Patents by Inventor Alexander Sasha Stojanovic

Alexander Sasha Stojanovic has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 10891272
    Abstract: The present disclosure relates generally to a data enrichment service that extracts, repairs, and enriches datasets, resulting in more precise entity resolution and correlation for purposes of subsequent indexing and clustering. As the data enrichment service can include a visual recommendation engine and language for performing large-scale data preparation, repair, and enrichment of heterogeneous datasets. This enables the user to select and see how the recommended enrichments (e.g., transformations and repairs) will affect the user's data and make adjustments as needed. The data enrichment service can receive feedback from users through a user interface and can filter recommendations based on the user feedback.
    Type: Grant
    Filed: September 24, 2015
    Date of Patent: January 12, 2021
    Assignee: ORACLE INTERNATIONAL CORPORATION
    Inventors: Alexander Sasha Stojanovic, Luis E. Rivas, Philip Ogren, Glenn Allen Murray
  • Publication number: 20200401385
    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 provides a programmatic interface, referred to herein in some embodiments as a foreign function interface, by which a user or third-party can define a service, functional and business types, semantic actions, and patterns or predefined complex data flows based on functional and business types, in a declarative manner, to extend the functionality of the system.
    Type: Application
    Filed: September 2, 2020
    Publication date: December 24, 2020
    Inventors: Ganesh Seetharaman, Alexander Sasha Stojanovic, Hassan Heidari Namarvar, David Allan
  • Publication number: 20200334020
    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.
    Type: Application
    Filed: July 6, 2020
    Publication date: October 22, 2020
    Inventors: Ganesh Seetharaman, Alexander Sasha Stojanovic, Hassan Heidari Namarvar, David Allan
  • Patent number: 10776086
    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 provides a programmatic interface, referred to herein in some embodiments as a foreign function interface, by which a user or third-party can define a service, functional and business types, semantic actions, and patterns or predefined complex data flows based on functional and business types, in a declarative manner, to extend the functionality of the system.
    Type: Grant
    Filed: August 22, 2017
    Date of Patent: September 15, 2020
    Assignee: ORACLE INTERNATIONAL CORPORATION
    Inventors: Ganesh Seetharaman, Alexander Sasha Stojanovic, Hassan Heidari Namarvar, David Allan
  • Publication number: 20200241853
    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.
    Type: Application
    Filed: April 10, 2020
    Publication date: July 30, 2020
    Inventors: David Allan, Alexander Sasha Stojanovic, Hassan Heidari Namarvar, Ganesh Seetharaman
  • Publication number: 20200241854
    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 perform an ontology analysis of a schema definition, to determine the types of data, and datasets or entities, associated with that schema; and generate, or update, a model from a reference schema that includes an ontology defined based on relationships between datasets or entities, and their attributes. A reference HUB including one or more schemas can be used to analyze data flows, and further classify or make recommendations such as, for example, transformations enrichments, filtering, or cross-entity data fusion of an input data.
    Type: Application
    Filed: April 10, 2020
    Publication date: July 30, 2020
    Inventors: Alexander Sasha Stojanovic, Hassan Heidari Namarvar, David Allan, Ganesh Seetharaman
  • Patent number: 10705812
    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.
    Type: Grant
    Filed: August 22, 2017
    Date of Patent: July 7, 2020
    Assignee: ORACLE INTERNATIONAL CORPORATION
    Inventors: Ganesh Seetharaman, Alexander Sasha Stojanovic, Hassan Heidari Namarvar, David Allan
  • Patent number: 10671445
    Abstract: Systems, methods, and computer-readable media for identifying an optimal cluster configuration for performing a job in a remote cluster computing system. In some examples, one or more applications and a sample of a production load as part of a job for a remote cluster computing system is received. Different clusters of nodes are instantiated in the remote cluster computing system to form different cluster configurations. Multi-Linear regression models segmented into different load regions are trained by running at least a portion of the sample on the instantiated different clusters of nodes. Expected completion times of the production load across varying cluster configurations are identified using the multi-linear regression models. An optimal cluster configuration of the varying cluster configurations is determined for the job based on the identified expected completion times.
