Emc Corporation

Dell Products Inc.

Emc IP Holding Company LLC

Other

Emc International Company

Hanna Yehuda

Ofir Ezrielev

Nicole Reineke

Oran Epelbaum

Daniel Lanzi

Frank Murphy

Kristen Jeanne Walsh

Inga Sogaard

JoAnne Hubbard

Ju-Lien Lim

Morrie Gasser

Francois Gauvin

Amanuel Ronen Artzi

Serena M Doyle

James M Apple

Alan R Schell

James Edward Lavallee

Robert Barrett

Mark Adam Arakelian

Ethan D Roberts

Anne-Marie McReynolds

Michaell A Galvin

Corinne Schulze

Rony R Baenziger

Gary K Roberts

Debra Arneson

Alan Sevajian

Stephen J Todd

Robert Anthony Lincourt, Jr

Joel Evans Christner

Serge Marokhovsky

Aurelian Dumitru

David Barta

Donagh A Buckley

Vandana Mallempati

Farida Shafik

Eran Dvir

Kenneth Fickie

Steven Teng

Erik Shawn Hansen

Shary Beshara

Paul Bradley

Norris V Li

Zakir G Magdum

Michael Patrick Bushe

Ming Qian

Jiale Huo

Freda Weinbrecht

John C Sexton

Joshua Onffroy

David S Shaffer

Brad Corey Maltz

Eyal Kedem

Ashok Palani

Christopher Hill

Roland Raphael Baenziger

Katarzyna Leskow

James Bernard McElroy, Jr

Charles Dao

Kassem Al-Sayed Ali

Anita M Stout

Elizabeth J Phalen

Ketan Shah

Omar Abdul Aal

Sunil Ramchandra Upalekar

Ahmad Refaat Abdel Fadeel Ahmad El Rouby

Ravi Luhadiya

Charles Joseph Williams

Eitan Segal

Methods and systems for curating data by a data manager are disclosed. Data may be curated from various data sources before being provided to downstream consumers that may rely on the trustworthiness of the curated data in order to provide desired computer-implemented services. During the data curation process, data curation resources are used to improve the trustworthiness and/or value of the collected data. However, data curation resources (e.g., data curators, computing resources) may be limited and/or insufficient to perform the data curation process as desired, which may result in unusable and/or uncurated (e.g., untrustworthy) data. Thus, the data may be screened for anomalous data points. Features of the anomalous data points that meet importance criteria may be presented to the data source and the data source may indicate whether the features are expected. If the features are expected, the data pipeline may be populated with the anomalous data points.

Curating anomalous data for use in a data pipeline through interaction with a data source

Methods and systems for curating data by a data manager are disclosed. Data may be curated from various data sources before being supplied to downstream consumers, and the downstream consumers may rely on the trustworthiness of the curated data to facilitate desired computer-implemented services. Therefore, to identify data (e.g., poisoned data) that, if provided to downstream consumers may negatively impact the desired computer-implemented services, the data may undergo anomaly detection. Data anomalies indicating the presence of poisoned data may be identified based on anomaly patterns exhibited by the data. However, an anomaly pattern may be likely to appear by chance (e.g., may incorrectly indicate the presence of poisoned data) in large volumes of complex data. Therefore, anomaly detection may be coupled with false positive detection in order to manage false positive anomalies. When a true positive anomaly (e.g., poisoned data) is detected, the poisoned portion may be identified and/or remediated.

Identifying poisoned data during data curation using false positive detection

Methods and systems for managing operation of a data pipeline are disclosed. To manage the operation, a system may include one or more data sources, a data manager, and one or more downstream consumers. Changes to a system of representation of information in data requested by the downstream consumers may cause the data pipeline to provide unusable data to the downstream consumers. To remediate the change, a translation schema may be obtained to translate data based on an unexpected system of representation of information to data based on an expected system of representation of information. The translation schema may be tested and, if the translation schema is determined successful, a translation layer may be added to the data pipeline based on the translation schema.

