公开(公告)号：US10318557B2

公开(公告)日：2019-06-11

申请号：US15618391

申请日：2017-06-09

Applicant: SAP SE

Inventor： Edward-Robert Tyercha ,  Gerrit Simon Kazmaier ,  Hinnerk Gildhoff ,  Isil Pekel ,  Lars Volker ,  Tim Grouisborn

IPC: G06F16/22 ,  G06F16/28

Abstract: 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.

公开(公告)号：US11797885B2

公开(公告)日：2023-10-24

申请号：US17031665

申请日：2020-09-24

Applicant: SAP SE

Inventor： Steven Jaeger ,  Isil Pekel ,  Manuel Zeise

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A data processing pipeline may be generated to include an orchestrator node, a preparator node, and an executor node. The preparator node may generate a training dataset. The executor node may execute machine learning trials by applying, to the training dataset, a machine learning model and/or a different set of trial parameters. The orchestrator node may identify, based on a result of the machine learning trials, a machine learning model for performing a task. The execution of the data processing pipeline may be optimized. Examples of optimizations include pooling multiple machine learning trials for execution at a single executor node, executing at least some machine learning trials using a sub-sample of the training dataset, and adjusting a proportion of trial parameters sampled from a uniform distribution to avoid a premature convergence to a local minima within the hyper-parameter space for generating the machine learning model.

公开(公告)号：US20220092471A1

公开(公告)日：2022-03-24

申请号：US17031665

申请日：2020-09-24

Applicant: SAP SE

Inventor： Steven Jaeger ,  Isil Pekel ,  Manuel Zeise

IPC: G06N20/00

Abstract: A data processing pipeline may be generated to include an orchestrator node, a preparator node, and an executor node. The preparator node may generate a training dataset. The executor node may execute machine learning trials by applying, to the training dataset, a machine learning model and/or a different set of trial parameters. The orchestrator node may identify, based on a result of the machine learning trials, a machine learning model for performing a task. The execution of the data processing pipeline may be optimized. Examples of optimizations include pooling multiple machine learning trials for execution at a single executor node, executing at least some machine learning trials using a sub-sample of the training dataset, and adjusting a proportion of trial parameters sampled from a uniform distribution to avoid a premature convergence to a local minima within the hyper-parameter space for generating the machine learning model.

公开(公告)号：US20210089970A1

公开(公告)日：2021-03-25

申请号：US16582950

申请日：2019-09-25

Applicant: SAP SE

Inventor： Manuel Zeise ,  Isil Pekel ,  Steven Jaeger

IPC: G06N20/10 ,  G06N20/20 ,  G06K9/62 ,  G06N3/04

Abstract: Data for processing by a machine learning model may be prepared by encoding a first portion of the data including a spatial data. The spatial data may include a spatial coordinate including one or more values identifying a geographical location. The encoding of the first portion of the data may include mapping, to a cell in a grid system, the spatial coordinate such that the spatial coordinate is represented by an identifier of the cell instead of the one or more values. The data may be further prepared by embedding a second portion of the data including textual data, preparing a third portion of the data including hierarchical data, and/or preparing a fourth portion of the data including numerical data. The machine learning model may be applied to the prepared data in order to train, validate, test, and/or deploy the machine learning model to perform a cognitive task.

公开(公告)号：US20170308605A1

公开(公告)日：2017-10-26

申请号：US15618391

申请日：2017-06-09

Applicant: SAP SE

Inventor： Edward-Robert Tyercha ,  Gerrit Simon Kazmaier ,  Hinnerk Gildhoff ,  Isil Pekel ,  Lars Volker ,  Tim Grouisborn

IPC: G06F17/30

CPC classification number: G06F17/30598 ,  G06F17/30315

Abstract: 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.

公开(公告)号：US20170147637A1

公开(公告)日：2017-05-25

申请号：US14946658

申请日：2015-11-19

Applicant: SAP SE

Inventor： Tobias Mindnich ,  Julian Schwing ,  Christoph Weyerhaeuser ,  Isil Pekel ,  Johannes Merx ,  Alena Bakulina

IPC: G06F17/30

CPC classification number: G06F17/30448

Abstract: Methods and apparatus, including computer program products, are provided for union node pruning. In one aspect, there is provided a method, which may include receiving, by a calculation engine, a query; processing a calculation scenario including a union node; accessing a pruning table associated with the union node, wherein the pruning table includes semantic information describing the first input from the first data source node and the second input from the second data source node; determining whether the first data source node and the second data source node can be pruned by at least comparing the semantic information to at least one filter of the query; and pruning, based on a result of the determining, at least one the first data source node or the second data source node. Related apparatus, systems, methods, and articles are also described.