The Multidimensional Machine Learning Data and User Interface Segment Tagging Engine Apparatuses, Methods and Systems (“MLUI”) transforms ambient condition data, sales data, user interface selections, cognitive intelligence question input inputs via MLUI components into project projections, campaigns, user interface visualizations, cognitive intelligence question output outputs. A category identifier selection is obtained via a category selection interaction interface mechanism. Entity segment identifier selections are obtained via entity segment selection interaction interface mechanisms. A set of visualization cognitive intelligence (CI) datapoint identifiers is determined as CI datapoint identifiers associated with each combination of a selected entity segment identifier and the selected category identifier. CI datapoint values corresponding to the set of visualization CI datapoint identifiers are retrieved from a NoSQL database configured to act as cache for generating visualizations based on metrics calculated using survey data. A visualization is generated using the retrieved CI datapoint values.

The Multidimensional Machine Learning Data and User Interface Segment Tagging Engine Apparatuses, Methods and Systems (“MLUI”) transforms ambient condition data, sales data, user interface selections, cognitive intelligence question input inputs via MLUI components into project projections, campaigns, user interface visualizations, cognitive intelligence question output outputs. A category identifier selection is obtained via a category selection interaction interface mechanism. Entity segment identifier selections are obtained via entity segment selection interaction interface mechanisms. A set of visualization cognitive intelligence (CI) datapoint identifiers is determined as CI datapoint identifiers associated with each combination of a selected entity segment identifier and the selected category identifier. CI datapoint values corresponding to the set of visualization CI datapoint identifiers are retrieved from a NoSQL database configured to act as cache for generating visualizations based on metrics calculated using survey data. A visualization is generated using the retrieved CI datapoint values.

A method including detecting a webpage accessed by a user on a computing device via a browser. Content on the webpage is determined, and a model is applied to the content to determine a plurality of keyword sets. A network search is performed based on each of the plurality of keyword sets to generate a plurality of search results. The plurality of search results are compared to the content, and the plurality of search results are compared to each other. A factualness of the content is determined based on the comparing of the plurality of search results to the content and based on the comparing of the plurality of the search results to each other, and the user is notified via the browser of the factualness of the content.

A method including detecting a webpage accessed by a user on a computing device via a browser. Content on the webpage is determined, and a model is applied to the content to determine a plurality of keyword sets. A network search is performed based on each of the plurality of keyword sets to generate a plurality of search results. The plurality of search results are compared to the content, and the plurality of search results are compared to each other. A factualness of the content is determined based on the comparing of the plurality of search results to the content and based on the comparing of the plurality of the search results to each other, and the user is notified via the browser of the factualness of the content.

Disclosed herein are methods, systems, and processes to distribute and disperse search loads to optimize security event processing in cybersecurity computing environments. A search request that includes a domain specific language (DSL) query directed to a centralized search cluster by an event processing application is intercepted. The event processing application is inhibited from issuing the search request to the centralized search cluster if a structured or semi-structured document matches the DSL query.

A system and method, including determining, by a processor, a data type for each column of a database table; determining, by the processor and based on the determined data type for each column of the database table, an indication of a size of the database table; calculating, by the processor and based on the determined indication of the size of the database table, a start nbit size for a nbit compression process to be used on the database table; specifying, by the processor, the calculated start nbit size for the nbit compression process; and compressing the database table by executing the nbit data compression process using the specified start nbit size.

A system and method, including determining, by a processor, a data type for each column of a database table; determining, by the processor and based on the determined data type for each column of the database table, an indication of a size of the database table; calculating, by the processor and based on the determined indication of the size of the database table, a start nbit size for a nbit compression process to be used on the database table; specifying, by the processor, the calculated start nbit size for the nbit compression process; and compressing the database table by executing the nbit data compression process using the specified start nbit size.

Technologies generally described herein relate to a computing device for an input assistance scheme for an online marketplace posting. In one aspect, a computing device receives an input image containing an object to be posted on an online marketplace. In response to receiving the object, the computing device extracts, from the input image, feature data relating to the object. The device performs a search of an image database based on the extracted feature data to determine one or more images containing the object. The computing device obtains information data of the determined images and generates a reference dataset for the object based on the reference dataset.

Embodiments of system and method for performing object-modifying commands in an unstructured storage service are described. Embodiments may include utilizing one or more nodes to implement an unstructured storage service including a service interface configured to service data storage requests and data retrieval requests from remote clients. The unstructured storage service is configured to isolate access to each of multiple storage domains of the service to different clients. Embodiments may also include receiving from a given remote client, a request to store a data object to a given storage domain of the service. The request further specifying a command to modify source data for the object prior to storing the object. Embodiments may also include using the unstructured storage service to perform the command without exporting the source data from the unstructured storage service to the given remote client prior to completion of the command.

Embodiments of system and method for performing object-modifying commands in an unstructured storage service are described. Embodiments may include utilizing one or more nodes to implement an unstructured storage service including a service interface configured to service data storage requests and data retrieval requests from remote clients. The unstructured storage service is configured to isolate access to each of multiple storage domains of the service to different clients. Embodiments may also include receiving from a given remote client, a request to store a data object to a given storage domain of the service. The request further specifying a command to modify source data for the object prior to storing the object. Embodiments may also include using the unstructured storage service to perform the command without exporting the source data from the unstructured storage service to the given remote client prior to completion of the command.