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In computer science, a bidirectional map is an associative data structure in which the (,) pairs form a one-to-one correspondence. Thus the binary relation is functional in each direction: each v a l u e {\displaystyle value} can also be mapped to a unique k e y {\displaystyle key} .
A Data Mapper is a Data Access Layer that performs bidirectional transfer of data between a persistent data store (often a relational database) and an in-memory data representation (the domain layer). The goal of the pattern is to keep the in-memory representation and the persistent data store independent of each other and the data mapper itself.
Hibernate ORM (or simply Hibernate) is an object–relational mapping [2]: §1.2.2, [12] tool for the Java programming language. It provides a framework for mapping an object-oriented domain model to a relational database .
In computer science, bidirectionalization refers to the process of given a source-to-view transformation (automatically) finding a mapping from the original source and an updated view to an updated source.
A bidirectional map is a related abstract data type in which the mappings operate in both directions: each value must be associated with a unique key, and a second lookup operation takes a value as an argument and looks up the key associated with that value.
Single table inheritance is a way to emulate object-oriented inheritance in a relational database.When mapping from a database table to an object in an object-oriented language, a field in the database identifies what class in the hierarchy the object belongs to. [1]
NHibernate is a port of the Hibernate object–relational mapping (ORM) tool for the Microsoft .NET platform. It provides a framework for mapping an object-oriented domain model to a traditional relational database. Its purpose is to relieve the developer from a significant portion of relational data persistence-related programming tasks.
One example is the constrained shortest path problem, [16] which attempts to minimize the total cost of the path while at the same time maintaining another metric below a given threshold. This makes the problem NP-complete (such problems are not believed to be efficiently solvable for large sets of data, see P = NP problem ).