Remediating a change in a system of representation of information in data used by a data pipeline

Methods and systems for managing operation of a data pipeline are disclosed. To manage the data pipeline, a system may include one or more data sources, a data repository, and one or more downstream consumers. Updates to the data pipeline may cause the data pipeline to become misaligned. To avoid misalignment and, therefore, failure of the data pipeline, information regarding the operation of the data pipeline may be distributed and used to decide how to update the data pipeline. The update may be evaluated through simulation prior to implementation.

Managing data pipelines using graphical user interfaces

Methods and systems for curating data from data sources are disclosed. Data may be curated from various data sources before being stored in a repository and/or supplied to downstream consumers. The downstream consumers may rely on the trustworthiness of the curated data to provide desired computer-implemented services. During the data curation process, collected data may undergo quality control processes such as anomaly detection that may identify anomalies in the data. The identified anomalies may indicate the presence of poisoned data that, if provided to downstream consumers, may negatively impact the computer-implemented services facilitated by the downstream consumers. When poisoned data is detected among the data, portions of the data affected by the poisoned data (e.g., the poisoned portions) may be identified using an optimization process. The poisoned data may be used to identify and initiate performance of an action set that may reduce the impact of the poisoned data.

System and method for identifying poisoned data during data curation

Methods and systems for managing operation of a data pipeline are disclosed. To manage the data pipeline, a system may include one or more data sources, a data repository, and one or more downstream consumers. As new data becomes available for use in the data pipeline, the new data may be automatically evaluated for utility. The evaluation may be made through establishment of a shadow pipe. The shadow pipe may allow for comparison of operation of the data pipeline with the new data against operation of the data pipeline without the new data.

Managing integration of new data for data pipelines

Methods and systems for curating data from data sources are disclosed. Data may be curated from various data sources before being supplied to downstream consumers that may rely on the trustworthiness of the curated data to facilitate desired computer-implemented services. During data curation, collected data may undergo anomaly detection to identify anomalies in the data. Data anomalies may indicate the presence of poisoned data that, if provided to downstream consumers, may negatively impact the desired computer-implemented services. When poisoned data is detected among the data, a poisoned portion of the data may be identified using an optimization process. The optimization process may consider the degree of anomalousness of the data (e.g., using statistical representations of the anomaly) and/or characteristics of the data source that supplied the anomalous data to identify the poisoned portion. Remedial actions may be identified and/or performed in order to reduce an impact of the poisoned data.

System and method for identifying poisoned data during data curation using data source characteristics

Techniques for authenticating a digital asset represented by an NFT that is stored in a blockchain are disclosed. An NFT that has been minted for an asset is identified. The NFT was minted by generating metadata for the asset and by generating an original hash of the asset. The NFT is stored in a blockchain, and the metadata is stored in a centralized repository. The original hash is stored in either the repository or the blockchain. A potential transfer of the NFT is triggered. Triggering the potential transfer includes downloading, from the centralized repository, a local version of the asset. A local hash of the local version of the asset is generated. The local hash is compared against the original hash. If the two hashes are the same, then the transfer is permitted; otherwise, the transfer is not permitted.

Method and system of NFT validation in a broker marketplace

Methods and systems for managing operation of a data pipeline are disclosed. To manage the data pipeline, a system may include one or more data sources, a data repository, and one or more downstream consumers. Updates to the data pipeline may cause the data pipeline to become misaligned. To avoid misalignment and, therefore, failure of the data pipeline, any updates intended for implementation in the data pipeline may first be implemented in a digital twin of the data pipeline. The digital twin and the data pipeline may operate concurrently using substantially identical data. The performance of the digital twin following implementation of the potential update may be compared to the performance of the data pipeline without implementation of the potential update to obtain a performance delta. If the performance delta meets a threshold, the potential update may cause failure of the data pipeline and may be remediated prior to implementation.

