Qualtrics, LLC

This disclosure relates to methods, non-transitory computer readable media, and systems generate a knowledge graph to implement, process, and analyze digital surveys and digital survey data cross computer networks. In particular, the disclosed systems can generate and utilize a knowledge graph to determine and coordinate topic ontologies, store and access digital survey data, and generate digital benchmarks. For example, the disclosed systems generate a knowledge graph based on a predefined ontology of topics by connecting topic nodes via a plurality of edges. Additionally, the disclosed systems receive survey data associated with administering an electronic survey to respondent client devices. The disclosed systems extract topics from the survey data and determine connections between the survey data and the topic nodes in the knowledge graph. In one or more embodiments, the disclosed systems generate digital benchmarks between sets of data based on the relationships.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for applying attribute definitions to various survey data and utilizing attribute definitions to generate survey analyses. In particular, in one or more embodiments, the disclosed systems intelligently determines attribute definitions to apply to digital surveys, digital survey questions, and digital survey responses. The disclosed systems can determine attribute definitions to apply to survey data based on user input, associated survey templates, associated prior use of attribute definition, analysis of the text of digital surveys, and a variety of other attributes of survey data. The disclosed systems can generate digital survey analysis by utilizing attribute definitions applied to a variety of types of survey data.

This disclosure relates to methods, non-transitory computer readable media, and systems generate a knowledge graph to implement, process, and analyze digital surveys and digital survey data cross computer networks. In particular, the disclosed systems can generate and utilize a knowledge graph to determine and coordinate topic ontologies, store and access digital survey data, and generate digital benchmarks. For example, the disclosed systems generate a knowledge graph based on a predefined ontology of topics by connecting topic nodes via a plurality of edges. Additionally, the disclosed systems receive survey data associated with administering an electronic survey to respondent client devices. The disclosed systems extract topics from the survey data and determine connections between the survey data and the topic nodes in the knowledge graph. In one or more embodiments, the disclosed systems generate digital benchmarks between sets of data based on the relationships.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for applying attribute definitions to various survey data and utilizing attribute definitions to generate survey analyses. In particular, in one or more embodiments, the disclosed systems intelligently determines attribute definitions to apply to digital surveys, digital survey questions, and digital survey responses. The disclosed systems can determine attribute definitions to apply to survey data based on user input, associated survey templates, associated prior use of attribute definition, analysis of the text of digital surveys, and a variety of other attributes of survey data. The disclosed systems can generate digital survey analysis by utilizing attribute definitions applied to a variety of types of survey data.

This disclosure relates to methods, non-transitory computer readable media, and systems that relay domain-event objects within an enhanced multi-platform data stream to listen for and react to digital events indicated by the domain-event objects that occur across a wide variety of computing platforms. Specifically, the disclosed systems can receive domain-event objects within the multi-platform data stream. From among the domain-even objects transmitted through the multi-platform data stream, the disclosed systems can identify a domain-event object that is relevant to a digital-analytics platform by identifying domain-event objects that include properties satisfying domain-event-listener rules. Based on an entity identifier and an object event from the relevant domain-event object, the disclosed systems can perform a platform action within the digital-analytics platform (e.g., to react to a change in another platform as indicated by the domain-event object).

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating account prioritization scores for various accounts based on experience data and organization data and providing visual representations of the account prioritization scores via a graphical user interface. In particular, in one or more embodiments, the disclosed systems organize experience data and organization data to utilize in generating the account prioritization score. Further, in some embodiments, the disclosed systems generate and provide an explanation of a key factor for the account prioritization score. Thus, the disclosed systems provide an accurate and efficient graphical user interface to enable quick comprehension of even very large datasets corresponding to various accounts.

This disclosure covers systems and methods that retrieve and transform a requested dataset into a data shape that a widget specifies for visualization. In certain embodiments, the disclosed systems and methods respond to data requests for widgets by retrieving datasets formatted according to different data models for different platforms. The systems and methods then transform the datasets into a data shape specified by the widgets. By transforming requested datasets to conform to data shapes specified by widgets, the disclosed systems and methods orchestrate the update of a widget (or multiple instances of a widget) with data that originally conformed to different data models and, in some cases, was formatted for different software applications.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for intelligently generating an efficient new data set for storage in non-temporary storage during ingestion of other inefficient data sets. In particular, in one or more embodiments, the disclosed systems ingests a subset of a second data set of operational data items along with a first data set of response data items based on correlations between the subset of operational data items and the response data items. Thus, the disclosed systems provide a robust solution to efficiently ingesting data sets while avoiding computing storage and processing waste.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for efficiently providing a graphical user interface for visualizing large amounts of data on a single interface. In particular, in one or more embodiments, the disclosed systems utilize topic hierarchies and multidata display elements to intuitively present many types of data for many topics on a single interface. The system also uses an activatable timeline to visualize changes to large amounts of data over time.

This disclosure relates to methods, non-transitory computer readable media, and systems generate a knowledge graph to implement, process, and analyze digital surveys and digital survey data cross computer networks. In particular, the disclosed systems can generate and utilize a knowledge graph to determine and coordinate topic ontologies, store and access digital survey data, and generate digital benchmarks. For example, the disclosed systems generate a knowledge graph based on a predefined ontology of topics by connecting topic nodes via a plurality of edges. Additionally, the disclosed systems receive survey data associated with administering an electronic survey to respondent client devices. The disclosed systems extract topics from the survey data and determine connections between the survey data and the topic nodes in the knowledge graph. In one or more embodiments, the disclosed systems generate digital benchmarks between sets of data based on the relationships.