Embodiments of the present disclosure provide apparatuses, methods, computer program products, and systems for data perspective generation and visualization. Some example embodiments provide advantages of exploring various ideas, represented by and/or associated with one or more perspective data objects, without utilizing a complex re-configuration stage for one or more machine learning models, and/or without utilizing one or more team members conventionally required to ensure proper implementation of the idea(s). Similar advantages are obtained when desiring to change an existing idea, change the level of granularity associated with the processing, and/or the like. Some embodiments are configured to cause rendering of interfaces associated with the processing, and/or enable a user to user interaction for inputting the desired level of granularity of the generated perspectives and/or desired granularity for the analysis of the data set. Some embodiments generate various graphical data objects to enable such processing.

Methods for displaying improved user interfaces are disclosed. The method includes receiving a user interface request to display a user interface of an issue tracking system from a user device. The user interface displays a plurality of objects and the user interface request includes a user identifier of a user of the user device and a user identifier of the requested user interface. The method further includes determining a time since the user last viewed the user interface, retrieving object data for the requested user interface based on the user interface identifier, retrieving activity data for the requested user interface based on the time since the user last viewed the user interface, and communicating the object data and activity data to the user device for displaying on the user device, the user device displaying the object data and displaying one or more activity indicators based on the received activity data.

A system for electronic data interchange (EDI) management includes a memory for storing the EDI document data and a machine learning model representing a set of features of EDI documents and a corresponding status. The system further includes a processor and a non-transitory computer readable medium storing instructions for: accessing an EDI file, the EDI file comprising envelope metadata for an envelope and a first EDI document; and translating the EDI file into a first translated EDI document containing the envelope metadata and a set of EDI document data extracted from the first EDI document, the first translated EDI document formatted according to a hierarchical structure comprising attributes translatable into features processable by the machine learning model to determine a status of the first EDI document.

Embodiments are directed to managing visualizations of data. A provided data model may include a tree specification that declares parent-child relationships between objects in the data model. In response to a query associated with objects in the data model: employing the parent-child relationships to determine a tree that includes parent objects and child objects from the objects based on the parent-child relationships; determining a root object based on the query and the tree; traversing the tree from the root object to visit the child objects in the tree; determining partial results based on characteristics of the visited child objects such that the partial results are stored in an intermediate table; and providing a response to the query that includes values based on the intermediate table and the partial results.

A method executes at a computing device that includes a display. The device displays a user interface that includes a data flow pane and a calculation pane, the data flow pane including a node/link diagram for a data prep flow. A user selects a node in the diagram, and the device populates affordances in the calculation pane according to data fields of a data set associated with the selected node. A first user input specifies grouping on a first data field, and a second user input specifies an aggregation function on a second data field. In response to the user inputs, for each distinct value of the first data field, the device aggregates corresponding values of the second data field according to the aggregation function. Calculated data values are displayed in the calculation pane. The device saves rows of data displayed in the calculation pane as a new data source.

Techniques are disclosed relating to methods that include preprocessing, by a computer system, records of a database to create one or more token sets for a given record. The created token sets may correspond to ones of a plurality of search string functions, and may include token sets that include a plurality of possible substrings located within data strings of a corresponding database record. The methods may further include receiving a query for a search of the database. The query may include at least one of the plurality of search string functions. The method may also include performing the search by traversing, using at least a portion of the records, at least one token set corresponding to the included search string functions, as well as returning results for the search based on the query and the traversing.

Systems and methods for a relationship-based search in a computing environment are provided. An example method includes providing a graph database. The graph database includes nodes representing workloads of the computing environment and edges representing relationships between the nodes. The method also includes enriching the graph database by associating the nodes with metadata associated with the nodes and the relationships. The method also includes receiving a user query including data associated with at least one of the metadata and the relationships. The method also includes determining, based on the user query, a subset of the nodes in the graph database and a subset of relationships between the nodes in the subset of the nodes. The method also includes displaying, via a graphical user interface, a graphical representation of the subset of the nodes and relationships between the nodes in the subset of the nodes.

A method includes receiving, by a logic layer processor, over a communication network, from a plurality of electronic resources, initial user personal identifiable information (PII) of a user of a plurality of users. The user PII includes a plurality of data elements. The plurality of data elements of the initial PII of the user are classified to populate a profile map data structure having a standardized predefined data schema of a plurality of vector elements so as to form a user-specific profile map data structure of the user. Additional user personal identifiable information (PII) of the user is iteratively received from the plurality of electronic resources. The additional user PII of the user is iteratively classified to update the user-specific profile map data structure of the user. A plurality of user-specific data management software functions is enabled based on the user-specific profile map data structure.

A computer-implemented method of retrieving and displaying data from an entity network database (130) comprising a plurality of entities and a plurality of relationships between the entities is provided. The method comprises receiving a selection of a plurality of anchor entities of the entity network database; retrieving a plurality of connector entities of the entity network database, each connector entity disposed on a path in the entity network database extending between at least two of the anchor entities; determining a relevance score for each of the connector entities, the relevance score reflecting the relevance of each connector entity to the anchor entities; and displaying the connector entities within a shape defined by the anchor entities, wherein the distance of each connector entity from the anchor entities is based on the relevance score of the connector entity.

Systems and methods are described herein for interpreting natural language search queries that account for contextual relevance of words of the search query that would ordinarily not be processed, including, for example, processing each word of the query. Each term or phrase is associated with a respective part of speech, and a frequency of occurrence of a combination of adjacent terms or phrases public domain is determined. A relevance of each term is then determined based on its respective type of term and frequency of occurrence in the public domain. The natural language search query is then interpreted based on the importance or relevance of each term.