System and method for managing a data pipeline using a digital twin

Methods and systems for managing operation of a data pipeline are disclosed. To manage operation of a data pipeline when a portion of data is inaccessible may require generating synthetic portion of data to generalize the inaccessible portion of data. Prior to the generation of the synthetic portion of data, an analysis of an intended use of the inaccessible portion of data may be performed. The analysis may reduce the likelihood of generation and use of synthetic portion of data that is unreliable. Once obtained, the synthetic portion of data may be analyzed to determine a likelihood that the synthetic portion of the data may successfully generalize the inaccessible portion of data. When the synthetic portion of data is determined to meet or exceed quality criteria, the synthetic portion of data may be utilized by the data pipeline.

Operating a data pipeline based on data privacy regulations

Methods and systems for managing operation of a data pipeline are disclosed. To manage the operation, a system may include one or more data sources, a data manager, and one or more downstream consumers. Requests for data from the downstream consumers may have unexpected characteristics that may cause misalignment of application programming interfaces used by the data pipeline. To remediate the misalignment and reduce occurrences of future misalignments, an error message may be obtained indicating a type of error associated with the request. The error message may be used to obtain an error classification for the request and an action set may be performed based on the error classification. In addition, data provided to the downstream consumers may cause misalignment of an application programming interface used by the downstream consumers. Similarly, an error message may be obtained and used to identify an appropriate action set to remediate the misalignment.

System and method for remediating misalignment of a data pipeline using error messages

Methods and systems for managing operation of a data pipeline are disclosed. To manage the operation, a system may include one or more data sources, a data manager, and one or more downstream consumers. Interruptions to the operation may impact provision of data processing services by the data pipeline and may cause the data processing services to no longer align with operation quality goals for the data pipeline. To maintain compliance with the operation quality goals, the operation may be monitored over time. Operation data may be obtained for the data pipeline and may be used to determine representations of operation quality of the data pipeline. The representations of operation quality of the data pipeline may be compared to the operation quality goals and actions may be performed to remediate differences between the representations of operation quality of the data pipeline and the operation quality goals.

Monitoring operation of a data pipeline to obtain representations of operation quality

The technologies described herein are generally directed toward deploying networked equipment based on a selected deployment model. According to an embodiment, a system can comprise a processor and a memory that can enable performance of operations including receiving a deployment request from a request source, for a deployment task comprising networked equipment to be deployed and a location for deployment of the networked equipment. The operations can further include querying a data source for deployment information implicating the location and the deployment task. Based on the request source, the deployment task, and the deployment information, the operations include selecting a deployment model to facilitate processing the deployment request. The operations additionally include, based on the deployment task and the deployment model, deploying the networked equipment at the location.

Deploying networked equipment based on a deployment model

Methods and systems for curating data by a data manager are disclosed. Data may be curated from various data sources before being provided to downstream consumers that may rely on the trustworthiness of the curated data in order to provide desired computer-implemented services. During the data curation process, data curation resources are used to improve the trustworthiness and/or value of the collected data. However, data curation resources (e.g., data curators, computing resources) may be limited and/or insufficient to perform the data curation process as desired, which may result in unusable and/or uncurated (e.g., untrustworthy) data. Thus, the data may be screened for ambiguous values. A potential replacement value for each ambiguous value may be provided to the data source and the data source may indicate whether the potential replacement value should be used in the data pipeline as a final replacement value for the ambiguous value.

Curating ambiguous data for use in a data pipeline through interaction with a data source

A PCIe resource management system includes a PCIe resource registration subsystem and a PCIe resource monitoring subsystem. Assets register to use PCIe resources of other assets and to allow other assets to use their PCIe resources. Assets specify which types of PCIe resources it can borrow, when it can borrow those PCIe resources, and a logical group of other assets from which the asset can borrow the PCIe resources. Assets also specify which types of PCIe resources it will lend, when it will lend those PCIe resources, and a logical group of other assets to which the asset will lend the PCIe resources. The PCIe resource registration subsystem maintains a PCIe resource registration datastore maintaining PCIe borrow and lending rules associated with assets in logical groups of assets. PCIe resources are shared between assets in the logical groups as needed, as determined by the PCIe resource monitoring subsystem.

Method and apparatus for establishing trusted PCIe resource sharing

Growing community of inventors

Newton, MA, United States of America

Hanna Yehuda