Sap Se

Lars Volker

Gerrit Simon Kazmaier

Hinnerk Gildhoff

Tim Grouisborn

Edward-Robert Tyercha

Isil Pekel

Boris Gruschko

Florian Foebel

Nico Licht

Christian Lahmer

Torsten Abraham

Marcus Lieberenz

Jan Rittinger

Bernhard Jaecksch

Yeonghee Choi

Daniel Merkel

Stefan Uhrig

A data set of spatial data having a plurality of dimensions and including linestrings can be processing by decomposing each linestring of the plurality of linestrings into a plurality of line segments. Each coordinate dimension appears in at least one line segment of the plurality of line segments can be listed in one of a plurality of dimensional dictionaries that each correspond to a dimension of the plurality of dimensions. A linestring of the plurality of linestrings can be represented as a set of the line segments using the plurality of dimensional dictionaries.

Column store optimization using telescope columns

Data is received that is derived from a plurality of geo-spatial sensors that respectively generate data characterizing a plurality of sources within a zone of interest. The data includes series time-stamped frames for each of the sensors and at least one of the sources has two or more associated sensors. The received data can be sorted and processed, for each sensor on a sensor-by-sensor basis, using a sliding window. The sorted and processed data can then be correlated and written into a data storage application. Related apparatus, systems, techniques and articles are also described.

Pre-processing of geo-spatial sensor data

A query of spatial data is received by a database comprising a columnar data store storing data in a column-oriented structure. Thereafter, a minimal bounding rectangle associated with the query is identified using a grid order scanning technique. The spatial data set corresponding to the received query is then mapped to physical storage in the database using the identified minimal bounding rectangle so that the spatial data set can be retrieved. Related apparatus, systems, techniques and articles are also described.

Querying spatial data in column stores using grid-order scans

DBSCAN clustering analyses can be improved by pre-processing of a data set using a Hilbert curve to intelligently identify the centers for initial partitional analysis by a partitional clustering algorithm such as CLARANS. Partitions output by the partitional clustering algorithm can be process by DBSCAN running in parallel before intermediate cluster results are merged.

Hilbert curve partitioning for parallelization of DBSCAN

According to some embodiments, input data may be received from a data source in an enterprise database in accordance with an enterprise performance management planning model, stored by a processor at the enterprise database. An operation may then be performed on the input data to produce a result. The result may then be stored in a data target, wherein the data target points to a data holding entity in an instantiation of a plan data container at the enterprise database.

Enterprise performance management planning operations at an enterprise database

Polygons can be simplified from an original, higher resolution to a simplified, lower resolution such that the simplified versions of the polygons do not introduce errors and also do not render boundaries shared with other polygons invalid.

Polygon simplification

Correlated and processed data is received that is derived from a plurality of geo-spatial sensors that respectively generate data characterizing a plurality of sources within a zone of interest. The data includes a series of time-stamped frames for each of the sensors. Subsequently, events of interest are identified, in real-time, based on relative positions of the sources within the zone of interest prior to the data being written to a data storage application. Data can then be provided (e.g., loaded, stored, displayed, transmitted, etc.), in real-time, that characterize the events of interest. Related apparatus, systems, techniques and articles are also described.

Processing of geo-spatial athletics sensor data

A query of spatial data is received by a database comprising a columnar data store storing data in a column-oriented structure. Thereafter, a minimal bounding rectangle associated with the query is identified using a tree-order scanning technique. A spatial data set that corresponds to the received query is then mapped to the physical storage in the database using the identified minimal bounding rectangle. Next, the spatial data set is then retrieved. Related apparatus, systems, techniques and articles are also described.

Querying spatial data in column stores using tree-order scans

Using index clusters to approximate coordinate values for vertices of compressed simplexes of a spatial data set, valid subspaces can be identified and used to identify other simplexes that may intersect a first simplex. Such approaches can be used for filtering, refining, etc. analysis of intersections between areas, lines, volumes, etc. within spatial data sets.

Column store optimization using simplex store

Cluster analysis of data points in a data set can be optimized by identification of a preferred cluster analysis method. This identification can be based on indexing the data using a Hilbert curve and determining whether the data points are predominantly in spherical or non-spherical clusters. Methods, systems, and articles of manufacture are described.

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Karlsruhe, Germany

Lars Volker