Abstract: A device may receive, from a user device, a request for a set of forecasts of an extract transform load (ETL) completion time for a group of ETL jobs associated with an organization. The device may obtain a set of performance indicators associated with the group of ETL jobs. The device may filter the set of performance indicators using one or more filtering techniques. The device may generate the set of forecasts of the ETL completion time by using a data model to process the set of performance indicators and/or a set of assumptions associated with a set of recommendations for reducing the ETL completion time. The device may provide the set of forecasts of the ETL completion time to the user device. The device may perform one or more actions that cause the ETL system to execute the group of ETL jobs within a threshold completion time.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for container image vulnerability reduction. In one aspect, a method includes obtaining a first container image that is formed from a set of files, determining that a first particular file of the set of files is necessary for an application and a second particular file of the set of files is not necessary for the application based on execution of the application in a first container instantiated with the first container image, in response generating a second container image from the first particular file and not from the second particular file, and executing the application in a second container instantiated with the second container image.

Abstract: A transaction platform may provide access, to a distributed shared data structure, to a user device and a service provider device. The transaction platform may store, in the distributed shared data structure, user information associated with a user and service information associated with a service provider. The transaction platform may process a transaction between the user and the service provider by using a first set of processing nodes to generate a first transaction result and a second set of processing nodes is to generate a second transaction result. The transaction platform may determine whether the first transaction result and the second transaction result are the same, and may store, based on determining that the first transaction result and the second transaction result match, a smart contract, associated with the transaction, in a distributed ledger.

Abstract: A device may determine a plurality of components for a development project. The device may determine a blueprint template based on the plurality of components of the development project. The device may generate a blueprint for the development project based on the blueprint template. The device may generate a package for the development project based on the blueprint for the development project. The device may provide output associated with the blueprint and the package based on generating the blueprint and generating the package.

Abstract: A device may perform a testing operation on a device under test. The device may obtain test result data based on performing the testing operation on the device under test. The device may identify a user device that is to receive the test result data associated with the device under test from the device and via a network. The device may be in communication with a set of user devices via the network. The set of user devices may include the user device. The user device may control operation of the device. The device may determine network condition information associated with the user device and the network. The device may provide, using a technique that is based on the network condition information, the test result data to the user device. The user device may receive the test result data based on controlling operation of the device.

Abstract: Techniques are described for generating prediction metrics that describe one or more predicted characteristics and/or outcomes of a software development project. Implementations employ machine learning (ML) algorithms to develop prediction models to predict characteristics of a project. For example, the models can be employed to predict a number of defects that may be present in a software product that is produced by a software development project. A model can be trained using input data captured from previous software projects, such as data describing the feature set of a software product, the timeline for its development, the particular type or domain of software, a number of modules, functions, etc., the particular number and/or identity of personnel working on the project, and so forth. Such input data can be analyzed to extract features that are provided as training data to train a model that is used to predict project metrics.

Abstract: An augmented reality (AR) device may identify a package in using a camera of the AR device. The AR device may identify a first corner of the package based on detecting differences in contrast, of a plurality of surfaces of the package, in a video feed generated by the AR device. The AR device may identify, based on identifying the first corner, a second corner of the package, a third corner of the package, and a fourth corner of the package. The AR device may determine a plurality of dimensions of the package. The AR device may transmit, to a shipping management platform, information identifying the plurality of dimensions, wherein the plurality of dimensions is to be used to generate a loading configuration for loading the package, along with a plurality of other packages, into a cargo container.

Abstract: A transport stack may control identity information that may be owned by a user. An information record of the identity information may be stored on a distributed ledger. Transactors may request a viewing-share for the identity information to support transactions with the user. The transport stack may generate a grant record when a transactor is provided with a viewing-share of the identity information. The grant record may be stored on the distributed ledger. The distributed ledger may provide a verifiable record of the identity information content and history of viewing-share grants.

Abstract: Implementations are directed to receiving a first set of event data from a first data source, the first set of event data including data representative of an occurrence of a real-world event, providing an event ticket, the event ticket including at least a portion of data of the first set of event data, transmitting presentation data to one or more computing devices, the presentation data being based on the event ticket and being processable by the computing device to display a real-time view including one or more graphical representations representative of the real-world event, receiving a second set of event data from a second data source, the second set of event data including data associated with the real-world event, revising the event ticket to include at least a portion of data of the second set of event data to provide a revised event ticket, and transmitting revised presentation data to one or more computing devices, the revised presentation data being based on the revised event ticket and being proc

Abstract: A device receives and processes current, forecasted, and historical entity information, associated with an entity, to generate processed information. The device calculates an operating enterprise value for the entity based on the processed information and bifurcates the operating enterprise value into a current value associated with current operations of the entity and a future value associated with investments of the entity. The device determines a growth rate based on the current value and the future value, and processes the current value, the future value, and the growth rate, with a first model, to determine underlying drivers of total returns for stakeholders associated with the entity. The device processes the underlying drivers of total returns for stakeholders and revenue, costs, assets, and liabilities associated with the entity, with a second model, to identify analytics data for the entity, and performs actions based on the analytics data identified for the entity.

