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In computing, the network model is a database model conceived as a flexible way of representing objects and their relationships. Its distinguishing feature is that the schema , viewed as a graph in which object types are nodes and relationship types are arcs, is not restricted to being a hierarchy or lattice .
Those users who stayed with IDMS were primarily interested in its high performance, not in its relational capabilities. It was widely recognized (helped by a high-profile campaign by E. F. Codd, the father of the relational model) that there was a significant difference between a relational database and a network database with a relational veneer.
A database model is a type of data model that determines the logical structure of a database. It fundamentally determines in which manner data can be stored, organized and manipulated. The most popular example of a database model is the relational model, which uses a table-based format.
A federated database system (FDBS) is a type of meta-database management system (DBMS), which transparently maps multiple autonomous database systems into a single federated database. The constituent databases are interconnected via a computer network and may be geographically decentralized. Since the constituent database systems remain ...
An example is a "customer" record that has links to that customer's "orders", which in turn link to "line_items". The hierarchical database model mandates that each child record has only one parent, whereas each parent record can have zero or more child records. The network model extends the hierarchical by allowing multiple parents and ...
A graph database is a database that is based on graph theory. It consists of a set of objects, which can be a node or an edge. It consists of a set of objects, which can be a node or an edge. Nodes represent entities or instances such as people, businesses, accounts, or any other item to be tracked.
Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. [ 1 ]
The key idea of Apache Ignite Machine Learning toolkit is an ability to perform distributed training and inference instantly without massive data transmissions. It's based on MapReduce approach, resilient to node failures and data rebalances, allows to avoid data transfers and so that speed up preprocessing and model training. [26]