    Type: Grant
    Filed: December 4, 2017
    Date of Patent: June 2, 2020
    Assignee: CISCO TECHNOLOGY, INC.
    Inventors: Antonio Nucci, Dragan Milosavljevic, Ping Pamela Tang, Athena Wong, Alex V. Truong, Alexander Sasha Stojanovic, John Oberon, Prasad Potipireddi, Ahmed Khattab, Samudra Harapan Bekti
  • Patent number: 10620924
    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 perform an ontology analysis of a schema definition, to determine the types of data, and datasets or entities, associated with that schema; and generate, or update, a model from a reference schema that includes an ontology defined based on relationships between datasets or entities, and their attributes. A reference HUB including one or more schemas can be used to analyze data flows, and further classify or make recommendations such as, for example, transformations enrichments, filtering, or cross-entity data fusion of an input data.
    Type: Grant
    Filed: August 22, 2017
    Date of Patent: April 14, 2020
    Assignee: ORACLE INTERNATIONAL CORPORATION
    Inventors: Alexander Sasha Stojanovic, Hassan Heidari Namarvar, David Allan, Ganesh Seetharaman
  • Patent number: 10620923
    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.
    Type: Grant
    Filed: August 22, 2017
    Date of Patent: April 14, 2020
    Assignee: ORACLE INTERNATIONAL CORPORATION
    Inventors: David Allan, Alexander Sasha Stojanovic, Hassan Heidari Namarvar, Ganesh Seetharaman
  • Publication number: 20190171494
    Abstract: Systems, methods, and computer-readable media for identifying an optimal cluster configuration for performing a job in a remote cluster computing system. In some examples, one or more applications and a sample of a production load as part of a job for a remote cluster computing system is received. Different clusters of nodes are instantiated in the remote cluster computing system to form different cluster configurations. Multi-Linear regression models segmented into different load regions are trained by running at least a portion of the sample on the instantiated different clusters of nodes. Expected completion times of the production load across varying cluster configurations are identified using the multi-linear regression models. An optimal cluster configuration of the varying cluster configurations is determined for the job based on the identified expected completion times.
    Type: Application
    Filed: December 4, 2017
    Publication date: June 6, 2019
    Inventors: Antonio Nucci, Dragan Milosavljevic, Ping Pamela Tang, Athena Wong, Alex V. Truong, Alexander Sasha Stojanovic, John Oberon, Prasad Potipireddi, Ahmed Khattab, Samudra Harapan Bekti
  • Patent number: 10296192
    Abstract: The present disclosure relates generally to a data enrichment service that automatically profiles data sets and provides visualizations of the profiles using a visual-interactive model within a client application (such as a web browser or mobile app). The visual profiling can be refined through end user interaction with the visualization objects and guide exploratory data visualization and discovery. Additionally, data sampling of heterogeneous data streams can be performed during ingestion to extract statistical attributes from multi-columnar data (e.g., standard deviation, median, mode, correlation coefficient, histogram, etc.). Data sampling can continue in real-time as data sources are updated.
    Type: Grant
    Filed: September 24, 2015
    Date of Patent: May 21, 2019
    Assignee: Oracle International Corporation
    Inventors: Alexander Sasha Stojanovic, Luis E. Rivas, Kevin L. Markey, Christopher F. Bidwell
  • Publication number: 20190138538
    Abstract: The present disclosure relates to performing similarity metric analysis and data enrichment using knowledge sources. A data enrichment service can compare an input data set to reference data sets stored in a knowledge source to identify similarly related data. A similarity metric can be calculated corresponding to the semantic similarity of two or more datasets. The similarity metric can be used to identify datasets based on their metadata attributes and data values enabling easier indexing and high performance retrieval of data values. A input data set can labeled with a category based on the data set having the best match with the input data set. The similarity of an input data set with a data set provided by a knowledge source can be used to query a knowledge source to obtain additional information about the data set. The additional information can be used to provide recommendations to the user.
