Diffusing a root identity of an entity among association and event covenants in a multi-dimensional computing security system involves generating a first generation of diffusion of identities of entities participating in mediated association and generating a second generation of diffusion of identities of the entities through recombinant mediated association of the entities and at least one other entity. The second generation of diffusion of identities facilitates securely constraining a computing system action associated with one of the entities.

A computer-implemented schema-independent method of modeling data from diverse sources is described. A server transmits to a client computer a blueprint for visualizing and interacting with data, wherein the blueprint defines an application, for visualizing and interacting with data. The application may operate on the client computer within a web browser and may include program code or scripts that operate within the web browser and transmit data and commands to and from the server. In response to receiving a data fetch message from the application, the server receives data from a selected one of a plurality of domains. The server then transforms the received data into a semantic data format. The transformed data is then stored by the server as a first data set. The first data set can then be transmitted to the client computer for further processing and visualization by the application using the blueprint.

A method for searching data includes storing a probe data and a target data expressed in a first orthogonal domain. The target data includes potential probe match data each characterized by the length of the target data. The probe data representation and the target data are transformed into an orthogonal domain. In the orthogonal domain, the target data is encoded with modulation functions to produce a plurality of encoded target data, each of the modulation functions having a position index corresponding to one of the potential probe match data. The plurality of encoded target data is interfered with the probe data in the orthogonal domain and an inverse transform result is obtained. If the inverse transform result exceeds a threshold, information is output indicating a match between the probe data and a corresponding one of the potential probe match data.

A data processing system for producing a subset of data from a plurality of data sources, including: memory storing a plurality of data sources to be represented in an editor interface; a data structure modification module that selects a plurality of data sources to be represented in an editor interface and generates a subset of data included in the plurality of data sources; memory that stores the selected data structures included in the subset, with at least one of the stored data structures including the one or more modified attributes of the one or more respective fields; rendering module that displays, in the editor interface, representations of the stored data structures; and a segmentation modules that segments a plurality of received data records.

Improved workflow processes for efficiently progressing through a credit union membership application using a user interface having a specially structured layout to facilitate the progression are disclosed. Initially, the user interface presents a membership account selection option to select which type of membership account to initiate. Based on the selected type of membership account, the user interface then displays a product selection option for selecting a specific type of product to initiate with the credit union. The user interface then displays an applicant information option structured to receive information about an applicant. Based on this information, there is a determination as to whether the applicant qualifies for membership. Additionally, the layout of the user interface is specifically required to have a number of features, which are designed to facilitate the application workflow.

The present disclosure discloses a method for acquiring a structured question-answering (QA) model, a QA method and corresponding apparatuses, and relates to knowledge graph and deep learning technologies in the field of artificial intelligence technologies. A specific implementation solution involves: acquiring training samples corresponding to N structured QA database types, the training samples including question samples, information of the structured QA database types and query instruction samples used by the question samples to query structured QA databases of the types, N being an integer greater than 1; and training a text generation model by using the training samples to obtain the structured QA model, wherein the question samples and the information of the structured QA database types are taken as input to the text generation model, and the query instruction samples are taken as target output of the text generation model.

According to one embodiment, a method, computer system, and computer program product for provisioning a tag schema. The embodiment may include determining data types for tag-keys within an existing ecosystem of tags. Each tag of the existing ecosystem comprises a tag-key and a tag-value. The embodiment may include performing pairwise clustering of the tags within the ecosystem. The embodiment may include identifying a main tag-key within each cluster of tags. The embodiment may include grouping the clusters into a broader category. The embodiment may include providing a tag schema recommendation for the ecosystem based on the grouped clusters.

A method, system and computer program product for processing metrics data with graph data context analysis. Graph data representing one or more devices or sensors is stored into a first database, and metrics data generated by the devices or sensors is stored in a second database. The metrics data is then applied to the graph data for the context analysis, wherein the context analysis reflects the relationships of the devices or sensors in the graph data to the metrics data generated by the devices or sensors. The graph data comprises nodes for representing the devices or sensors, edges for representing a topology of the devices or sensors, and properties for storing the metrics data associated with the nodes and edges; and the metrics data comprises time-series data that is logged by the devices and sensors.

Embodiments relate to metadata-based generic parsing of an incoming data object to transform it for different purposes, such as conversion into a format more suitable for a specific storage type. The received data object comprises members organized according to a structure, each member containing data. The object's structure and the member's type are defined by the associated metadata. The metadata is referenced by parsing logic to generate a schema representing the structure of the data object in a different format (such as an ER table schema). The schema is then stored. When data is received, the metadata is referenced to parse the corresponding object's data accordingly and for ingestion into the generated schema and storage.

An example method of displaying a data visualization includes displaying a plurality of selectable fields and receiving user selections of two different fields from the plurality of selectable fields. The method also includes generating, in accordance with the received user selections, data marks to be displayed in a data visualization, each data mark corresponding to a respective retrieved tuple of data from a multidimensional database, where (i) each data mark has an x-position defined according to data for a first field in the respective tuple and (ii) each data mark has a y-position defined according to data for a second field in the respective tuple. The method also includes displaying the data visualization that includes the generated data marks.