Aspects of the present disclosure disclose techniques for data retrieval. The method includes receiving, from a client device, a request defining an original structured query language (SQL) query; processing the SQL query to identify a set of search engine clauses included in the original SQL query; converting the set of search engine clauses into a search engine query; communicating the search engine query to a search engine for execution; receiving object identifiers for one or more objects that match the search engine query; generating a database query based on the received object identifiers; executing the database query; and receiving and returning results of the executed database query to the client device.

A method for determining, from a document, an answer to a query using a query answering system, comprising: (i) encoding, using an encoder, one or more documents; (ii) encoding a received query; (iii) generating, using an attention mechanism, a query-aware document representation comprising alignment between one or more words in one of the plurality of documents and one or more words in the query; (iv) generating, using a hierarchical self-attention mechanism, a word-to-sentence alignment of the query-aware document representation; (v) labeling, using a conditional random field classifier, each of a plurality of words in the word-to-sentence alignment with one of a one of a plurality of different sequence identifiers, resulting in possible labeled answering spans; and (vi) generating, from the one or more possible labeled answering spans, a response to the query.

To execute function-step-based graph queries on a graph engine that has its own graph query language, rather than re-implementing an existing infrastructure to support function-step-based graph protocols, function-step-based graph queries are transformed to the graph query language that is understood by the graph engine. The existing infrastructure computes the results of the transformed queries. Result sets are then transformed to function-based-based result sets, which are returned to customers. In this manner, the graph engine supports function-step-based graph query workloads without implementation of the function-step-based graph protocol.

The present disclosure relates to computer-implemented methods, software, and systems for exchanging metadata between applications. One example method includes providing a metadata service defining a service metadata model for exposing metadata of data objects defined at one or more applications. A connection to a data source associated with a first application is established from a separate application to acquire the metadata from the data source for one or more data objects related to the first application. In response to establishing the connection, a view of content from the data source is provided that includes a list of entities from the service metadata model. A query defined based on the list of entities is received for acquiring metadata associated with the one or more identified entities. The query is evaluated at a backend of the first application and the metadata for the identified entities from the data source is provided.

A method, system, and computer program product for improving the possibilities to retrieve data from a database, includes receiving instructions from a user interface and providing data to the user interface, wherein the database contains a plurality of datasets and a plurality of relationships of the datasets. The database provides a search interface based on graph query language. Data is exchanged by the search interface based on the graph query language. Instructions entered in the user interface being at least partially not in graph query language are processed and/or data retrieved from the database being in graph query language are processed.

Systems and methods are described for mitigating errors introduced during processing of user input such as voice input. A query may be derived from processed user input. A performance predictor analyzes the query and uses historical data to predict whether the query will return relevant results if executed. If the query's predicted performance is below a threshold, a query rewriter may identify potential alternatives to the query from a library of “known good” queries. Different analyzers may be applied to identify different sets of alternatives, and machine learning models may be applied to rank the outputs of the analyzers. The best-matching alternatives from each analyzer may then be provided as inputs to a further machine learning model, which assesses the probability that each of the identified alternatives reflects the intent of the user. A most likely alternative may then be selected to execute in place of the original query.

Techniques are disclosed for classifying a user search query for a database system. In disclosed techniques, a computing system receives a user search query for the database system and determines, based on the user search query, a database query that is compatible with an interface of the database system to implement the user search query. In some embodiments, the computing system causes a query remediation interface to be presented to a user that entered the user search query. In some embodiments, the interface includes: a classification of the user search query that specifies attributes of the database query, and one or more elements selectable to alter the database query. The computing system receives, from the user via the query remediation interface, input for altering the database query and determines an updated database query based on the input. The computing system may access the database system using the updated database query.

In a pipeline of operations having a terminating operation and a source operation, a builder is built corresponding to the terminating operation. The builder may also correspond to one or more intermediate operations. A database query is generated corresponding to the builder and is sent to a database or a data source for efficient access to the database.

Methods and systems disclosed herein may optimize write operations in a transaction to reduce the number of operations to the point where each of the write operations may be performed in parallel. The writer optimizer may review a first write operation and a second write operation to determine whether the first write operation and the second write operation write to the same row in the same table. When the first write operation and the second write operation write to the same row in the same table, the first write operation and the second write operation may be combined to generate a functionally-equivalent third write operation. The third write operation may reduce the number of operations in the transaction by replacing both the first write operation and the second write operation.

Exemplary methods, apparatuses, and systems include a file system process inserting a first key/value pair and a second key/value pair into a first tree. The second key is a duplicate of the first key and the value of the second key/value pair is an operation changing the value. In response to a request for a range of key/value pairs, the process reads the second key/value pair and inserts it in a second tree. The process reads the first pair and determines, while inserting the first pair in the second tree, that the second key is a duplicate of the first key. The file system process determines an updated value of the first value by applying the operation in the second value to first value. The file system operation updates the second key/value pair in the second tree with the updated value and returns the requested range of key/value pairs.