    Type: Application
    Filed: December 31, 2018
    Publication date: May 9, 2019
    Applicant: Oracle International Corporation
    Inventors: Alexander Sasha Stojanovic, Mark Kreider, Michael Malak, Glenn Allen Murray
  • Patent number: 10210246
    Abstract: The present disclosure relates to performing similarity metric analysis and data enrichment using knowledge sources. A data enrichment service can compare an input data set to reference data sets stored in a knowledge source to identify similarly related data. A similarity metric can be calculated corresponding to the semantic similarity of two or more datasets. The similarity metric can be used to identify datasets based on their metadata attributes and data values enabling easier indexing and high performance retrieval of data values. A input data set can labeled with a category based on the data set having the best match with the input data set. The similarity of an input data set with a data set provided by a knowledge source can be used to query a knowledge source to obtain additional information about the data set. The additional information can be used to provide recommendations to the user.
    Type: Grant
    Filed: September 24, 2015
    Date of Patent: February 19, 2019
    Assignee: Oracle International Corporation
    Inventors: Alexander Sasha Stojanovic, Mark Kreider, Michael Malak, Glenn Allen Murray
  • Publication number: 20180067732
    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.
    Type: Application
    Filed: August 22, 2017
    Publication date: March 8, 2018
    Inventors: Ganesh Seetharaman, Alexander Sasha Stojanovic, Hassan Heidari Namarvar, David Allan
  • Publication number: 20180052861
    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 provides a programmatic interface, referred to herein in some embodiments as a foreign function interface, by which a user or third-party can define a service, functional and business types, semantic actions, and patterns or predefined complex data flows based on functional and business types, in a declarative manner, to extend the functionality of the system.
    Type: Application
    Filed: August 22, 2017
    Publication date: February 22, 2018
    Inventors: Ganesh Seetharaman, Alexander Sasha Stojanovic, Hassan Heidari Namarvar, David Allan
  • Publication number: 20180052870
    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 perform an ontology analysis of a schema definition, to determine the types of data, and datasets or entities, associated with that schema; and generate, or update, a model from a reference schema that includes an ontology defined based on relationships between datasets or entities, and their attributes. A reference HUB including one or more schemas can be used to analyze data flows, and further classify or make recommendations such as, for example, transformations enrichments, filtering, or cross-entity data fusion of an input data.
    Type: Application
    Filed: August 22, 2017
    Publication date: February 22, 2018
    Inventors: Alexander Sasha Stojanovic, Hassan Heidari Namarvar, David Allan, Ganesh Seetharaman
  • Publication number: 20180052878
    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 data governance functionality such as, for example, provenance (where a particular data came from), lineage (how the data was acquired/processed), security (who was responsible for the data), classification (what is the data about), impact (how impactful is the data to a business), retention (how long should the data live), and validity (whether the data should be excluded/included for analysis/processing), for each slice of data pertinent to a particular snapshot in time; which can then be used in making lifecycle decisions and dataflow recommendations.
    Type: Application
    Filed: August 22, 2017
    Publication date: February 22, 2018
    Inventors: Ganesh Seetharaman, Alexander Sasha Stojanovic, Hassan Heidari Namarvar, David Allan
  • Publication number: 20180052898
    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.
    Type: Application
    Filed: August 22, 2017
    Publication date: February 22, 2018
    Inventors: David Allan, Alexander Sasha Stojanovic, Hassan Heidari Namarvar, Ganesh Seetharaman
  • Publication number: 20180052897
    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.
    Type: Application
    Filed: August 22, 2017
    Publication date: February 22, 2018
    Inventors: Hassan Heidari Namarvar, Alexander Sasha Stojanovic, David Allan, Ganesh Seetharaman