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.

    Auto derivation of summary data using machine learning

    公开(公告)号:US11238077B2

    公开(公告)日:2022-02-01

    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.

    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.

    CLOUD-BASED PLUGGABLE CLASSIFICATION SYSTEM
    6.
    发明申请

    公开(公告)号:US20180322185A1

    公开(公告)日:2018-11-08

    申请号:US15584195

    申请日:2017-05-02

    Applicant: SAP SE

    Abstract: Example embodiments for classification are described. In an example embodiment, a request including a text term to be classified is received from a source system via a communication network at a computer system. A rule associated with the text term is accessed, in which the rule indicates at least one classifier of a plurality of classifiers installed at the computer system. Classification information for the text term is generated at the computer system using the at least one classifier indicated by the rule. The generated classification information includes a classification selected from a taxonomy by the at least one classifier. The generated classification information is transmitted via the communication network to the source system.

    AUGMENTING DATABASE SCHEMA USING INFORMATION FROM MULTIPLE SOURCES

    公开(公告)号:US20180032553A1

    公开(公告)日:2018-02-01

    申请号:US15221085

    申请日:2016-07-27

    Applicant: SAP SE

    CPC classification number: G06F16/213

    Abstract: Example embodiments for augmenting master data schema are described. In an example embodiment, schema of master data to be employed by a plurality of applications are accessed. First additional information is added to the schema, wherein the first additional information is to be employed by each of the plurality of applications in accessing the master data. After the adding of the first additional information, second additional information is added to the schema, wherein the second additional information is to be employed by a first corresponding one of the plurality of applications in accessing the master data. In some example embodiments, third additional information is added to the schema, wherein the third additional information is to be employed by a second corresponding one of the plurality of applications in accessing the master data.

    Database record searching with multi-tier queries

    公开(公告)号:US11294906B2

    公开(公告)日:2022-04-05

    申请号:US16432196

    申请日:2019-06-05

    Applicant: SAP SE

    Abstract: Various examples are directed to systems and methods for identifying database records in a database table. A database management system receives a search request comprising a first set of strings associated with a first column of the database table and a second set of strings associated with a second column of the database table. The database management system selects a set of first column keywords using the first set of strings and executes a first tier query at the database table. Responsive to determining that no database record returned by the first tier query has a relevance score greater than a threshold value, the database management system executes a second tier query at the database table.

    Cloud-based pluggable classification system

    公开(公告)号:US10942948B2

    公开(公告)日:2021-03-09

    申请号:US15584195

    申请日:2017-05-02

    Applicant: SAP SE

    Abstract: Example embodiments for classification are described. In an example embodiment, a request including a text term to be classified is received from a source system via a communication network at a computer system. A rule associated with the text term is accessed, in which the rule indicates at least one classifier of a plurality of classifiers installed at the computer system. Classification information for the text term is generated at the computer system using the at least one classifier indicated by the rule. The generated classification information includes a classification selected from a taxonomy by the at least one classifier. The generated classification information is transmitted via the communication network to the source system.

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