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 a query that recites a particular question for which a user who originated the query needs an answer, parsing the query to identify the question, identifying information that is responsive to the question, presenting the information to the user in a user-selectable form, and receiving, from the user, a selection of the information. In some cases, the information presented to the user may include one or more datasets, or one or more pipelines.

Abstract: One example method includes receiving a query that recites a particular question for which a user who originated the query needs an answer, parsing the query to identify the question, identifying information that is responsive to the question, presenting the information to the user in a user-selectable form, and receiving, from the user, a selection of the information. In some cases, the information presented to the user may include one or more datasets, or one or more pipelines.

Abstract: Techniques are disclosed for normalized asset valuation and secure storage of information associated with such normalized asset valuation. In one example, a method obtains two or more asset valuation models for a given asset, wherein at least two of the two or more asset valuation models are generated using different asset valuation algorithms, and then converts one or more values in each of the two or more asset valuation models into a common valuation form such that the two or more asset valuation models are converted into two or more normalized valuation models. The common valuation form may be in the form of a cryptocurrency. The method may also combine the two or more normalized valuation models to form a single normalized valuation for the given asset. Still further, the method may store valuation information associated with the converting step in a secure storage system, e.g., a distributed ledger system.

Abstract: 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.

Abstract: One example method includes receiving a user request for a data set, and the user request includes information concerning user requirements for the data set, identifying data records that satisfy one or more of the user requirements, calculating a respective relative value for each of the data records, and the relative values are based in part on the user requirements, and providing access controls for each data record that enable tracing of accesses of the data record.

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: One example method includes checking an asset against an Inclusion List and/or an Exclusion List to determine if the asset is permitted to contribute data, generated by the asset, to an enterprise data confidence fabric, when the asset is present on the Inclusion List, or not present on the Exclusion List, designating the asset as a trusted asset and appending the data generated by the asset to a ledger of the enterprise data confidence fabric, updating a ledger content index to reflect the data that was appended to the ledger, and annotating the data generated by the asset with trust metadata.

Abstract: One example method includes receiving, at an entity, a stream of data and associated trust metadata, inspecting, by the entity, the trust metadata to identify a policy annotation, when the entity is capable of doing so, processing, by the entity, the stream of data according to requirements of the policy annotation, and annotating, by the entity, the processed data with an annotation to indicate that the data was processed in accordance with the requirements of the policy annotation.

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: Data valuation techniques in a sensor data environment are provided. For example, a method obtains at least one sensor data element generated by at least one sensor associated with a set of one or more sensors operatively coupled to a gateway. At least one data valuation algorithm is applied to the at least one sensor data element to compute at least one value. The computed value is distributed for assignment to one or more of the gateway, the at least one sensor, and the at least one sensor data element.

Abstract: Techniques are disclosed for data valuation using language-neutral content addressing techniques in an information processing system. For example, a method comprises the following steps. The method obtains original content in an original language. The method generates a language-neutral representation of the original content. The method then generates an object comprising the language-neutral representation of the original content and at least one valuation algorithm, wherein the at least one valuation algorithm is configured to perform content valuation. The method generates a cryptographic hash value of the object, and stores the object for access using the cryptographic hash value.

Abstract: Routing data in a data confidence fabric. Data ingested into a data confidence fabric is routed to maximize confidence scores and to minimize the amount of missing confidence information. Routing is based on a configuration file and on pathing map information that allows nodes capable of applying the trust insertions set forth in the configuration file to be identified.

Abstract: Mapping applications or smart contracts to a data confidence fabric. A smart contract is deployed and executed in a data confidence fabric based on trust requirements of the smart contract. The trust requirements are mapped to the nodes of the data confidence fabric. A ledger is created on the identified nodes and the application is deployed to and run on the identified nodes.

Abstract: One example method includes detecting a threat in a data confidence fabric, assigning a data confidence score to data implicated by the threat, generating trust insertion metadata concerning the threat, creating a ledger entry based on the data confidence score and the trust insertion metadata, and using the ledger entry to determine an overall data confidence score for the data confidence fabric. A data threat portfolio view is generated based on the data confidence score and the trust insertion metadata, and the data threat portfolio view is presented to a user.

Abstract: Techniques for value-based data reputation management in a data marketplace environment are provided. For example, a method comprises the following steps. In a data marketplace environment with at least one data producer and at least one data consumer, an electronic data storage area is established, by one of the data producer and the data consumer, for receiving and storing feedback data from the other of the data producer and the data consumer. The feedback data relates to a transaction between the data producer and the data consumer with respect to a given data set.

Abstract: One example method includes identifying user information associated with a user-specific decentralized identity (DID) of a first party, associating the user information with the DID, defining data terms that specify which user information may be shared, and under what circumstances, presenting the DID for verification by a second party that comprises a computing entity, receiving an indication that the DID has been verified by the computing entity, and based on verification of the DID, entering into a transaction with the second party, wherein the transaction comprises providing, to the computing entity, in accordance with the data terms, only user information that is needed by the computing entity to effect the transaction.

Abstract: One example method includes receiving, at a node of a data confidence fabric (DCF), a DCF backbone, installing the DCF backbone at the node, receiving a config file at the node, and the config file includes configuration information concerning the node, and receiving and installing a trust insertion component specified in the configuration information, where operation of the trust insertion component is enabled by the DCF backbone, and the trust insertion component is operable to associate trust metadata with data received by the node.

Abstract: In a multi-cloud computing environment comprising a plurality of cloud platforms across which an enterprise stores primary data and copies of the primary data, a method maintains a distributed ledger system with a plurality of nodes, wherein a given one of the plurality of cloud platforms is operatively coupled to a given one of the plurality of nodes of the distributed ledger system. Further, the method manages capacity planning and data placement for the primary data and the copies of the primary data in association with the distributed ledger system by storing transaction data in the distributed ledger system that represents at least one of one or more pricing models associated with each cloud platform and one or more regulatory policies associated with each cloud platform to enable an environment-wide view of at least one of the pricing models and the regulatory policies of the plurality of cloud platforms.

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.