Aggregation in a computing system can include receiving, at a service node of the computing system, a first query specifying aggregation and translating the first query into a second query having a first canonical format and specifying the aggregation. The method can include forwarding the second query to a first subset of a plurality of endpoint nodes and translating, at each endpoint node of the first subset, the second query into a third query having a format executable by a data source connected to the endpoint node. The third query can specify a level of the aggregation to be performed by the data source determined based upon a processing capability of the data source. The endpoint nodes can initiate execution of the third query by the data sources and provide an aggregated result including a result from the data source(s) to the service node.

Aggregation in a computing system can include receiving, at a service node of the computing system, a first query specifying aggregation and translating the first query into a second query having a first canonical format and specifying the aggregation. The method can include forwarding the second query to a first subset of a plurality of endpoint nodes and translating, at each endpoint node of the first subset, the second query into a third query having a format executable by a data source connected to the endpoint node. The third query can specify a level of the aggregation to be performed by the data source determined based upon a processing capability of the data source. The endpoint nodes can initiate execution of the third query by the data sources and provide an aggregated result including a result from the data source(s) to the service node.

The present disclosure provides an apparatus for and method of generating a context category dataset. According to some embodiments, the present disclosure provides a context category dataset generating apparatus and method which predict a context category to which a user-inputted hashtag belongs, receive from the user the user's context category to which the hashtag belongs, and generate and update the context category dataset.

The present disclosure provides systems and methods that generate query templates that are expressed in a generic schema-agnostic language. The query templates can be generated “from scratch” or can be automatically generated from existing queries, a process which may be referred to as “templatizing” the existing queries. As one example, generation of query templates can be performed through an iterative process that iteratively generates candidate templates over time to optimize a coverage over a set of existing queries. After generation of the schema-agnostic query templates, the systems and methods described herein can automatically translate/map the templatized queries into “concrete,” schema-specific queries that can be evaluated over specific customer schemas/datasets. In this manner, a query template for a given semantic query (e.g., “return the names of all employees”), is required to be written only once.

The present disclosure describes a system, method, and computer program for converting a natural language update instruction to a structured update database statement. In response to receiving a natural language query for a database, an NLU model is applied to the query to identify an intent and entities associated with the query. If the intent is to update a data object, the system evaluates the entities to identify update fields and update values. Update fields are matched to update values based on update parameters, operand type of the update value, and location of the update fields and values. For each update field and value pair, an update context is calculated to determine whether the update value is absolute or relative to an existing field value. An update plan is created with the update field and value pairs and corresponding update contexts, and a database update statement is generated from the update plan.

A datastore engine at an edge location of a content delivery network (CDN) may perform low-latency query processing and data retrieval for multiple types of databases at one or more origin servers. When a client sends a query to the edge location, the datastore engine translates the query from a back-end database format into a native format of the local edge datastore. If the requested data is not there, then the datastore engine retrieves the data from the back-end table and inserted inserts the data into the local edge datastore. By using multiple queries over time to re-construct data from the backend database tables at the edge, the datastore engine may provide low-latency access to data from the backend database tables (avoiding the need to retrieve data from the back-end tables to serve subsequent queries).

A datastore engine at an edge location of a content delivery network (CDN) may perform low-latency query processing and data retrieval for multiple types of databases at one or more origin servers. When a client sends a query to the edge location, the datastore engine translates the query from a back-end database format into a native format of the local edge datastore. If the requested data is not there, then the datastore engine retrieves the data from the back-end table and inserted inserts the data into the local edge datastore. By using multiple queries over time to re-construct data from the backend database tables at the edge, the datastore engine may provide low-latency access to data from the backend database tables (avoiding the need to retrieve data from the back-end tables to serve subsequent queries).

A system and method for translating questions into database queries are provided. A text to database query system receives a natural language question and a structure in a database. Question tokens are generated from the question and query tokens are generated from the structure in the database. The question tokens and query tokens are concatenated into a sentence and a sentence token is added to the sentence. A BERT network generates question hidden states for the question tokens, query hidden states for the query tokens, and a classifier hidden state for the sentence token. A translatability predictor network determines if the question is translatable or untranslatable. A decoder converts a translatable question into an executable query. A confusion span predictor network identifies a confusion span in the untranslatable question that causes the question to be untranslatable. An auto-correction module to auto-correct the tokens in the confusion span.

A search interface is displayed in a table format that includes one or more columns, each column including data items of an event attribute, the data items being of a set of events, and a plurality of rows forming cells with the one or more columns, each cell including one or more of the data items of the event attribute of a corresponding column. Based on a user selecting one or more of the cells, a list of options if displayed corresponding to the selection, and one or more commands are added to a search query that corresponds to the set of events, the one or more commands being based on at least an option that is selected from the list of options and the event attribute for each of the one or more of the data items of each of the selected one or more cells.

Systems and methods for managing a highly available distributed hybrid database comprising: a memory storing instructions; and one or more processors configured to execute the instructions to: receive a query from a user device to retrieve data from a distributed database comprising a source node, a first plurality of replica nodes, and a second plurality of replica nodes, wherein the source node and the first plurality of replica nodes form a transactional cluster, and wherein the second plurality of replica nodes forms an analytical cluster; determine whether to process the query using the transactional cluster or the analytical cluster based on one or more rules; translate the query into a first protocol that the determined cluster comprehends; select a replica node corresponding to the determined cluster; process the query using the selected replica node; and send data associated with results from processing the query to the user device.