Sap Se

Sap Ag

Robert Boschgmbh

Peter Sanders

Franz Faerber

Jonathan Dees

Jochen Seidel

Ingo Mueller

Cornelius Ratsch

Frederik Transier

Kai Stammerjohann

Nico Bohnsack

Christian Mathis

Martin Weidner

Ingo Tobias Müller

Valentin Buchhold

Markus Kromer

Hans-Christian Haag

Ingo Müller

An apparatus for and a method of operating a fleet of vehicles. The apparatus provides a set of routes for moving entities of the fleet of vehicles, provides a request for at least two stops of an entity of the fleet of vehicles including at least one pick up location and at at least one drop off location, selecting a route from the set of routes for that a measure for a distance of one or more stops included in the request to the route meets a condition, inserting the at least one pick up location and at at least one drop off location as stops into the route and instructing to operate an entity according to the route.

Apparatus for and method of operating a means of transport

Techniques are described for efficient execution of analytical queries on large amounts of data in a parallel database cluster while making maximal use of the available hardware.

Fast OLAP query execution in main memory on large data in a cluster

Innovations for adaptive compression and decompression for dictionaries of a column-store database can reduce the amount of memory used for columns of the database, allowing a system to keep column data in memory for more columns, while delays for access operations remain acceptable. For example, dictionary compression variants use different compression techniques and implementation options, Some dictionary compression variants provide more aggressive compression (reduced memory consumption) but result in slower run-time performance. Other dictionary compression variants provide less aggressive compression (higher memory consumption) but support faster run-time performance. As another example, a compression manager can automatically select a dictionary compression variant for a given column in a column-store database. For different dictionary compression variants, the compression manager predicts run-time performance and compressed dictionary size, given the values of the column, and selects one of the dictionary compression variants.

Adaptive dictionary compression/decompression for column-store databases

A pattern can be identified in at least part of a query whose definition is received in a query request. The identified pattern can be matched with a set of pre-defined patterns, each of which has associated therewith at least one pre-compiled query execution sub-component of a plurality of pre-compiled query execution sub-components retained in a library. A plan for executing the query can be generated, for example by incorporating the pre-compiled query execution sub-component associated with the matched pattern into the plan based on a pseudo code representation of the plan derived from the definition.

Many-core algorithms for in-memory column store databases

Innovations for adaptive compression and decompression for dictionaries of a column-store database can reduce the amount of memory used for columns of the database, allowing a system to keep column data in memory for more columns, while delays for access operations remain acceptable. For example, dictionary compression variants use different compression techniques and implementation options. Some dictionary compression variants provide more aggressive compression (reduced memory consumption) but result in slower run-time performance. Other dictionary compression variants provide less aggressive compression (higher memory consumption) but support faster run-time performance. As another example, a compression manager can automatically select a dictionary compression variant for a given column in a column-store database. For different dictionary compression variants, the compression manager predicts run-time performance and compressed dictionary size, given the values of the column, and selects one of the dictionary compression variants.

Aggregation of an in-memory database includes receiving, by at least one processor having a plurality of threads, input having records stored in random access memory, distributing, by the at least one processor, the input into portions, one of the plurality of threads having an assigned portion, aggregating, by the at least one processor, the records in the assigned portion based on locality of keys in the records and outputting, by the at least one processor, the aggregated records into a global hash table.

Hash table and radix sort based aggregation

A system may include a provider database, a reader database, and a database management system. The provider database may include a provider data area having a plurality of provider block addresses, and the reader database may include a reader data area having a plurality of reader block addresses, and a mapping of provider-specific identifiers to block addresses of the plurality of provider data pages and of reader-specific identifiers to block addresses of the plurality of reader data pages. The database management system may modify a database object of the reader database, the object being is associated with a provider-specific identifier; and modify the mapping to map the provider-specific identifier to a first block address of one of the plurality of reader data pages.

Growing community of inventors

Karlsruhe, Germany

Peter Sanders