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Data abstraction enforces a clear separation between the abstract properties of a data type and the concrete details of its implementation. The abstract properties are those that are visible to client code that makes use of the data type—the interface to the data type—while the concrete implementation is kept entirely private, and indeed ...
The data structure itself is an abstraction because it hides the details of how the data is stored in memory and provides a set of operations or interfaces for working with the data (e.g., push and pop for a stack, insert and delete for a binary search tree).
An abstraction can be seen as a compression process, [6] mapping multiple different pieces of constituent data to a single piece of abstract data; [7] based on similarities in the constituent data, for example, many different physical cats map to the abstraction "CAT".
The basic mechanism of control abstraction is a function or subroutine. Data abstractions include various forms of type polymorphism. More elaborate mechanisms that may combine data and control abstractions include: abstract data types, including classes, polytypism etc. The quest for richer abstractions that allow less duplication in complex ...
A database abstraction layer (DBAL [1] or DAL) is an application programming interface which unifies the communication between a computer application and databases such as SQL Server, IBM Db2, MySQL, PostgreSQL, Oracle or SQLite. Traditionally, all database vendors provide their own interface that is tailored to their products.
Data abstraction is a design pattern in which data are visible only to semantically related functions, to prevent misuse. The success of data abstraction leads to frequent incorporation of data hiding as a design principle in object-oriented and pure functional programming.
The Liskov substitution principle (LSP) is a particular definition of a subtyping relation, called strong behavioral subtyping, that was initially introduced by Barbara Liskov in a 1987 conference keynote address titled Data abstraction and hierarchy.
Data independence is the type of data transparency that matters for a centralized DBMS. [1] It refers to the immunity of user applications to changes made in the definition and organization of data. Application programs should not, ideally, be exposed to details of data representation and storage.