Oracle International Corporation

Amazon Technologies, Inc.

Hassan Heidari Namarvar

Alexander Sasha Stojanovic

Ganesh Seetharaman

David Allan

Ryan Bickhart

Richard Leider

Tarek Amara

Sunil Gopal Koteyar

John Bowers

Krishnakanth Rapaka

Techniques and systems for adaptive bitrate optimization for live content delivery are disclosed. In some embodiments, such techniques may include extracting, in real-time, features from content segments of a video stream; for each content segment of the video stream, identifying a rate-distortion (RD) cluster from a mapping of extracted features to RD clusters using one or more trained machine learning models; and transcoding the video stream by applying, to the content segments, a transcoding ladder having bitrate information corresponding to each identified RD cluster.

Adaptive bitrate optimization for content delivery

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.

System and method for automated mapping of data types for use with dataflow environments

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.

System and method for dynamic lineage tracking, reconstruction, and lifecycle management

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.

System and method for dynamic, incremental recommendations within real-time visual simulation

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.

System and method for metadata-driven external interface generation of application programming interfaces

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.

System and method for ontology induction through statistical profiling and reference schema matching

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.

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Mountain View, CA, United States of America

Hassan Heidari Namarvar