Abstract: A knowledge data management system (KDMS) implements data refinement orchestration, resolution, and refinement to provide a reusable and generic solution to ingest and link data via relationships and properties when constructing a knowledge graph. The KDMS thus operates to break down existing data storage silos by normalizing and integrating data with a uniform semantic schema, which results in more accurate and faster knowledge graph construction.

Abstract: Implementations are directed to integrating a computer-executed service platform with multiple channels, and include actions of providing a transition layer as an application executed on a client-side device, which receives input from a channel of the multiple channels, and providing a unified input that is in a unified format for processing by the service platform, receiving, by the service platform, the unified input, identifying, by the service platform, a set of actions based on the unified input, the set of actions including to be executed by one or more back-end services, providing a service layer including a plurality of bots, each bot interacting with a back-end service based on an action to receive at least one result from the respective back-end service, receiving result data including data describing the at least one result, and transmitting an output based on the result data through the transition layer of the client-side device.

Abstract: An ingestion code generation architecture facilities making large and complex datasets available for processing by parallel processing clusters. The architecture generates a set of data ingestion interfaces through which the operator specifies characteristics of their dataset. After receiving the specifications, the architecture automatically samples the dataset, analyzes its structure, and generates program code to ingest the dataset. The architecture solves the technical challenges of making complex and extensive datasets readily available to the parallel processing cluster so that the cluster may successfully perform its specialized processing over the dataset.

Abstract: A first device may receive, from a second device, an input corresponding to a search for an item, may identify a set of items, and may determine a set of trend scores associated with the set of items. The first device may determine a set of sentiment scores, and may identify a subset of items, of the set of items, based on the set of trend scores and the set of sentiment scores. The first device may provide, to the second device, information associated with the subset of items to permit the second device to provide, for display, the information associated with the subset of items, and may receive, from the second device, information associated with a selected item, of the subset of items. The first device may determine a return score associated with the selected item, and may perform an action based on the return score.

Abstract: A device may obtain, from one or more cameras, an image that depicts one or more encoded decals positioned on a body of a vehicle. The device may process the image and may identify, based on processing the image, one or more characteristics of the one or more encoded decals. The device may determine, based on the one or more characteristics of the one or more encoded decals, positioning information to be used to position a loading bridge relative to the body of the vehicle. The device may determine one or more control signals based on the positioning information. The device may provide the one or more control signals to one or more actuators to automatically position the loading bridge relative to the body of the vehicle.

Abstract: A machine provides a system and interface to allow domain experts and other users to develop, deploy, and iterate on analytical models. The machine facilitates building, deploying, and/or training analytical models by, e.g., exposing analytical model configuration parameters to a user while abstracting model building and model deployment activities. The machine can also determine resource loads or execution times for various analytical models and can schedule model execution accordingly. The machine also provides a dynamically reconfigurable user interface for controlling the system.

Abstract: A device may communicate with a group of devices to obtain data regarding a set of events occurring for the group of devices. The device may process the data regarding the set of events to remove a subset of data entries, from the data, that is associated with an anomalous event. A first layer of analysis may relate to the group of devices, a second layer of analysis relating to a set of sessions of operating a user interface via the group of devices, and a third layer of analysis relating to information provided via the user interface. The device may perform the multiple layers of analysis via a machine learning technique to identify an alteration relating to the information provided via the user interface. The device may alter the information provided via the user interface based on performing the multiple layers of analysis.

Abstract: According to an example, with respect to hash based device configuration management, a device configuration status and a device configuration hash value associated with a configuration file for a device may be ascertained. Based on a determination that the device configuration hash value matches an application configuration hash value, an application configuration status for the device may be modified to correspond to the device configuration status. If there is no match, a determination may be made as to whether the device has permission to locally modify the configuration for the device, and if not, a configuration file may be sent to the device to modify the configuration for the device. If the device has permission, the configuration file that is associated with the device configuration hash value may be requested, and a configuration record associated with the device may be updated based on the requested configuration file.

Abstract: A device may process application data or historical data to identify a set of metrics to be used to analyze a set of applications or to identify baseline values for the set of metrics. The device may determine a score, for each application of the set of applications, based on values for the set of metrics for the each application of the set of applications. The device may determine a refined classification for the each application of the set of applications based on the historical data or information related to the classification or the score. The device may generate a set of recommendations related to optimizing a current deployment of the set of applications. The device may perform an action to implement the set of recommendations related to optimizing the current deployment of the set of applications.

Abstract: A device may receive a plurality of data items related to an entity to be analyzed. The device may store the plurality of data items using a storage device based on receiving the plurality of data items. The device may analyze the plurality of data items to identify complexity factors related to the entity based on storing the plurality of data items. The complexity factors may positively impact the entity or negatively impact the entity. The device may generate a set of action items associated with modifying operations of the entity to impact the complexity factors related to the entity. The set of action items may be associated with positively impacting the entity. The device may perform a set of actions based on the set of action items to positively impact the entity.

