Abstract: Some implementations generate logical queries from a canonical query, where the logical queries each reflect a modified scope of the canonical query. Implementations receive, via a personalized analytics system, a canonical query that is associated with a user. The canonical query can be analyzed to determine an intent of the canonical query. In turn, one or more implementations generate, based on the intent an anecdotal information associated with the user, a logical query that reflects a modified scope of the canonical query. In implementations multiple logical queries are generated and are processed to remove a duplicate logical query. A logical query can be used to extract data from a database associated with the personalized analytics system based on a modified scope.

Abstract: Techniques are described for dynamic phase generation and load reduction for a query. A query, for instance, is based on user input of a query in a natural language (NL) form, e.g., an NL query. Generally, an NL query may include multiple terms and/or phrases that make up a complex query, such as a sentence in a human-readable language. Accordingly, to enable a query result to be generated, the NL query is parsed into multiple logical sections and query contexts are determined for the logical sections. A set of search phases is generated based on the logical sections and the query contexts. The search phases can then be executed in a specific execution order to generate a query result for the NL query.

Abstract: Some implementations generate logical queries from a canonical query, where the logical queries each reflect a modified scope of the canonical query. Implementations receive, via a personalized analytics system, a canonical query that is associated with a user. The canonical query can be analyzed to determine an intent of the canonical query. In turn, one or more implementations generate, based on the intent an anecdotal information associated with the user, a logical query that reflects a modified scope of the canonical query. In implementations multiple logical queries are generated and are processed to remove a duplicate logical query. A logical query can be used to extract data from a database associated with the personalized analytics system based on a modified scope.

Abstract: Some implementations generate logical queries from a canonical query, where the logical queries each reflect a modified scope of the canonical query. Implementations receive, via a personalized analytics system, a canonical query that is associated with a user. The canonical query can be analyzed to determine an intent of the canonical query. In turn, one or more implementations generate, based on the intent an anecdotal information associated with the user, a logical query that reflects a modified scope of the canonical query. The logical query can be used to extract data from a database associated with the personalized analytics system based on the modified scope.

Abstract: Techniques are described for dynamic phase generation and load reduction for a query. A query, for instance, is based on user input of a query in a natural language (NL) form, e.g., an NL query. Generally, an NL query may include multiple terms and/or phrases that make up a complex query, such as a sentence in a human-readable language. Accordingly, to enable a query result to be generated, the NL query is parsed into multiple logical sections and query contexts are determined for the logical sections. A set of search phases is generated based on the logical sections and the query contexts. The search phases can then be executed in a specific execution order to generate a query result for the NL query.

Abstract: Some implementations generate logical queries from a canonical query, where the logical queries each reflect a modified scope of the canonical query. Implementations receive, via a personalized analytics system, a canonical query that is associated with a user. The canonical query can be analyzed to determine an intent of the canonical query. In turn, one or more implementations generate, based on the intent an anecdotal information associated with the user, a logical query that reflects a modified scope of the canonical query. The logical query can be used to extract data from a database associated with the personalized analytics system based on the modified scope.