The improved exercise of artificial intelligence by providing a systematic way for a computing system to interface with output from AI models. To do this, the computing system obtains results of an input data set being applied to an AI model. The results are then refined based upon characteristic(s) of the AI model and perhaps the input data set. Based upon characteristic(s) of the AI model and perhaps the input data set, interface element(s) are identified that can be used to interface with the refined results. The interface element(s) are then communicated to an interface element that interfaces with the refined results. The interface element(s) may include, for instance, operator(s) or term(s) that may be used to query against the refined results and/or an identification of visualization(s) that may be used to present to a user results of queries against the refined results.

Systems and methods can suggest applications and/or recipients for a user of a computing device to perform communication. The suggestions can be provided on a user interface for a user to select. A suggestion engine can use historical user interactions that include a recipient, a communication application used to communicate with the recipient, and contextual data to determine which application and/or recipients to suggest. The user interactions may occur in a variety of ways, e.g., after a content object has been selected within a host application, where a communication application is selected thereafter. Multiple models may be used to provide the suggestions, such as a pattern model or a cluster model that uses recent user interactions. As another example, a heuristics model may also be used.

Systems and methods can suggest applications and/or recipients for a user of a computing device to perform communication. The suggestions can be provided on a user interface for a user to select. A suggestion engine can use historical user interactions that include a recipient, a communication application used to communicate with the recipient, and contextual data to determine which application and/or recipients to suggest. The user interactions may occur in a variety of ways, e.g., after a content object has been selected within a host application, where a communication application is selected thereafter. Multiple models may be used to provide the suggestions, such as a pattern model or a cluster model that uses recent user interactions. As another example, a heuristics model may also be used.

A system may include a processor and a memory. The memory may include computer-executable code that, when executed by the processor, causes the processor to retrieve a workflow dataset from a database based on a query input associated with an industrial automation device. The workflow dataset may include an instruction associated with one or more operations for the industrial automation device and a virtual object associated with the one or more instructions and the industrial automation device. The memory may include computer-executable code that, when executed by the processor, causes the processor to transmit a first portion of the workflow dataset to a computing device corresponding to a first instruction. The memory may include computer-executable code that, when executed by the processor, causes the processor to transmit a second portion of the workflow dataset to the computing device in response to determining that the first instruction is completed.

A system may include a processor and a memory. The memory may include computer-executable code that, when executed by the processor, causes the processor to retrieve a workflow dataset from a database based on a query input associated with an industrial automation device. The workflow dataset may include an instruction associated with one or more operations for the industrial automation device and a virtual object associated with the one or more instructions and the industrial automation device. The memory may include computer-executable code that, when executed by the processor, causes the processor to transmit a first portion of the workflow dataset to a computing device corresponding to a first instruction. The memory may include computer-executable code that, when executed by the processor, causes the processor to transmit a second portion of the workflow dataset to the computing device in response to determining that the first instruction is completed.

A method for providing optimized support for transactional processing and analytical processing with minimal memory footprint may include storing, on a data page in a disk of a database system, a portion of one or more columns of data from a database table. A metadata associated with the data page may be stored on a metadata page in the disk of the database system. The metadata may include one or more byte ranges on the data page at which the portion of the one or more columns of data is stored. The database system may execute one or more queries by accessing, based at least on the metadata associated with the data page, a portion of the data page storing the portion of the one or more columns of data required by the one or more queries. Related systems and articles of manufacture are also provided.

A method for providing optimized support for transactional processing and analytical processing with minimal memory footprint may include storing, on a data page in a disk of a database system, a portion of one or more columns of data from a database table. A metadata associated with the data page may be stored on a metadata page in the disk of the database system. The metadata may include one or more byte ranges on the data page at which the portion of the one or more columns of data is stored. The database system may execute one or more queries by accessing, based at least on the metadata associated with the data page, a portion of the data page storing the portion of the one or more columns of data required by the one or more queries. Related systems and articles of manufacture are also provided.

In accordance with one disclosed method, a computing system may receive, via a first version of an application presenting an interface in a first language, a first query entered in a second language different from the first language. The computing system may search resources accessible to a second version of the application to identify at least a first resource corresponding to the first query, the second version of the application being in the second language. Based at least in part on a result of the searching, an indication of the first resource may be returned to the interface.

In accordance with one disclosed method, a computing system may receive, via a first version of an application presenting an interface in a first language, a first query entered in a second language different from the first language. The computing system may search resources accessible to a second version of the application to identify at least a first resource corresponding to the first query, the second version of the application being in the second language. Based at least in part on a result of the searching, an indication of the first resource may be returned to the interface.

Security related anomalies in the data related to network entities are identified, and a risk score is assigned to each entity based on the anomalies. Visualization data is generated for a color-coded interactive visualization. Generating the visualization data includes assigning each entity to a separate polygon to be displayed concurrently on a display screen; selecting a size of each polygon to indicate one of: a number of security related anomalies associated with the entity, or a risk level assigned to the entity, where the risk level is based on the risk score of the entity, and selecting a color of each polygon to indicate the other one of: the number of security related anomalies associated with the entity, or the risk level assigned to the entity; and causing, the color-coded interactive visualization to be displayed on a display device based on the visualization data.