公开(公告)号：US11797549B2

公开(公告)日：2023-10-24

申请号：US17987668

申请日：2022-11-15

Applicant: Oracle International Corporation

Inventor： Gopal Srinivasa Raghavan ,  Abhiram Madhukar Gujjewar ,  Ganesh Seetharaman ,  Jai Motwani ,  Sayon Dutta ,  Rajat Mahajan ,  Manasjyoti Sharma

IPC: G06F17/00 ,  G06F7/00 ,  G06F16/2457 ,  G06F16/22 ,  G06N20/00 ,  G06F16/248

CPC classification number: G06F16/24578 ,  G06F16/221 ,  G06F16/2246 ,  G06F16/2255 ,  G06F16/248 ,  G06N20/00

Abstract: A machine-learning model may be previously trained with a supervised learning algorithm to identify whether a pair of labels provided as input are similar. A locality sensitive hashing forest (LSH) may be generated for the set of candidate labels. When a user later identifies an input label (e.g., by search query, by interface selection, etc.) the input label may be used to query the LSH forest to identify a subset of the candidate labels. This subset may be used to generate respective pairs comprising the input label, one of the subset candidate labels, and a corresponding feature set generated for the pair. This data may be provided to the model to identify a degree to which the pair of labels are similar. The user may be provided one or more recommendations including similar terms identified from the model's output.

公开(公告)号：US20230076308A1

公开(公告)日：2023-03-09

申请号：US17987668

申请日：2022-11-15

Applicant: Oracle International Corporation

Inventor： Gopal Srinivasa Raghavan ,  Abhiram Madhukar Gujjewar ,  Ganesh Seetharaman ,  Jai Motwani ,  Sayon Dutta ,  Rajat Mahajan ,  Manasjyoti Sharma

IPC: G06F16/2457 ,  G06F16/248 ,  G06F16/22 ,  G06N20/00

Abstract: A machine-learning model may be previously trained with a supervised learning algorithm to identify whether a pair of labels provided as input are similar. A locality sensitive hashing forest (LSH) may be generated for the set of candidate labels. When a user later identifies an input label (e.g., by search query, by interface selection, etc.) the input label may be used to query the LSH forest to identify a subset of the candidate labels. This subset may be used to generate respective pairs comprising the input label, one of the subset candidate labels, and a corresponding feature set generated for the pair. This data may be provided to the model to identify a degree to which the pair of labels are similar. The user may be provided one or more recommendations including similar terms identified from the model's output.

公开(公告)号：US20230055129A1

公开(公告)日：2023-02-23

申请号：US17980412

申请日：2022-11-03

Applicant: Oracle International Corporation

Inventor： Ganesh Seetharaman ,  Robert Velisar ,  Geoffrey William Watters ,  Yuda Dai

IPC: G06F16/245 ,  G06F16/23 ,  G06F16/22 ,  G06F16/248

Abstract: Systems, devices, and methods discussed herein are directed to utilizing patterns and logical entities to identify and maintain relationships between data assets. In some embodiments, a query comprising a logical entity qualifier, one or more pattern identifiers that indicate a pattern, and a data set identifier may be received. The pattern is executed against a data set corresponding to the data set identifier and one or more logical entities are generated based on this execution. A logical entity may be a label that represents a set of one or more data assets in a data set. Assets that share a label can share attributes that are described by the label. The label corresponding to each logical entity may be presented, where each label represents a different set of data assets which share a common trait. In some embodiments, the user may define a pattern by which commonality may be assessed.

公开(公告)号：US11537369B2

公开(公告)日：2022-12-27

申请号：US16845734

申请日：2020-04-10

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： David Allan ,  Alexander Sasha Stojanovic ,  Hassan Heidari Namarvar ,  Ganesh Seetharaman

IPC: G06F8/41 ,  G06N20/00 ,  G06F16/14 ,  G06F16/21 ,  G06F16/25 ,  G06F16/435 ,  G06F16/23 ,  G06N5/04 ,  G06Q10/06 ,  G06F40/30 ,  G06F3/0482 ,  G06F17/18 ,  G06N5/02 ,  G06F8/10 ,  G06F8/34 ,  G06F3/042 ,  G06F3/06 ,  G06F9/50

Abstract: In accordance with various embodiments, described herein is a system (Data Artificial Intelligence system, Data AI system), for use with a data integration or other computing environment, that leverages machine learning (ML, DataFlow Machine Learning, DFML), for use in managing a flow of data (dataflow, DF), and building complex dataflow software applications (dataflow applications, pipelines). In accordance with an embodiment, the system can include a software development component and graphical user interface, referred to herein in some embodiments as a pipeline editor, or Lambda Studio IDE, that provides a visual environment for use with the system, including providing real-time recommendations for performing semantic actions on data accessed from an input HUB, based on an understanding of the meaning or semantics associated with the data.

