Sap Se

Sap Ag

Michael Wilking

Stefan Dipper

Sebastian Schroetel

Christof Mueller

Ingo Raasch

Stefan Unnebrink

Hans-Georg Beuter

Martin Friedt

Frank Klingl

Helidon Dollani

Gregor Dieckmann

Tim Kaiser

Jeffery Majestic

In one embodiment, a method receives a query for analyzing data in a database. The query includes a first level calculation that is performed before a second level calculation. When an operator associated with the first level calculation is present in the query, the method performs: calculating the first level calculation by removing group by information for a reference characteristic for the first level calculation to determine a first level calculation result and calculating the second level calculation using the first level calculation result using the group by information for the reference characteristic to determine a second level calculation result, wherein the group by in the second level calculation combines groups of rows in a database table of the database that have the same content in their specified columns.

Database query calculation using an operator that explicitly removes group-by characteristics

In one embodiment, a method receives a query for analyzing data in a database. The method then determines a constant in the query and determines an operator applied to the constant in the query. The operator explicitly controls a behavior of the constant. The constant is represented as a scalar representation in the query instead of as a vectorial representation where the constant would have been used as the vectorial representation without the use of the operator due to a rule governing use of constants. Then, the method performs a calculation for the query to determine a query result using the constant as the scalar representation.

Operators for constants in aggregated formulas

The disclosure generally describes computer-implemented methods, software, and systems for generating and executing queries. A query is received. A set of structure elements associated with the query is determined, wherein the structure elements are used for accessing, in a database, data tuples associated with the set of structure elements. Optionally, the set of structure elements is reduced for accessing data tuples to the structure elements selected. Contributing structure elements are added to the reduced set to not produce objects in the result set but for creating associated objects. Contributing structure elements are handled in an optimized way to keep the data set on which the query result set is calculated as small as possible. This is done by using the smallest set of characteristics in the group-by clause for retrieving the data set to calculate the requested set of structure elements. A result set of the query is provided.

Efficient query optimization for selective use of structure elements

A system includes reception of an instruction to evaluate homogeneity of a first set of values of a plurality of stored sets of values, determination of a first minimum of the first set of values, determination of a first maximum of the first set of values, determination of whether the first minimum and the first maximum are equal, and if it is determined that the first minimum and the first maximum are equal, return of a value equal to the first minimum and the first maximum in response to the instruction.

Homogeneity evaluation of datasets

Embodiments provide a multi-buffering system in an application layer to support unified read/write access to different data source types of an underlying database layer. In a particular embodiment, the database layer comprises both a multi-dimensional data source (e.g. a cube), and another data source type (e.g. a Data Store Object&#x2014;DSO). An abstract buffer component of the multi-buffering system is specialized into a delta buffer configured to read/write data of the cube, and into an after image buffer configured to read/write data of the DSO. A storage mechanism component of the multi-buffering system is specialized into a cache store configured to read/write data of the cube, and a calculation scenario store configured to read/write data of the DSO. The after image buffer may support certain aggregation rules during a DSO read/write operation. The calculation scenario store may leverage capabilities of an in memory database during a DSO read/write operation.

Multi-buffering system supporting read/write access to different data source type

A system and method of key figure data filters are presented. The key figure data filters are implemented in an analytical engine of a business warehouse system. The key figure data filters employ conditions, which can be expressed as a kind of selection that describe a set. A key figure data algorithm can be implemented by the analytical engine using the conditions, yet still respect hierarchies in the business warehouse database.

Key figure data filters in OLAP with hierarchies

In one embodiment, a method receives a query for analyzing data in a database. The query includes a first level calculation that is performed before a second level calculation. The method determines an operator in the query configured to explicitly remove group by information for a reference characteristic in the first level calculation where the group by information is specified for the reference characteristic in the second level calculation. The first level calculation is performed by removing the group by information for the reference characteristic for the first level calculation to determine a first level calculation result and the second level calculation is performed using the first level calculation result using the group by information for the reference characteristic to determine a second level calculation result.

Removing group-by characteristics in formula exception aggregation

A system includes determination of a dimension over which to iterate, determination of a first measure for which an aggregated value is determined per iteration, determination of a second measure for which a value is determined once per calculation of an iterative measure and against which to compare the aggregated value per iteration, and storage of the dimension, the first measure and the second measure as metadata of an iterative measure.

Iterative measures

Systems and methods for generating an optimized deployment scheme include generating a request identifying a type of resource requested by hierarchically arranged organizational entities including a first organizational entity located at a first level and a second organizational entity at a second level, executing a database query to retrieve business data corresponding to the first and second organizational entities, determining, based on the business data, a first quantity and a second quantity, the first quantity identifying a quantity of the resource available for deployment to the first organizational entity, and the second quantity identifying a quantity of the resource available for deployment to the second organizational entity, modifying the first quantity based on the second quantity, and generating, by using a processor of the computer system, an optimized deployment scheme based on the first quantity, the modified first quantity, and the second quantity.

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Zuzenhausen, Germany

Michael Wilking