In one embodiment, a device, in communication with a plurality of data lake sites, receives a federated data lake query. The device determines a plurality of data lake operator sets that each correspond to one of the plurality of data lake sites, wherein each of the plurality of data lake operator sets is used to establish a respective data pipeline for the federated data lake query. The device selects a particular data lake site of the plurality of data lake sites as a destination for data pipelines that are established for the federated data lake query. The device sends the plurality of data lake operator sets that each correspond to one of the plurality of data lake sites to cause the plurality of data lake sites to send query results to the particular data lake site using the data pipelines, wherein the particular data lake site stitches the query results.

In one embodiment, a method includes receiving, at a network management system (NMS) from a client, a message having an object reference embedding a query, the query requesting an operation to be performed on data stored in a data tree maintained by the NMS. The method provides for generating, by the NMS, a result of the query by performing the operation on the data. In this embodiment, the method further provides for sending, by the NMS to the client, the result of the query. In some embodiments, the object reference may include a pathname.

Systems, computer-implemented methods, applications, user interfaces, and tangible non-transitory computer readable media for providing triggers in a system of record are disclosed. For example, a computer-implemented method may include maintaining a trigger associated with an application where the trigger comprises a set of conditions and a set of operations associated with a custom computer language that is supported by the application, evaluating the conditions associated with the trigger based on an occurrence of an event associated with the application, determining that the conditions associated with the trigger are satisfied based on the evaluating of the conditions, and executing the operations associated with the custom computer language based on determining that the conditions of the trigger are satisfied. For example, execution of such operations may include performing one or more actions in association with the application and/or one or more third-party applications that are integrated with the application.

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.

Disclosed herein are example embodiments of an improved narrative generation system where an analysis service that executes data analysis logic that supports story generation is segregated from an authoring service that executes authoring logic for story generation through an interface. Accordingly, when the authoring service needs analysis from the analysis service, it can invoke the analysis service through the interface. By exposing the analysis service to the authoring service through the shared interface, the details of the logic underlying the analysis service are shielded from the authoring service (and vice versa where the details of the authoring service are shielded from the analysis service). Through parameterization of operating variables, the analysis service can thus be designed as a generalized data analysis service that can operate in a number of different content verticals with respect to a variety of different story types.

A data hub for servicing data hub dependent data consumers includes a persistent storage and a data validator. The persistent storage stores validated data. The data validator obtains a data validation request; in response to obtaining the data validation request: imports data from a data aggregator to obtain the validated data; performs a continuity analysis of the validated data to generate a data deviation report; and provides a portion of the validated data to one of the data hub dependent data consumers.

Techniques for integrative machine learning using sentiment-based predictive inferences and symptom-based predictive are discussed herein. In one example, a method includes determining, based on one or more health monitoring logs, a first distribution of symptomatic prediction labels over a first period of time associated with the one or more health monitoring logs; processing the one or more health monitoring logs and using a sentiment detection machine learning model to determine a second distribution of extracted sentiment scores over the first period of time; generating, based on the first distribution and the second distribution, an aggregate distribution of inferred health-related predictions over the first period of time; and causing display of an aggregate distribution user interface that is configured to display the aggregate distribution.

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.

A data pre-processing architecture may include an interface and a pruning logic configured to receive, via the interface, at least one filter value from a query processor; use the at least one filter value to scan rows or columns of a data table stored in a memory; generate a selection indicator identifying a set of rows or columns of the data table where the at least one filter value resides; and provide to the query processor a filtered output based on the selection indicator.