Implementations described herein relate to systems and methods to provide platform agnostic query acceleration. In some implementations, a method includes receiving, at a processor associated with a query acceleration service, a request from an client/application, wherein the request conforms to a particular wire protocol of a plurality of supported wire protocols, and wherein the request includes header data and body content data, analyzing the request to identify at least one of a query and a command in the body content data, determining an optimal matched model of the one or more query acceleration models, rewriting the query based on the optimal matched model, transmitting the rewritten query to the query processing platform, receiving a response to the rewritten query or the query from the query processing platform, and transmitting the received response to the application, wherein the transmission is configured based on the particular wire protocol.

According to one embodiment, a method that supports queries deploying operators based on multiple programming languages is described. A sequence of operators associated with a query is identified, where the sequence of operators includes at least two neighboring operators including a first operator based on a first programming language and a second operator based on a second programming language that is different from the first programming language. Thereafter, a schema associated with the first operator and a schema associated with the second operator is determined along with the compatibility between the schema of the first operator and the schema of the second operator. A query error message is generated in response to incompatibility between the first operator schema and the second operator schema. Compatibility is determined when an output generated by execution of the first operator provides machine data needed as input for execution of the second operator.

The present disclosure provides a generalization processing method, apparatus, device and computer storage medium, and relates to technical field of artificial intelligence and specifically to a deep learning technique. A specific implementation solution is: determining a set of candidate queries in a query library that are similar to a requested query in at least one of a literal matching manner, a semantic matching manner and a query rewriting manner; determining a generalized query corresponding to the requested query from the set of candidate queries by using a pre-trained query matching model; wherein the query matching model is obtained by pre-training based on a cross attention model. The generalization for the requested query can be achieved according to the present disclosure.

A method executes at a computer system to retrieve data from a database. Upon receiving a database query, the computer system translates the query into an intermediate representation, and estimates a compilation time to compile the intermediate representation into machine executable code. The query execution time to retrieve a result set is also estimated. In accordance with a determination that the query execution time and compilation time satisfy an interpretation criterion, the computer system invokes a byte code interpreter to interpret the intermediate representation and retrieve the result set from the database. In accordance with a determination that the query execution and compilation times satisfy one of a plurality of compilation criteria, the computer system compiles the intermediate representation to form machine code and executes the machine code to retrieve the result set from the database. In some cases, the query intermediate representation is optimized prior to compilation.

Described herein is a technique to facilitate conjunctive filtering for an embedding-based information retrieval system. Each item in a content collection is initially analyzed to identify its property values for some set of properties. For each item, its property values are encoded in the form of a vector, and concatenated with an item embedding for the item to derive an enhanced item embedding. When a query is received, a query embedding is generated. Any filtering parameters received with or as part of the query are encoded in the form of a vector, and the vector is concatenated with the query embedding to derive an enhanced query embedding. The enhanced query embedding is then used in a “k” approximate nearest neighbor search to identify items relevant to the query and having property values that satisfy the filtering parameters.

Top frequency worksheet filtering including sending a first request comprising a first search term; receiving, in response to the first request, a first plurality of distinct values having a greatest frequency in a data set and responsive to the first search term, wherein the first plurality of distinct values comprises a number of distinct values less than or equal to a size threshold; presenting the first plurality of distinct values; receiving a selection of a first value from the first plurality of distinct values; and filtering, based on the selected first value, a worksheet comprising a presentation of the data set.

A method includes receiving a plurality of product query arrays each including a plurality of individual product queries received during a single user search session. The method further includes inputting the plurality of product query arrays into the query rewrite model. Text of each of the plurality of individual product queries in each product query array is treated as a whole token. The method further includes receiving a product query from a user electronic device. The method further includes determining a query rewrite for the product query using the query rewrite model and determining search results for the product query using the query rewrite. The method further includes sending information for presenting the search results on a display of the user electronic device responsive to the product query.

Techniques are disclosed relating to methods that include preprocessing, by a computer system, records of a database to create one or more token sets for a given record. The created token sets may correspond to ones of a plurality of search string functions, and may include token sets that include a plurality of possible substrings located within data strings of a corresponding database record. The methods may further include receiving a query for a search of the database. The query may include at least one of the plurality of search string functions. The method may also include performing the search by traversing, using at least a portion of the records, at least one token set corresponding to the included search string functions, as well as returning results for the search based on the query and the traversing.

Systems are configured for processing user queries to generate search results that are contextually relevant for the user based on customer values associated with a corresponding customer schema associated with the user and that are indexed in a customer value index. When search queries are received, they are processed to identify customer values associated with the user context and to perform an initial search query based on the initial search query terms. The systems also generate additional altered search queries to perform contemporaneously, based on the initial search terms and restructured/reformatted based on the customer values. Resulting supplement search results are obtained for the additional altered search queries, which are merged with the initial search results. The merged results are then ranked and provided to the user. These systems facilitate obtaining search results that are more relevant than results obtained by conventional systems.

In some embodiments, a data server generates an open data reference specifying a location for storing report data associated with a report. To generate the report data, the data server generates a data query based on a report definition that describes how the report data associated with the report is generated. The data server executes the data query on source data to obtain the report data and causes the report data to be stored on a storage device at the location specified by the open data reference. The report data can be retrieved via a network using the open data reference for integration within a third-party application. The data server further generates updated report data by executing, on the source data, an updated data query generated based on the report definition and has the report data stored on the storage device to be replaced with updated report data.