AUTO DERIVATION OF SUMMARY DATA USING MACHINE LEARNING

    公开(公告)号:US20200380022A1

    公开(公告)日:2020-12-03

    申请号:US16425736

    申请日:2019-05-29

    Applicant: SAP SE

    Abstract: A method of processing raw data as it is received from a data provider via an input channel is disclosed. Values are derived from the raw data as it is received from the data provider via the input channel. The derived values correspond to custom fields of a summary table. The summary table is configured to store a summary of the raw data The custom fields correspond to data capable of improving an analysis of an entity by an analysis tool. The derived values are inserted into the custom fields of the summary table. Access to the summary table is provided as enriched data for use by the analysis tool to improve the analysis of the entity.

    Data curation with capacity scaling

    公开(公告)号:US12271762B2

    公开(公告)日:2025-04-08

    申请号:US17358976

    申请日:2021-06-25

    Applicant: SAP SE

    Abstract: A method may include allocating, based on a first load requirement of a first tenant, a first bin having a fixed capacity for handing the first load requirement of the first tenant. In response to the first load requirement of the first tenant exceeding a first threshold of the fixed capacity of the first bin, packing a second bin allocated to handle a second load requirement of a second tenant. The second bin may be packed by transferring, to the second bin, the first load requirement of the first tenant based on the transfer not exceeding the first threshold of the fixed capacity of the second bin. In response to the transfer exceeding the first threshold of the fixed capacity of the second bin, allocating a third bin to handle the first load requirement of the first tenant.

    Independently loading related data into data storage

    公开(公告)号:US12204511B2

    公开(公告)日:2025-01-21

    申请号:US17894856

    申请日:2022-08-24

    Applicant: SAP SE

    Abstract: Some embodiments provide a program that receives a set of data for a first record in a first table. The set of data includes a set of values for a set of attributes. In a data loading process configured to load a subset of the set of data into a subset of a first set of columns in the first table, the program determines that a first column in a first set of columns does not belong in the subset of the first set of columns. The program generates the first record in the first table. The program generates a value for the first column in the first set of columns that refers to a second record in the second table configured to represent a defined type of record. The program stores the value in the first column in the first set of columns of the first record.

    INDEPENDENTLY LOADING RELATED DATA INTO DATA STORAGE

    公开(公告)号:US20240070132A1

    公开(公告)日:2024-02-29

    申请号:US17894856

    申请日:2022-08-24

    Applicant: SAP SE

    CPC classification number: G06F3/0655 G06F3/0604 G06F3/0679

    Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a set of data for a record in a first table. The set of data comprises a set of values for a set of attributes. The first table comprises a first set of columns. A first column in the first set of columns in the first table is configured to refer to a second column in a second set of columns in a second table. The program further generates the record in the first table. The program also generates a value for the first column in the first set of columns in the first table based on a subset of the set of values for a subset of the set of attributes. The program further stores the value in the first column in the first set of columns of the record.

    DYNAMICALLY SCALABLE MACHINE LEARNING MODEL GENERATION AND DYNAMIC RETRAINING

    公开(公告)号:US20220318686A1

    公开(公告)日:2022-10-06

    申请号:US17223796

    申请日:2021-04-06

    Applicant: SAP SE

    Abstract: In an example embodiment an applications (apps) intelligence framework is utilized to quickly operationalize machine learned models (of different use cases, products, or applications) and take them to production through a set of predetermined pipelines. The app server may include a model configuration component to allow an entity to configure a model for an entity's specific use case. This configuration is then passed to a model generation component in the machine learning component, which acts to generate the specific model for the entity's use case using the configuration. An intelligent scheduling component may then be used to schedule retraining of the specific model at particular intervals. Notably, the intelligent scheduling component is itself a machine learned model (in one example embodiment a neural network) that is trained to dynamically output a training interval for a particular model based on various features.

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