Sap Ag

Erich Marschall

Guenter Radestock

Stefan Dipper

Franz Faerber

Klaus Nagel

Stefan Unnebrink

Stefan Biedenstein

Christian M Bartholomae

Andrew Ross

Olaf Meincke

Jens-Peter Dittrich

Hartmut Koerner

Christoph Weyerhaeuser

Tobias Mindnich

Daniel Baeumges

Thomas Zurek

Peter Karlheinz Zimmerer

Peter John

In one embodiment the present invention includes an OLAP execution model using relational operations. In one embodiment, the present invention includes, a method comprising receiving a first query in an online analytic processor (OLAP) executing on one or more computers, the OLAP generating and comprising a model specifying a graph defining a plurality of nodes and a plurality of tiers, each node corresponding to a different operation on data. A second query is generated by the OLAP. The second query includes a plurality of layered subqueries each corresponding to one of the nodes in the graph for specifying the different operations on data. The second query is received in a relational engine coupled to the datastore. The relational engine executes the second query, and in accordance therewith, retrieves data.

OLAP execution model using relational operations

In one embodiment the present invention includes a system for securing and authorizing access to multidimensional transactional data. The system includes online analytical processing (OLAP) system and a database system. The OLAP system accesses a selection of multidimensional transactional data from the database system according to a multidimensional request. The OLAP system aggregates the selection to form aggregated information. The OLAP system filters the selection according to the authorization for the user, to form a filtered selection. In this manner the user can see accurate aggregations yet not see unauthorized data.

System and method of securing and authorizing multidimensional transactional data

A query having multiple parts may be processed to form an intermediate results set. This intermediate results set may be partitioned into a plurality of groups. Thereafter, the groups may be sorted into a plurality of containers so that each container contains data sufficient to calculate one requested result in the multipart query. Related techniques, apparatuses, systems, and computer program products are also described.

Efficient handling of multipart queries against relational data

Methods and apparatus, including computer systems and program products, relating to an information management system and aggregating data by performing table scans. In general, in one aspect, the technique includes receiving a query for a response to a search on a database, loading data from the database into memory, filtering the data based on the query to generate a list of results, buffering at least one key figure corresponding to a result, buffering at least one dimension value corresponding to each key figure, aggregating the dimension values to generate an aggregate key, aggregating key figures corresponding to the sane aggregate key to generate one or more aggregate key figures, and displaying the response to the search on a display device. Loading the data may include compressing the data. Filtering the data may be performed blockwise.

Fast aggregation of compressed data using full table scans

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 database system may include a database, a set of tools for accessing the database, a data buffer for storing data objects from the database, and a 'delta buffer' for storing delta records. Simulations may be performed in the database system by the set of tools that access the database. Simulative changes of the data may be handled by the delta buffer. The delta buffer need not contain all the data used for simulations and may include delta records. Each delta record may include simulative changes of the data and a request identifier. The content of the delta buffer may be integrated with the unchanged, original data from the database. The delta buffer in combination with the data buffer may provide an integrated view for tools that access the database, such as planning and reporting tools.

Delta-mechanism for integration of OLAP-based planning and reporting

Systems and techniques for the hybrid provision of data are described. In one aspect, a system includes a first collection of information stored in accordance with a reporting data model, a second collection of information setting forth a collection of one or more additive delta records, and an analytic engine to perform queries on the first collection of information and the second collection of information to yield an aggregated result set that is based on the contents of both the first collection of information and the second collection of information. The additive delta records each include a numeric value that characterizes the magnitude of a change to a numeric measure in the first collection and identification information that identifies the numeric measure in the first collection. The identified numeric measure is no longer current.

Hybrid data provider

Methods and apparatus, including computer systems and program products, relating to an information management system and aggregating data by performing table scans. In general, in one aspect, the technique includes receiving a query for a response to a search on a database, loading data from the database into memory, filtering the data based on the query to generate a list of results, buffering at least one key figure corresponding to a result, buffering at least one dimension value corresponding to each key figure, aggregating the dimension values to generate an aggregate key, aggregating key figures corresponding to the same aggregate key to generate one or more aggregate key figures, and displaying the response to the search on a display device. Loading the data may include compressing the data. Filtering the data may be performed blockwise.

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Nussloch, Germany

Erich Marschall