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The first phase, Analysis, computes all the necessary information from the logfile. The Redo phase restores the database to the exact state at the crash, including all the changes of uncommitted transactions that were running at that point in time. The Undo phase then undoes all uncommitted changes, leaving the database in a consistent state.
Database normalization is the process of structuring a relational database in accordance with a series of so-called normal forms in order to reduce data redundancy and improve data integrity. It was first proposed by British computer scientist Edgar F. Codd as part of his relational model .
In computer science, in the field of databases, read–write conflict, also known as unrepeatable reads, is a computational anomaly associated with interleaved execution of transactions. Specifically, a read–write conflict occurs when a "transaction requests to read an entity for which an unclosed transaction has already made a write request."
Formally, a "database" refers to a set of related data accessed through the use of a "database management system" (DBMS), which is an integrated set of computer software that allows users to interact with one or more databases and provides access to all of the data contained in the database (although restrictions may exist that limit access to particular data).
Codd's steps for organizing database tables and their keys is called database normalization, which avoids certain hidden database design errors (delete anomalies or update anomalies). In real life the process of database normalization ends up breaking tables into a larger number of smaller tables.
In computer science, in the field of databases, write–write conflict, also known as overwriting uncommitted data is a computational anomaly associated with interleaved execution of transactions. Specifically, a write–write conflict occurs when "transaction requests to write an entity for which an unclosed transaction has already made a ...
Isolation is typically enforced at the database level. However, various client-side systems can also be used. It can be controlled in application frameworks or runtime containers such as J2EE Entity Beans [2] On older systems, it may be implemented systemically (by the application developers), for example through the use of temporary tables.
In the context of information retrieval, QBE has a somewhat different meaning. The user can submit a document, or several documents, and ask for "similar" documents to be retrieved from a document database [see search by multiple examples [2]]. Similarity search is based comparing document vectors (see Vector Space Model).