Sap Se

Amazon Technologies, Inc.

Hinnerk Gildhoff

Tim Grouisborn

Gerrit Simon Kazmaier

Lars Volker

Edward-Robert Tyercha

Isil Pekel

Michael Jung

Kai-Niklas Bastian

Marcus Paradies

Cornelia Kinder

Romans Kasperovics

Thomas Fischer

Bjoern Deiseroth

Vadim Tschernezki

Oliver Blum

Wenzel Svojanovsky

Michèle Wyss

Menelaos Karavelas

Nemanja Boric

Ioanna Tsalouchidou

Eric Javier Biagioli

A system, technique, or computer program product generates a simplified version of a geometry, based on a target number of points to be included in the output. A first plurality of points, representative of a geometry, is received. The simplified version of the geometry is generated by, at least, expanding a segment of a simplified version of the geometry. The segment is identified for expanding by determining that a point associated with the segment is at a distance from the segment that exceeds a tolerance value, and is includable in the simplified version of the geometry without causing the simplified version of the geometry to exceed the target size.

Spatial data simplification and storage

Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives, from a client device, a percentage value for a set of points. The program further determines a triangulation based on the set of points. The program also determines an alpha value based on the triangulation and the percentage value. The program further determines an alpha shape based on the alpha value. The program also provides the client device the alpha shape.

System and method for determining alpha values for alpha shapes

The present disclosure involves systems, software, and computer implemented methods for predicting wildfires on the basis of biophysical indicators and spatiotemporal properties. A method includes receiving a request for a wildfire prediction for at least one geographical area. At least one biophysical indicator is identified. Each biophysical indicator provides biophysical data for the at least one geographical area. The at least one biophysical indicator is provided to a long short term memory (LSTM) network. The LSTM network includes a convolutional neural network (CNN) for each of multiple LSTM units. Each LSTM unit and each CNN are associated with a historical time period in a time series. The LSTM is used to generate at least one prediction for wildfire risk for the at least one geographical area for an upcoming time period. The at least one prediction is provided responsive to the request.

Predicting wildfires on the basis of biophysical indicators and spatiotemporal properties using a long short term memory network

Systems, software, and computer implemented methods can be used to predict wildfires based on biophysical and spatiotemporal data. A method includes receiving a request for a wildfire prediction for at least one geographical area. At least one biophysical indicator is identified. Each biophysical indicator provides biophysical data for the at least one geographical area. The at least one biophysical indicator is provided to a convolutional neural network (CNN). The CNN is trained using ground truth data that includes historical information about wildfires for at least one ground truth geographical area. The CNN is used to generate at least one prediction for wildfire risk for the at least one geographical area. The at least one prediction is provided responsive to the request.

Predicting wildfires on the basis of biophysical indicators and spatiotemporal properties using a convolutional neural network

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

A system for processing graph-modeled data in a relational database is provided. The system can include at least one data processor and at least one memory storing instructions that are executed by the at least one data processor. Executing the instructions can result in operations comprising: receiving a request to execute a graph algorithm operating on graph-modeled data stored at a relational database; and executing the graph algorithm within the relational database, the executing comprising use of an adjacency structure within the relational database. Related methods and articles of manufacture, including computer program products, are also provided.

Adjacency structures for executing graph algorithms in a relational database

A system for processing graph-modeled data in a relational database is provided. In some implementations, the system performs operations comprising: receiving, from a first user, a request to define a graph algorithm operating on a graph workspace, the graph workspace comprising at least a portion of graph-modeled data stored at a relational database; applying a first security rule associated with the relational database, the applying comprising determining whether the first user has a privilege to define the graph algorithm operating on the graph workspace; and storing the graph algorithm at the relational database, when the first user is determined to have the privilege to define the graph algorithm operating on the graph workspace. Related methods and articles of manufacture, including computer program products, are also provided.

Graph-modeled data processing in a relational database

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

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.

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Heidelberg, Germany

Hinnerk Gildhoff