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Microsoft Query is a visual method of creating database queries using examples based on a text string, the name of a document or a list of documents. The QBE system converts the user input into a formal database query using Structured Query Language (SQL) on the backend, allowing the user to perform powerful searches without having to explicitly compose them in SQL, and without even needing to ...
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Query by Example (QBE) is a database query language for relational databases. It was devised by Moshé M. Zloof at IBM Research during the mid-1970s, in parallel to the development of SQL . [ 1 ] It is the first graphical query language, using visual tables where the user would enter commands, example elements and conditions.
In order to retrieve the desired data the user presents a set of criteria by a query. Then the database management system selects the demanded data from the database. The retrieved data may be stored in a file, printed, or viewed on the screen. A query language, like for example Structured Query Language (SQL), is
Well-formed output language code fragments Any programming language (proven for C, C++, Java, C#, PHP, COBOL) gSOAP: C / C++ WSDL specifications C / C++ code that can be used to communicate with WebServices. XML with the definitions obtained. Microsoft Visual Studio LightSwitch: C# / VB.NET Active Tier Database schema
Quick Object's designer added support for adding multiple custom templates for code generation. V4.0 also added support for LINQ and enabled the capability to specify queries in LINQ and run them against any of the supported databases. On April 7, 2008, Version 3.5 was released with a new free edition of the product called Community Edition.
Retrieval-Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.
Cubes contains a SQL query generator that translates the reporting queries into SQL statements. The query generator takes into account topology of the star or snowflake schema and executes only joins that are necessary to retrieve attributes required by the data analyst. The SQL backend uses SQLAlchemy Python toolkit to construct the queries.