公开(公告)号：US20220066753A1

公开(公告)日：2022-03-03

申请号：US17490186

申请日：2021-09-30

Applicant: Oracle International Corporation

Inventor： Hassan Heidari Namarvar ,  Alexander Sasha Stojanovic ,  David Allan ,  Ganesh Seetharaman

IPC: G06F8/41 ,  G06N20/00 ,  G06F16/14 ,  G06F16/21 ,  G06F16/25 ,  G06F16/435 ,  G06F16/23 ,  G06N5/04 ,  G06Q10/06 ,  G06F40/30 ,  G06F3/0482 ,  G06F17/18 ,  G06N5/02 ,  G06F8/10 ,  G06F8/34 ,  G06F3/042 ,  G06F3/06

Abstract: In accordance with various embodiments, described herein is a system (Data Artificial Intelligence system, Data AI system), for use with a data integration or other computing environment, that leverages machine learning (ML, DataFlow Machine Learning, DFML), for use in managing a flow of data (dataflow, DF), and building complex dataflow software applications (dataflow applications, pipelines). In accordance with an embodiment, the system can provide support for auto-mapping of complex data structures, datasets or entities, between one or more sources or targets of data, referred to herein in some embodiments as HUBs. The auto-mapping can be driven by a metadata, schema, and statistical profiling of a dataset; and used to map a source dataset or entity associated with an input HUB, to a target dataset or entity or vice versa, to produce an output data prepared in a format or organization (projection) for use with one or more output HUBs.

公开(公告)号：US11137987B2

公开(公告)日：2021-10-05

申请号：US15683551

申请日：2017-08-22

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Hassan Heidari Namarvar ,  Alexander Sasha Stojanovic ,  David Allan ,  Ganesh Seetharaman

IPC: G06F16/00 ,  G06F8/41 ,  G06N20/00 ,  G06F16/14 ,  G06F16/21 ,  G06F16/25 ,  G06F16/435 ,  G06F16/23 ,  G06N5/04 ,  G06Q10/06 ,  G06F40/30 ,  G06F3/0482 ,  G06F17/18 ,  G06N5/02 ,  G06F8/10 ,  G06F8/34 ,  G06F3/042 ,  G06F3/06 ,  G06F9/50

Abstract: In accordance with various embodiments, described herein is a system (Data Artificial Intelligence system, Data AI system), for use with a data integration or other computing environment, that leverages machine learning (ML, DataFlow Machine Learning, DFML), for use in managing a flow of data (dataflow, DF), and building complex dataflow software applications (dataflow applications, pipelines). In accordance with an embodiment, the system can provide support for auto-mapping of complex data structures, datasets or entities, between one or more sources or targets of data, referred to herein in some embodiments as HUBs. The auto-mapping can be driven by a metadata, schema, and statistical profiling of a dataset; and used to map a source dataset or entity associated with an input HUB, to a target dataset or entity or vice versa, to produce an output data prepared in a format or organization (projection) for use with one or more output HUBs.

公开(公告)号：US20200334020A1

公开(公告)日：2020-10-22

申请号：US16921533

申请日：2020-07-06

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Ganesh Seetharaman ,  Alexander Sasha Stojanovic ,  Hassan Heidari Namarvar ,  David Allan

IPC: G06F8/41 ,  G06N20/00 ,  G06F16/14 ,  G06F16/21 ,  G06F16/25 ,  G06F16/435 ,  G06F16/23 ,  G06N5/04 ,  G06Q10/06 ,  G06F40/30 ,  G06F3/0482 ,  G06F17/18 ,  G06N5/02 ,  G06F8/10 ,  G06F8/34 ,  G06F3/042 ,  G06F3/06

