Abstract: 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.

Abstract: A data confidence fabric that is network topology aware is disclosed. When data is able to be transmitted over one of multiple communication channels, confidence information related to the transmission of the data over a selected communication channel is associated with the data. Events, such as failover, are also reflected in the confidence information and confidence score of the data. The confidence information and/or confidence scores allow applications to determine how to trust data in a manner that accounts for the transmission path.

Abstract: 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.

Abstract: 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.

Abstract: Routing data in a data confidence fabric network. A confidence fabric adds confidence information to data delivered through the data confidence fabric network. A route is selected by considering inputs that include the trust insertion capabilities of nodes in potential routes, resource usage of nodes in the potential routes, and/or historical delivery times for the potential routes. One of the potential routes is selected as a selected route. The selected route may be based on best anticipated delivery time in view of the inputs.

Abstract: One example method includes monitoring, by a profiler at a node of a data confidence fabric, resource consumption of one or more resources by the node in connection with operations performed by the node, performing a check to determine if resource consumption by the node is within a specified range, and based on an outcome of the check, assigning, or adjusting, a confidence score for the resource consumption. The confidence score may be used as a basis to perform one or more actions concerning the node and its workloads.

Abstract: Time sensitive networking with data confidence is disclosed. Data ingested into a data confidence network is associated with annotations and/or confidence scores based on trust insertion technologies applied to the data. When time sensitive networking is applied, the annotations and confidence score reflect the application or use of time sensitive networking. Applications may determine whether to use or have confidence in the data based on the confidence score and/or annotations related to the use of time sensitive networking.

Abstract: 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.

Abstract: A data confidence fabric allows data to be associated with confidence scores that reflect how the data may be trusted. Sensors or other devices may generate data. Confidence scores for the sensors are determined and the data confidence fabric determines a security strategy for the data based on the confidence score of the sensor generating the data. Confidence information associated with the data may include an annotation reflecting the security strategy that was applied and which may impact the confidence score of the data.

Abstract: One example method includes receiving, at a node of a data confidence fabric, data from a data source, determining, by the node, whether the data source is listed in a portfolio of data sources, when the data source is listed in the portfolio, processing, by the node, the data, when the data source is not listed in the portfolio, performing, by the node, an analysis of the data, when the analysis reveals that the data is recognized by the node, processing, by the node, the data, and when the analysis reveals that the data is not recognized by the node, performing an action.

Abstract: One example method includes receiving, at a node of a data confidence fabric, a data stream, prepending, by the node, a data confidence fabric header to the data stream so as to create a prepended data stream, and the data confidence fabric header includes confidence metadata relating to the data stream, and transmitting, by the node, the prepended data stream to another node of the data confidence fabric. The confidence metadata includes metadata about hardware and/or software associated with the data in the data stream.

Abstract: 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.

Abstract: One example method includes receiving data from a node of a data confidence fabric, in response to receipt of the data, creating an annotation bookmark that (i) identifies a storage node at which the data was received and that (ii) uniquely identifies the data, annotating a copy of the data with the annotation bookmark, and storing, at the storage node, the copy of the data in association with the annotation bookmark.

Abstract: One method includes obtaining a snapshot of a data confidence fabric billing manifest, broadcasting the snapshot to one or more nodes of the data confidence fabric, deploying, at the nodes, billing logic included in the data confidence fabric billing manifest, and executing the billing logic to obtain information concerning a trust service provided regarding data associated with the nodes.

Abstract: Global optimization of quantum jobs in a multi-cloud or multi-edge environment is disclosed. The quantum jobs of multiple vendors are consolidated in a telemetry plane. The quantum jobs are evaluated based on user intents, quantum job characteristics, and quantum processing unit characteristics. The quantum jobs are then assigned to the quantum systems of the vendors based on the evaluation.

Abstract: 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.

Abstract: Distributing quantum jobs are disclosed. When a quantum processing unit is underutilized or when wait times are long, quantum jobs may be distributed from the job queue of one vendor to another vendor. This improves utilization and reduces wait times.

Abstract: Quantum job prioritization is disclosed. Quantum jobs may be stored as placeholders in a job queue associated with a quantum processing unit. The quantum jobs are prioritized to improve the usage of the quantum processing unit. Prioritizing quantum jobs allows the quantum processing unit to execute quantum jobs in different orders rather than on an application basis. This allows grace periods to be used for executing quantum jobs.

Abstract: 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.

Abstract: Techniques are disclosed for decentralized data flow valuation and deployment in a highly distributed system. For example, a method comprises the following steps performed in a highly distributed system comprising a plurality of gateway nodes, wherein each of the plurality of gateway nodes is operatively coupled to one or more sensors that provide sensor data to the corresponding gateway node. A data flow is obtained at a given gateway node from a decentralized data storage system upon which a plurality of data flows is stored. The data flow is executed at the given gateway node, wherein the data flow is executed on sensor data obtained by the given gateway node from the corresponding one or more sensors. A data flow valuation operation is performed at the given gateway node based on the executed data flow to generate a data flow valuation result.