Dell Products Inc.

Emc IP Holding Company LLC

Dell USA L P

Jason Alan Shepherd

Stephen J Todd

Yuan-Chang Lo

Philip M Seibert

Claude Lano Cox

Michael S Gatson

Todd Erick Swierk

Abu Shaher Sanaullah

Clifton J Barker

Liam B Quinn

Aurelian Dumitru

Raziuddin Ali

Gaurav Chawla

Charles D Robison

Jimmy Doyle Pike

Charles Delbert Robison, Jr

Jason Tait Franz

Roger A Siedow

Neal R Kohl

Carlton A Andrews

James Don Curlee

Charles D Hood

Douglas Evan Messick

Douglas Miles Anson

Robert Warren Johnson

Daniel S Carr

Eugene Buican

Kevin M Bailey

Richard Worley

Richard L Eddings, Ii

Robyn Reed McLaughlin

Marina Levina

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

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

One example method includes receiving authorization from a human user to collect data concerning an interaction of the human user with a computing element, interacting with the human user, collecting data concerning the interaction, analyzing the collected data, generating trust and confidence information, concerning the human user, based on analysis of the collected data, and storing the trust and confidence information.

Human trust API in a data confidence fabric

Hardware trust boundaries in a data confidence fabric are provided. Nodes in a data confidence fabric are provisioned with identifies and confidence scores. Hardware-based trust insertion technologies are applied to data in the data confidence fabric. Protocols allow nodes to join the data confidence fabric and be aware of other nodes. Paths of data can be graphed and audited.

Hardware trust boundaries and graphs in a data confidence fabric

A trust broker is disclosed for a data confidence fabric. The trust broker evaluates the trustworthiness of data flowing through a network that includes a data confidence fabric. The trust broker evaluates a baseline confidence score and generates a workorder to improve the baseline confidence score in a measurable way. The trust broker may implement the workorder and ensure that the trust improves in the data confidence fabric.

Data confidence fabric trust brokers

One example method includes deploying a group of bots in a computing environment that includes a group of nodes, each of the bots having an associated attack vector with respect to one or more of the nodes, receiving, from each of the bots, a report that identifies a node attacked by that bot, and a result of the attack, and adjusting, based on the bot reports, a confidence score of one or more of the attacked nodes.

Increasing edge data confidence via trusted ethical hacking

A method for managing data includes obtaining, by an indirection logic service, a data request for data, wherein the data request specifies a ledger entry, identifying an indirection logic entry stored in the indirection logic service based on the ledger entry, obtaining a selection of trust data from a client, wherein the ledger entry comprises metadata of the trust data, and initiating communication between the client and a local trust manager based on the selection of trust data, wherein the trust data was generated by the local trust manager.

System and method for redirecting data access to local trust managers via an indirection logic service

In general, in one aspect, the invention relates to a method for managing data, the method includes performing, by a data confidence fabric (DCF) node, a trust operation with a remote DCF node, updating, based on the trust operation, a trust level associated with the remote DCF node to obtain an updated trust level, integrating, based on the updated trust level, the remote DCF node into a DCF processing path to obtain a second DCF processing path, wherein the DCF processing path comprises the DCF node, and processing data using the second DCF processing path.

Method and system creating and using data confidence fabric processing paths

In general, in one aspect, the invention relates to a method for managing data, the method includes obtaining, by a data management system, a first resource usage of a local data system associated with a first data confidence fabric (DCF) pipeline, wherein the first DCF pipeline is associated with a first workload, obtaining second resource usage of the local data system associated with a second DCF pipeline, wherein the second DCF pipeline is associated with a second workload, analyzing the first resource usage and the second resource usage to obtain a workload ranking, and performing an action set based on the workload ranking.

Method and system optimizing the use of sub-data confidence fabrics

A social overlay is provided that allows an application to seek human input when performing high-risk decisions. The overlay allows an application to obtain data confidence scores when performing operations and to obtain human confidence scores that can be used to seek input from users with high confidence scores. This allows applications to improve performance and avoid situations where an automated application may make a wrong decision that does not account for the associated risk.

Human trust overlays for automated data confidence fabrics

Techniques are provided for dynamic communication protocol control in a communication environment. For example, a request is sent by a given processing node of a plurality of processing nodes to a computing system. Each of the plurality of processing nodes is operatively coupled to the computing system. Each of at least a subset of the plurality of processing nodes is configured to communicate via at least a first communication protocol with which each of the subset of the plurality of processing nodes share bandwidth. The request seeks usage of a given amount of additional bandwidth associated with the first communication protocol by the given processing node from one or more of the subset of processing nodes. One or more of the subset of processing nodes can then decide to cede the additional bandwidth to the given processing node and convert to using another communication protocol.

Dynamic gateway communication protocol control via software defined radio implemented devices

A compute node currently operating within a given computing environment and currently enabled to support at least a first communication protocol obtains one or more instructions for enabling the compute node to support a second communication protocol. In response to the one or more instructions, one or more configuration parameters associated with the compute node are automatically reconfigured to support the second communication protocol. An automatic determination is made, in the given computing environment within which the compute node currently operates, whether or not to certify the compute node for the second communication protocol.

Dynamic certification for configuration changes to software defined radio implemented devices

In general, in one aspect, the invention relates to a method for managing data. The method includes obtaining, by a local data manager, a first data confidence fabric (DCF) configuration file, where the first DCF configuration file is associated with a first DCF pipeline and a first workload. The method further includes registering the first DCF pipeline in a DCF pipeline registry, obtaining a data set, identifying the first DCF pipeline using the DCF pipeline registry, and processing the data set based on the first DCF pipeline to obtain first processed data.

Method and system creating and using sub-data confidence fabrics

Systems and methods for managing network connections and priorities based on device profiles are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution by the processor, cause the IHS to: receive instrumentation data from a plurality of devices coupled to the IHS via a network; receive observation data from the plurality of devices; correlate the observation data with the instrumentation data; and configure communications between the IHS and one or more the plurality of devices based, at least in part, upon the correlation.

Management of network connections and priorities based on device profiles

Systems and methods for communicating with a plurality of networked sensors include identifying at least one class of networked sensors of the plurality of networked sensors, broadcasting a virtual network identifier to advertise the availability of the at least one class of networked sensors, directing a device attempting to access a network identified by the virtual network identifier to a splash page where a user can subscribe to information from the one or more classes of network sensors, and providing sensor data from the network sensors to a subscribed user.

Growing community of inventors

Austin, TX, United States of America

Jason Alan Shepherd