Abstract: In some examples, application architecture generation may include ascertaining, for a project, an input that includes project information, component information, and target information, and parsing the project information to determine whether the project is an existing project or a new project. Application architecture generation may further include generating a component list from the component information, ascertaining components from the component list, and mapping each of the ascertained components to a corresponding target determined from the target information. Further, application architecture generation may include analyzing a dependency for each of the ascertained components relative to at least one other component of the ascertained components, and generating, based on the mapping and the analyzed dependency, an integrated output that includes an architecture for an application associated with the project.

Abstract: A device may receive information identifying a development project. The device may determine a set of scripts to assess program code of the development project. The device may execute the set of scripts to perform a project assessment of the development project after determining the set of scripts. The device may generate a user interface including information identifying a result of executing the set of scripts. The result may include information associated with detecting an execution type of error or information associated with detecting a non-execution type of error. The device may communicate with a client device to provide the user interface for display via the client device.

Abstract: A rapid application developer system is disclosed that provides a development platform configured to generate an application manifest document for developing a new target application within a certain industry. The application manifest is generated based on components identified from previously developed applications within the same industry as the target application.

Abstract: A system may provide a smart contract interface for blockchain interoperability. The system receive selection parameters indicative of a furnisher blockchain and a receiver blockchain. The system may obtain, based on the selection parameters, furnisher synchronization logic and receiver synchronization logic. The furnisher synchronization logic may include instructions to lock token data on the furnisher blockchain. The receiver synchronization logic may include instructions to commit token data to the receiver blockchain. The system may generate an interoperability smart contract comprising the furnisher synchronization logic and the receiver synchronization logic. The system may deploy the interoperability smart contract to a furnisher blockchain and the receiver blockchain.

Abstract: A system may receive a request to perform a token exchange between a first account and a second account. A selected token for the token exchange may be associated with the first account. A hash of the selected token may be stored on a furnisher blockchain of a first distributed ledger. The system may generate a new token and a hash of the new token. The system may generate a shielded exchange instruction, the shielded exchange instruction comprising the hash of the new token and a zero-knowledge proof, wherein the zero-knowledge proof is indicative of the selected token being associated with the first account. The system may submit the shielded exchange instruction to a distributed ledger technology platform to cause execution of a furnisher smart contract and a receiver smart contract. Each of the smart contracts are configured to separately verify the zero-knowledge proof and update corresponding blockchains to consummate and record the token exchange.

Abstract: According to an example, with respect to decentralized supply chain for three-dimensional printing based on distributed ledger technology, three-dimensional printing constraints may be ascertained for a three-dimensional part to be printed, and a plurality of attributes for three-dimensional printers may be ascertained. The three-dimensional printing constraints may be compared to each attribute of the plurality of attributes. A determination may be made as to whether the three-dimensional part is printable by at least one three-dimensional printer or not printable by any three-dimensional printer. Costs associated with three-dimensional printing of the three-dimensional part and non-three-dimensional printing based manufacturing of the three-dimensional part may be ascertained. A determination may be made as to whether the three-dimensional part is to be printed by the at least one three-dimensional printer.

Abstract: An applied intelligence framework may receive log information descriptive of a cloud computing stack. The applied intelligence framework may generate a stack token. The stack token may include a computer resource node representative a computer resource of the cloud computing stack. The applied intelligence framework may access, from an ontology repository, a diagnosis instruction. The diagnosis instruction may determine a fault based on at least one of the log parameters. The applied intelligence framework may execute the diagnosis instruction to determine the fault. The applied intelligence framework may append, to the stack token, a fault node representative of the fault. The applied intelligence framework may query an ontology repository based on the stack token to identify a resolution identifier. The applied intelligence framework may append, to the stack token, a resolution node.

Abstract: Techniques are described for aggregating data generated by multiple platforms of different types. A particular user (e.g., end-user) may interact with multiple individual (e.g., siloed) platforms of different types and/or that support different business purposes or industries. The individual platforms may generate data describing and/or resulting from these interactions. The data may be received, ingested, and processed by a super-platform. The super-platform may generate aggregate data by aggregating the data received from different individual platforms. Data aggregation may be performed on data that is generated by different individual platforms and that is associated with a particular user or multiple users. Aggregation may also be performed on data that is independent of any particular user, such as sensor data that describes an environment in proximity to the platform.

Abstract: Techniques are described for iteratively adjusting data processing decision results in accordance with rules. In some implementations, the applied rules may be data ethics rules associated with particular demographic groups, such as users in a particular geographic location, users in a particular age range, and so forth. The rules may describe the manner in which data, such as data that describes or identifies individuals, is collected, stored, analyzed, applied, manipulated, and/or destroyed. The various stages of data handling may be described as a data supply chain, and a set of rules may apply to the handling of data at one or more stages of the data supply chain. The rules may enforce data privacy considerations and/or other types of constraints on data handling.

Abstract: Examples for efficiently representing, processing and deciding amongst multiple ambiguous interpretations of human natural language text are described. Processing includes creating and augmenting an “interpretation graph” which represents all known ambiguous interpretations of some natural language text. The interpretation graph is made of vertices (junction points which lead to alternative interpretations) and ‘lexical items’ (natural language objects representing data blocks, tokens, word parts, phrases, clauses, parts of speech, entities, or semantic interpretations) that represent alternative ambiguous interpretations of portions of the text. The examples show a set of simple operations for augmenting the interpretation graph to create alternative interpretations.