Abstract: A method and system for fault analysis and prediction in an enterprise environment is described. In one embodiment, a method includes obtaining data from a plurality of sources in the enterprise environment. The plurality of sources includes at least one or more systems, users, or applications. The obtained data is associated with identifiers that include a theme selected from a set of themes and one or more keywords that are specific to each theme. The method includes generating a workflow for a user based on a session identifier and/or timestamps associated with activity by the user. The workflow identifies a time-based sequence of interactions by the user with the at least one or more systems or applications in the enterprise environment. The method also includes determining at least one fault identification or fault prediction based on the workflow and identifiers associated with the obtained data that corresponds to the workflow.

Abstract: A multi-layer committed compute reservation stack may generate prescriptive reservation matrices for controlling static reservation for computing resources. A transformation layer of the committed compute reservation stack may generate a time-mapping based on historical utilization and tagging data. An iterative analysis layer may determine a consumption-constrained committed compute state of a distribution of static reservation and dynamic requisition that achieves one or more consumption efficiency goals. Once the consumption-constrained committed compute state is determined, the prescriptive engine layer may generate a reservation matrix that may be used to control computing resource static reservation prescriptively.

Abstract: A device may receive information regarding a software environment and/or one or more modules associated with the software environment; receive information regarding one or more compliance requirements; determine one or more smart contracts based on the information regarding the one or more compliance requirements and the information regarding the software environment and/or the one or more modules associated with the software environment; obtain data regarding the software environment and/or the one or more modules; determine, using the one or more smart contracts, whether the data satisfies the one or more compliance requirements; and add at least part of a block, to a blockchain, to indicate whether the data satisfies the one or more compliance requirements, for realization of a reward or penalty associated with the one or more compliance requirements.

Abstract: Systems, apparatuses, and methods are directed toward audio and visual mapping. A visual frame associated with an environment may be received. The visual frame may be segmented into a plurality of regions of interest. A first position for a first region of the plurality of regions of interest is determined. A determination may be made that the first position intersects with a projected area associated with a user. The projected area is to include one or more areas that are outside of a current direction of movement of the user. One or more characteristics of a first auditory stimulus are selected based on the first position and the determination that the first position is within the projected area.

Abstract: An example method described herein involves receiving a data input; identifying a plurality of topics in the data input; determining an underrepresented set of data for a first set of topics of the plurality of topics based on a plurality of knowledge graphs associated with the first set of topics; calculating a score for each topic of the first set of topics based on a representative learning technique; determining that the score for a first topic of the first set of topics satisfies a threshold score; selecting a topic specific knowledge graph based on the first topic; identifying representative objects that are similar to objects of the data input based on the topic specific knowledge graph; generating representation data that is similar to the data input based on the representative objects to balance the underrepresented set of data with a set of data associated with a second set of topics of the plurality of topics; and performing an action associated with the representation data.

Abstract: A device may receive a widget template of a widget. The widget template may include information relating to one or more fields of the widget and content for the one or more fields. The device may receive a stylesheet of the widget. The stylesheet may include information relating to respective style identifiers of the one or more fields. The device may generate a markup object of the widget based on the widget template. The markup object may include respective markup object entries for populating the one or more fields with the content. The device may assign the respective style identifiers to the respective markup object entries, and cause an action to be performed in connection with the markup object.

Abstract: A device receives historical information associated with an individual to be monitored, wherein the historical information includes at least one of information associated with a health history of the individual, health histories of other individuals, activities of the individual, or activities of the other individuals. The device receives monitored information associated with the individual from one or more client devices associated with the individual, and pre-processes the monitored information to generate pre-processed monitored information that is understood by the trained machine learning model. The device processes the pre-processed monitored information, with a trained machine learning model, to identify one or more activities of the individual and one or more deviations from the one or more activities by the individual, and performs one or more actions based on the one or more activities of the individual and/or the one or more deviations from the one or more activities.

Abstract: A system may receive a credential token and an access command. The access command may identify access logic to modify a datablock stored in a blockchain. The credential token may be mapped to a role token included in a role-based access control model. The system may determine a role-based access control framework authorizes the access command by identifying an authorization token in the role-based access control model, and the system may determine the role token is mapped to the identified authorization token in the role-based access control model. The system may determine the datablock includes the identified authorization token. The system may validate the identified authorization token. The system may authorize execution of the access logic to modify the datablock in response to the access command being authorized by the role-based access control framework, and validation of the identified authorization token included in the datablock.

Abstract: A device may receive one or more images that depict one or more shapes associated with a first object that includes a portal. The device may identify, based on the one or more images, one or more characteristics of the one or more shapes. The device may determine, based on the one or more characteristics of the one or more shapes, one or more attributes of the portal. The device may determine, based on the one or more attributes of the portal, positioning information to be used to position a second object relative to the portal of the first object. The device may output, based on the positioning information, one or more control signals associated with positioning the second object relative to the portal.

Abstract: A data storage layer provides enhanced data security. In one implementation, the data storage layer allows selective encryption of specific parts of data blocks, with decryption restricted to pre-determined entities. The selective encryption may be applied to parts of data blocks that are stored in blockchains, for instance, to provide fine grained control over which entities with access to the blockchain can decrypt and use the parts of the data blocks with the enhanced security.