Abstract: In accordance with various embodiments, described herein is a system (Data Artificial Intelligence system, Data AI system), for use with a data integration or other computing environment, that leverages machine learning (ML, DataFlow Machine Learning, DFML), for use in managing a flow of data (dataflow, DF), and building complex dataflow software applications (dataflow applications, pipelines). In accordance with an embodiment, the system can provide a service to recommend actions and transformations, on an input data, based on patterns identified from the functional decomposition of a data flow for a software application, including determining possible transformations of the data flow in subsequent applications. Data flows can be decomposed into a model describing transformations of data, predicates, and business rules applied to the data, and attributes used in the data flows.

公开(公告)号：US20180067732A1

公开(公告)日：2018-03-08

申请号：US15683556

申请日：2017-08-22

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Ganesh Seetharaman ,  Alexander Sasha Stojanovic ,  Hassan Heidari Namarvar ,  David Allan

IPC: G06F9/45 ,  G06F9/44 ,  G06F3/0482 ,  G06F15/18

Abstract: In accordance with various embodiments, described herein is a system (Data Artificial Intelligence system, Data AI system), for use with a data integration or other computing environment, that leverages machine learning (ML, DataFlow Machine Learning, DFML), for use in managing a flow of data (dataflow, DF), and building complex dataflow software applications (dataflow applications, pipelines). In accordance with an embodiment, the system can provide a service to recommend actions and transformations, on an input data, based on patterns identified from the functional decomposition of a data flow for a software application, including determining possible transformations of the data flow in subsequent applications. Data flows can be decomposed into a model describing transformations of data, predicates, and business rules applied to the data, and attributes used in the data flows.

公开(公告)号：US09633052B2

公开(公告)日：2017-04-25

申请号：US14077140

申请日：2013-11-11

Applicant: Oracle International Corporation

Inventor： Ganesh Seetharaman ,  David Allan ,  John Leigh ,  Thomas Lau ,  Joseph F. Klein

IPC: G06F17/30 ,  G06F9/44

CPC classification number: G06F17/30292 ,  G06F8/20 ,  G06F8/34 ,  G06F8/35 ,  G06F17/30557 ,  G06F17/30563

Abstract: In various embodiments, a data integration system is disclosed which enables users to create a logical design which is platform and technology independent. The user can create a logical design that defines, at a high level, how a user wants data to flow between sources and targets. The tool can analyze the logical design, in view of the user's infrastructure, and create a physical design. The logical design can include a plurality of components corresponding to each source and target in the design, as well as operations such as joins or filters, and access points. Each component when transferred to the physical design generates code to perform operations on the data. Depending on the underlying technology (e.g., SQL Server, Oracle, Hadoop, etc.) and the language used (SQL, pig, etc.) the code generated by each component may be different.

公开(公告)号：US20250156160A1

公开(公告)日：2025-05-15

申请号：US19025695

申请日：2025-01-16

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Hassan Heidari Namarvar ,  Alexander Sasha Stojanovic ,  David Allan ,  Ganesh Seetharaman

IPC: G06F8/41 ,  G06F3/042 ,  G06F3/0482 ,  G06F3/06 ,  G06F8/10 ,  G06F8/34 ,  G06F9/50 ,  G06F16/14 ,  G06F16/21 ,  G06F16/23 ,  G06F16/25 ,  G06F16/435 ,  G06F17/18 ,  G06F40/30 ,  G06N5/022 ,  G06N5/04 ,  G06N5/046 ,  G06N20/00 ,  G06Q10/0637

Abstract: In accordance with various embodiments, described herein is a system (Data Artificial Intelligence system, Data AI system), for use with a data integration or other computing environment, that leverages machine learning (ML, DataFlow Machine Learning, DFML), for use in managing a flow of data (dataflow, DF), and building complex dataflow software applications (dataflow applications, pipelines). In accordance with an embodiment, the system can provide support for auto-mapping of complex data structures, datasets or entities, between one or more sources or targets of data, referred to herein in some embodiments as HUBs. The auto-mapping can be driven by a metadata, schema, and statistical profiling of a dataset; and used to map a source dataset or entity associated with an input HUB, to a target dataset or entity or vice versa, to produce an output data prepared in a format or organization (projection) for use with one or more output HUBs.