Emc Corporation

Emc IP Holding Company LLC

Dell Products Inc.

Other

Emc Cororation

Stephen J Todd

Michael Kilian

Tom Teugels

Mark A Parenti

Andreas L Bauer

Douglas A Wood

Jan Van Riel

Said Tabet

Michel F Fisher

Assaf Natanzon

Philippe Armangau

Jason Alan Shepherd

Wayne A Pauley, Jr

Steven M Blumenau

Kenneth Durazzo

David I Dietrich

Uday K Gupta

Mark A O'Connell

Pengfei Wu

Scott R Ostapovicz

Rhonda Baldwin

Yu Cao

Nicole Reineke

Sudhir Vijendra

Qiyan Chen

Dragan Savic

Brian A Castelli

Anand Singh

Kun Wang

Robert Anthony Lincourt, Jr

Nikhil Sharma

John Roese

Jeffrey Norton

Zoran Cakeljic

Aurelian Dumitru

Donagh A Buckley

Chenhui Fan

Eloy Francisco Macha

Kim Marivoet

Gaurav Chawla

Jeroen Mattijs Van Rotterdam

Amihai Savir

Walter A O'Brien, Iii

Jeffrey Alan Brown

David Stephen Reiner

Ziye Yang

Xiaoyan Guo

Tianqing Wang

Dong Xiang

John Daniel Hushon

Mohamed Sohail

Lintao Wan

John Philip Bell, Ii

Walter T Dobberpuhl

Paul M Bober

Mikhail Danilov

Jimmy Doyle Pike

Yael Villa

Timothy J Cox

Michael Robillard

Nihar Nanda

Qing Ye

Riaz Zolfonoon

James W Espy

Joseph T Frank

Matthew J D'Errico

Brian R Tetreault

George M Ericson

John D Hushon, Jr

Charles H Hopkins

Brian James Martin

Sheppard David Narkier

Gerald Cotter

Ari Juels

Kai Chen

Hanna Yehuda

Gregory William Lazar

Rui Chen

Stephen Glen Graham

Si Ruo Chen

Peter John McCann

Scott Auchmoody

David W DesRoches

Feng Guo

Zhenzhen Lin

Layne Lin Peng

John Michael Hayden

Zhe Dong

Xuebin He

Jun Tao

Oshry Ben-Harush

David E Frattura

Jidong Chen

Lejin Du

Simon Tao

Karthikeyan Srinivasan

Nalinkumar Mistry

Misha Nossik

Humberto Rodriguez

Millard C Taylor, Iii

One example method includes determining that an application is running on a node of a data confidence fabric; performing an investigation to determine if the application is new to the data confidence fabric; when it is determined that the application is new to the data confidence fabric, generating an update comprising metadata that identifies the application and the node on which the application is running; and automatically deploying, to the node of the data confidence fabric, an application-specific plug-in configured to annotate data received from the application that was determined to be new to the data confidence fabric.

Application discovery and data confidence fabric (DCF) overlay

One example method includes receiving, by a hybrid classical-quantum computing system, data from a node of a data confidence fabric, processing the data to create processed data, generating one or more confidence scores relating to the processed data, and making the one or more confidence scores and the processed data available to an end user. The hybrid classical-quantum computing system may also be a node of the data confidence fabric and may perform classical and/or quantum computing operations on the data.

Classical-quantum data confidence fabric

One example method includes receiving a hybrid/classical algorithm, determining a runtime characteristic of the hybrid/classical algorithm, based on the runtime characteristic, checking a memory availability for execution of the hybrid/classical algorithm, when adequate memory is not available to support execution of the hybrid/classical algorithm, modifying a classical/quantum memory fabric to provide enough memory to support execution of the hybrid/classical algorithm, and orchestrating the hybrid classical/quantum algorithm to an execution environment that includes the classical/quantum memory fabric.

Intelligent classical/quantum extended memory fabric

One example method includes receiving an orchestration automation request for an asset, identifying an ethics rule that applies to the asset, comparing the ethics rule to asset values contained in an AI ethics datastore, based on the comparing, identifying a list of assets that conform to the ethics rule, and when the asset appears in the list of assets that conform to the ethics rule, automatically placing the asset at an entity of a computing infrastructure.

AI ethics scores in automated orchestration decision-making

Generating policy-based confidence scores for data is disclosed. Data captured by a data confidence fabric is annotated when the data is created, mutated, transited or otherwise handled in the data confidence fabric. The annotations are weighted by a policy to generate policy-based confidence scores. The policy-based confidence scores are used in determining whether the data is sufficiently trusted for use by an application.

Data confidence fabric policy-based scoring

One example method includes performing a secure boot of hardware at a node of a data confidence fabric, creating an artifact that includes information concerning the secure boot, storing the artifact, receiving a data stream at the node, annotating data of the data stream with trust metadata, and associating the artifact with the data. An immutable ledger entry may be created that includes a pointer to the data, and a pointer to the artifact, and the immutable ledger entry may be accessible by an application.

