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abstract data type : ウィキペディア英語版
abstract data type

In computer science, an abstract data type (ADT) is a mathematical model for data types where a data type is defined by its behavior (semantics) from the point of view of a ''user'' of the data, specifically in terms of possible values, possible operations on data of this type, and the behavior of these operations. This contrasts with data structures, which are concrete representations of data, and are the point of view of an implementer, not a user.
Formally, an ADT may be defined as a "class of objects whose logical behavior is defined by a set of values and a set of operations"; this is analogous to an algebraic structure in mathematics. What is meant by "behavior" varies by author, with the two main types of formal specifications for behavior being ''axiomatic (algebraic) specification'' and an ''abstract model;'' these correspond to axiomatic semantics and operational semantics of an abstract machine, respectively. Some authors also include the computational complexity ("cost"), both in terms of time (for computing operations) and space (for representing values).
In practice many common data types are not ADTs, as the abstraction is not perfect, and users must be aware of issues like arithmetic overflow that are due to the representation. For example, integer are often implemented as fixed width (32-bit or 64-bit binary numbers), and thus experience integer overflow if the maximum value is exceeded.
ADTs are a theoretical concept in computer science, used in the design and analysis of algorithms, data structures, and software systems, and do not correspond to specific features of computer languages—mainstream computer languages do not directly support formally specified ADTs. However, various language features correspond to certain aspects of ADTs, and are easily confused with ADTs proper; these include abstract types, opaque data types, protocols, and design by contract. ADTs were first proposed by Barbara Liskov and Stephen N. Zilles in 1974, as part of the development of the CLU language.
==Examples==
For example, integers are an ADT, defined as the values …, −2, −1, 0, 1, 2, …, and by the operations of addition, subtraction, multiplication, and division, together with greater than, less than, etc., which behave according to familiar mathematics (with care for integer division), independently of how the integers are represented by the computer. Explicitly, "behavior" includes obeying various axioms (associativity and commutativity of addition etc.), and preconditions on operations (cannot divide by zero). Typically integers are represented in a data structure as binary numbers, most often as two's complement, but might be binary-coded decimal or in ones' complement, but the user is abstracted from the concrete choice of representation, and can simply use the data as integers.
An ADT consists not only of operations, but also of values of the underlying data and of constraints on the operations. An "interface" typically refers only to the operations, and perhaps some of the constraints on the operations, notably pre-conditions and post-conditions, but not other constraints, such as relations between the operations.
For example, an abstract stack, which is a last-in-first-out structure, could be defined by three operations: push, that inserts a data item onto the stack; pop, that removes a data item from it; and peek or top, that accesses a data item on top of the stack without removal. An abstract queue, which is a first-in-first-out structure, would also have three operations: enqueue, that inserts a data item into the queue; dequeue, that removes the first data item from it; and front, that accesses and serves the first data item in the queue. There would be no way of differentiating these two data types, unless a mathematical constraint is introduced that for a stack specifies that each pop always returns the most recently pushed item that has not been popped yet. When analyzing the efficiency of algorithms that use stacks, one may also specify that all operations take the same time no matter how many data items have been pushed into the stack, and that the stack uses a constant amount of storage for each element.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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