Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: Various embodiments of the present application set forth a computer-implemented method that includes receiving, from a device, a natural-language (NL) request. The method further includes selecting, using the NL request, an intent from a set of intents, wherein the intent is associated with a pre-defined intent template, the pre-defined intent template including a set of property fields that are associated with one or more portions of the NL request. The method also includes determining, based on the NL request, a set of property field values for the set of property fields. The method further includes generating a query to be executed on a field-searchable data source, wherein the query is based on one or more property field values included in the set of property field values. The method also includes receiving, in response to the query, a result that includes a set of event field values. In addition, the method includes causing the device to display at least a portion of the result.

Abstract: In various embodiments, a natural language (NL) application receives a partial NL request associated with a first context, and determining that the partial NL request corresponds to at least a portion of a first next NL request prediction included in one or more next NL request predictions generated based on a first natural language (NL) request, the first context associated with the first NL request, and a first sequence prediction model, where the first sequence prediction model is generated via a machine learning algorithm applied to a first data dependency model and a first request prediction model. In response to determining that the partial NL request corresponds to at least the portion of the first next NL request prediction, the NL application generates a complete NL request based on the first NL request and the partial NL request, and causes the complete NL request to be applied to a data storage system.

Abstract: Various embodiments of the present application set forth a computer-implemented method that includes processing a first natural language (NL) request, where the first NL request includes a first artifact. The method further includes determining that a first relationship, associated with the first artifact and useable to process the first NL request, is unavailable in a first NL language processing system. The method further includes generating a first data relationship recommendation based on the first NL request. In addition, the method includes causing the first data relationship recommendation to be provided to a user.

Abstract: In various embodiments, a natural language (NL) application enables users to more effectively access various data storage systems based on NL requests. As described, the NL application includes functionality for selecting an optimal interpretation algorithm, generating a dashboard, and/or generating an alert based on an NL request. Advantageously, the operations performed by the NL application reduce the amount of time and user effort associated with accessing data storage systems and increase the likelihood of properly addressing NL requests.

Abstract: In various embodiments, a natural language (NL) application receives a first incomplete natural language (NL) request, and generates one or more request completion recommendations based on at least the first incomplete NL request and a first recommendation model, where the first recommendation model is generated via a machine learning algorithm applied to a first data dependency model and a first request completion model. The NL application receives a selection of a first request completion recommendation included in the one or more request completion recommendations, generates a complete request based on the first incomplete NL request and the first request completion recommendation, and causes the complete request to be applied to the data storage system.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: In various embodiments, a natural language (NL) application implements functionality for recommending trending NL requests to users of the application. The functionality includes generating rating data associated with a plurality of natural language (NL) requests and one or more intents corresponding to the plurality of NL requests, wherein the rating data indicates a preference of at least one user for using at least one of the plurality of NL request to access data, training a trends recommendation model based on the rating data associated with the plurality of NL requests, generating a set of NL request recommendations based on the trends recommendation model, and causing the set of NL request recommendations to be presented in a query recommendation interface.

Abstract: Various embodiments of the present application set forth a computer-implemented method that includes receiving, from a device, a natural-language (NL) request. The method further includes selecting, using the NL request, an intent from a set of intents, wherein the intent is associated with a pre-defined intent template, the pre-defined intent template including a set of property fields that are associated with one or more portions of the NL request. The method also includes determining, based on the NL request, a set of property field values for the set of property fields. The method further includes generating a query to be executed on a field-searchable data source, wherein the query is based on one or more property field values included in the set of property field values. The method also includes receiving, in response to the query, a result that includes a set of event field values. In addition, the method includes causing the device to display at least a portion of the result.

Abstract: A natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. The operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: In various embodiments, a natural language (NL) application receives a partial NL request associated with a first context, and determining that the partial NL request corresponds to at least a portion of a first next NL request prediction included in one or more next NL request predictions generated based on a first natural language (NL) request, the first context associated with the first NL request, and a first sequence prediction model, where the first sequence prediction model is generated via a machine learning algorithm applied to a first data dependency model and a first request prediction model. In response to determining that the partial NL request corresponds to at least the portion of the first next NL request prediction, the NL application generates a complete NL request based on the first NL request and the partial NL request, and causes the complete NL request to be applied to a data storage system.

Abstract: Various embodiments of the present application set forth a computer-implemented method that includes processing a first natural language (NL) request, where the first NL request includes a first artifact. The method further includes determining that a first relationship, associated with the first artifact and useable to process the first NL request, is unavailable in a first NL language processing system. The method further includes generating a first data relationship recommendation based on the first NL request. In addition, the method includes causing the first data relationship recommendation to be provided to a user.

Abstract: In various embodiments, a natural language (NL) application enables users to more effectively access various data storage systems based on NL requests. As described, the NL application includes functionality for selecting an optimal interpretation algorithm, generating a dashboard, and/or generating an alert based on an NL request. Advantageously, the operations performed by the NL application reduce the amount of time and user effort associated with accessing data storage systems and increase the likelihood of properly addressing NL requests.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.

Abstract: In various embodiments, a natural language (NL) application enables users to more effectively access various data storage systems based on NL requests. As described, the NL application includes functionality for selecting an optimal interpretation algorithm, generating a dashboard, and/or generating an alert based on an NL request. Advantageously, the operations performed by the NL application reduce the amount of time and user effort associated with accessing data storage systems and increase the likelihood of properly addressing NL requests.

Abstract: In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.