Secure enclave pathing configuration for data confidence fabrics

One example method includes determining a spatial distribution of devices in a three dimensional physical environment, determining a polling frequency for each of the devices, receiving data generated by the devices, evaluating the data, and assigning a trust score to the data. The devices may take the form of respective sensors operable to sense a physical attribute of the three dimensional physical environment. A Fourier transform may be employed to determine the polling frequency.

Increasing device data confidence and operation via temporal and spatial analytics

A method and system for insight creation transparency. Explainable artificial intelligence, in recent years, has become synonymous with a framework through which users, relying on machine learning models for various applications, may come to trust the result(s) outputted by said models through better comprehension of the mechanisms leading to said result(s). Leveraging a vast database of metadata for a plethora of unstructured and structured data/information, embodiments disclosed herein derive, or infer, insights therefrom that best address any user-submitted queries. Embodiments disclosed herein, further, provide transparency information detailing, for example, which input(s) and which technique(s) and/or algorithm(s) were employed to arrive at the derived/inferred insights.

Insight creation transparency

A method and system metadata-based query rewriting. Query rewriting may generally refer to the automatic transformation of search queries in order to better represent the intent of the searcher (i.e., reason(s) motivating the searcher to conduct any search query). Embodiments disclosed herein, more specifically, leverage captured asset metadata, for assets hosted across various internal and external data sources, as well as graph techniques, to provide search query results that more closely match the said intent of the searcher. The graph techniques lead to the identification of adjacent areas and topics to the original search query, thereby enhancing the recall and/or precision of any returned search query results.

Metadata-based query rewriting

A method and system for business intent-assisted search. A business intent may generally refer to information, respective to an organization user, which may pertain to or describe the engagement of the organization user within and/or outside their organization (e.g., a commercial business, an education institution, etc.). Embodiments disclosed herein, accordingly, implement search query processing based on the business intent modeled for the search query submitter (e.g., an organization user). Further, a recall of any returned information, relevant to the search query, may be contingent on and/or directly correlated with a completeness of the modeled business intent for any given organization user.

Business intent-assisted search

One example method includes receiving a data stream at a node of a data confidence fabric that comprises a group of nodes that are each operable to assign trust metadata to data of the data stream, inspecting the data stream to determine a data type of data in the data stream, accessing a configuration file that applies to all the nodes of the data confidence fabric, and obtaining an equation from the configuration file, mapping the equation to the data, performing a trust insertion process on the data, as specified in the equation, and generating trust metadata that is associated with the data and based on the trust insertion process.

Increasing confidence scores via DCF equation mapping and automated stream inspection

One example method is performed in connection with a data confidence fabric. This method includes generating information about data as the data transits a data confidence fabric, ingesting the information, mapping the ingested information to a timeline, evaluating the timeline, based on the evaluating, generating a recommendation for an action concerning the data, and implementing the action in the data confidence fabric. In this method, the evaluating includes determining if any unexpected time gaps occurred as the data transited the data confidence fabric.

Timeseries DCF capabilities to predict data interactions based on past trends

Aspects of annotating data are disclosed. As data is ingested to a data confidence fabric, analytic values, which includes tags or annotations, are attached to or associated with the data by an analytic insertion mechanism. The analytic values allow the data to be used by applications including machine learning algorithms immediately. Feedback from the applications allow the analytic insertion mechanism to improve and generate more valuable analytic values and to generate higher confidence scores for the analytic values.

Increasing DCF confidence for analytic value

A method and system for metadata-based feature expansion for automated machine learning processes. In machine learning, feature selection—or a method through model inputs are reduced in dimensionality by retaining the relevant features, while also discarding the noise, thereof—often plays a pivotal role in the availability of algorithm(s) to derive model(s) from, the optimizing of said model(s) through training and/or testing, and, ultimately, the reaching of acceptable performance thresholds evaluating said model(s), which leads to their implementation in solving real-world problems. Embodiments disclosed herein, accordingly, leverage captured dataset metadata, as well as graph techniques, to identify and suggest one or more supplemental features distinct from original features for, and yet relevant to, the real-world problem and any machine learning models being evaluated.

Metadata-based feature expansion for automated machine learning processes

One example method includes receiving a transaction at a digital twin that incorporates all transactions that have occurred at a site from which the transaction was received, and wherein the digital twin was created based in part on a data confidence fabric ledger, entering the transaction in the data confidence fabric ledger at the digital twin, receiving another transaction at the digital twin, wherein the another transaction has caused a problem to occur, entering the another transaction in the data confidence fabric ledger, replaying any transactions that have occurred in a defined time window that includes the another transaction, based on the replaying, identifying a state of a system where the problem occurred, and a time when the problem occurred, and determining a resolution to the problem.

Best outcome AIOps modeling with data confidence fabrics

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North Andover, MA, United States of America

Stephen J Todd