to the transactional data, where the second aggregate record comprises the update to the key figures.

Simple aggregate mode for transactional data

In one embodiment the present invention includes a method comprising receiving a data filter for filtering a collection of data, wherein the collection of data is configured as a star schema including a fact table and dimension tables. The data filter is applied against the dimension tables to generate a modified dimension table. The modified dimension tables are applied against the fact table to produce a modified fact table. The data filter is then applied against the modified fact table to generate a second modified fact table, which is the output of the process.

Method and system for data processing using multidimensional filtering

In one aspect, in a computer-implemented method may make data anonymous, so that the data may be used during testing. The method may include receiving, from a user interface, an indication of a type of shuffling to be performed on data. Moreover, the data may be shuffled based on the received indication of the shuffling type. The shuffling may rearrange the data to make the data anonymous. The shuffled data may be provided to an application. Related systems, apparatus, methods, and/or articles are also described.

Anonymizing infocube data

A method for estimating a runtime of a query of a data repository may abstract, extract, or otherwise generate one or more parameters of the query. Thereafter, the abstracted parameters may be associated with one or more statistical query records. From these statistical query records, a query runtime may be estimated. Related apparatuses, computer program products, and computer systems are also described.

Data query cost estimation

A first data storage schema in which a characteristic in a first dimension table is mapped by a first table and a second table can be received and a second data storage schema can be received. The first table maps the characteristic to a first object that include attributes to which time information is irrelevant to data processing activities and the second maps the characteristic to a second object that includes attributes to which time information is relevant to data processing activities. The second data storage schema includes a fact table including at least some facts drawn from the first data storage schema and a second dimension table that includes at least some characteristics drawn from at least one of the first object and the second object.

Mapping data structures

A method determines whether a structural indicator of a first table of results generated from a first data repository query matches a corresponding structural indicator of a second table of results generated from a substantially identical second data repository query. If the first and second table structural indicators match, then values from at least two corresponding cells from each of the first and second tables are aggregated. If the aggregated values from the first and second tables are identical, then an indication is provided indicating that the first data repository query results are accurate. Related apparatuses, computer program products, and computer systems are also described.

Data query verification

A system and method for improving a query on a relational database in a business intelligence system is provided. A multidimensional data table is provided in the database. A sampling dimension is appended to the data table. The sampling dimension includes a number of cells, and wherein each cell includes a sampling value corresponding to the data of at least one of the dimensions of the data table. The data table is then clustered in at least one of the dimensions based on the associated sampling value in the sampling dimension. A query for a subset of data can then be executed on the clustered data table based on the sampling values.

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Walldorf, Germany

Thomas Zurek