Correlation Database is a computer database that allows users to store, update, and search large data sets for similar or related objects or developments. It is distinct from standard databases, which store single objects such as a record, or structured entities such as customers from a customer repository.
Unlike relational databases, which use relational algebra to compare objects between tables, correlation databases go beyond traditional comparison methods, such as SQL, to compare objects within large data sets. Correlation databases compare several attributes, rather than single ones, which can lead to quicker insights and discoveries.
The data sets held in correlation databases can be very diverse and complex. Examples include: genetic data; customer shopping patterns; stock market movements; security log data; network traffic; and patterns of consumer loyalty or product reviews.
Correlation databases can analyze all data within a single query, seeking multiple implications between variables in the data. Queries are often set up to find combinations not previously searched for, enabling users to discover new patterns in the data.
Because correlation databases analyze many different sets of variables at once, the results are often difficult to interpret. Many correlation databases provide the user with visual aides to help interpret the relationships and correlations between variables.
Correlation databases are used in a variety of industries including advertising, finance, government, healthcare and science. They have applications in marketing, machine learning and artificial intelligence, as well as in the detection of fraud or criminal behavior.
The use of correlation databases has seen a rise in popularity due to their flexibility, the speed with which large data sets can be analyzed, and the ability to automate query processes. They have become increasingly important tools for mining and analyzing big data, uncovering key insights in customer trends and movements.
