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Query Processing

The theory of query processing in data desegregation systems is commonly verbalized using conjunctive queries. One can generally cerebrate of a connective query as a logical purpose practical to the relations of a database much as "f(A,B) where A < B". If a tuple or set of tuples is substituted into the instruction and satisfies it, then we reflect that tuple as line of the set of answers in the query. Patch nominal languages equivalent Datalog get these queries briefly and without ambiguity, common SQL queries count as conjunctive queries as well.

In status of data desegregation, "query containment” represents an important property of conjunctive queries. A query A contains other query B (denoted ) if the results of applying B are a subset of the results of applying A for any database. The two queries are said to be equivalent if the resulting sets are same for any database. This is eminent because in both GAV and LAV systems, a someone poses conjunctive queries over a virtual schema represented by a set of views, or "materialized" conjunctive queries. Compounding seeks to rewrite the queries represented by the views to egest their results equivalent or maximally contained by our user's ask. This corresponds to the difficulty of responsive queries using views.

In GAV systems, a system specializer writes mediator code to describe the query-rewriting. Each element in the individual's query corresponds to a substitution concept virtuous as each element in the global schema corresponds to a query over the inspiration. Query processing only expands the subgoals of the person's query according to the label such in the mediator and thusly the resulting query is liable to be equal. While the designer does the majority of the work beforehand, some GAV systems take simplifying the mediator description process.

In LAV systems, queries undergo a more radical process of rewriting because no mediator exists to aline the some body's query with an ovate expansion strategy. The integration system must execute a search over the area of practical queries in rule to mature the best rewrite. The resulting rewrite may not be an equivalent query but maximally contained, and the resulting tuples may be incomplete.

Fig: Query Processing

Relational database application in data processing is suitable, in part, to the availability of nonprocedural languages, which can significantly alter application process and end-user productivity. By hiding the low-level details about the physical organization of the data, relational database languages allow the expression of composite queries in a compact and apiculate style. In fact, to suppose the response to the query, the user does not exactly expand the process to follow. This process is actually devised by a DBMS module, called as Query Processor. This relieves the someone from query improvement, a time consuming task that is handled right by the query processor.

This issue has important both in Centralized and Distributed processing systems. However, the query processing problem is much more difficult in distributed environments than in the conventional systems.

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