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In computer science, a data structure is a particular way of organizing and storing data in a computer so that it can be accessed and modified efficiently.[1][2][3] More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data.[4]

Contents

1 Usage 2 Implementation 3 Examples 4 Language support 5 See also 6 References 7 Bibliography 8 Further reading 9 External links

Usage[edit] Data structures can implement one or more particular abstract data types (ADT), which specify the operations that can be performed on a data structure and the computational complexity of those operations. In comparison, a data structure is a concrete implementation of the space provided by an ADT.[citation needed] Different kinds of data structures are suited to different kinds of applications, and some are highly specialized to specific tasks. For example, relational databases commonly use B-tree
B-tree
indexes for data retrieval,[5] while compiler implementations usually use hash tables to look up identifiers.[citation needed] Data structures provide a means to manage large amounts of data efficiently for uses such as large databases and internet indexing services. Usually, efficient data structures are key to designing efficient algorithms. Some formal design methods and programming languages emphasize data structures, rather than algorithms, as the key organizing factor in software design. Data structures can be used to organize the storage and retrieval of information stored in both main memory and secondary memory.[citation needed] Implementation[edit] Data structures are generally based on the ability of a computer to fetch and store data at any place in its memory, specified by a pointer—a bit string, representing a memory address, that can be itself stored in memory and manipulated by the program. Thus, the array and record data structures are based on computing the addresses of data items with arithmetic operations; while the linked data structures are based on storing addresses of data items within the structure itself. Many data structures use both principles, sometimes combined in non-trivial ways (as in XOR linking).[citation needed] The implementation of a data structure usually requires writing a set of procedures that create and manipulate instances of that structure. The efficiency of a data structure cannot be analyzed separately from those operations. This observation motivates the theoretical concept of an abstract data type, a data structure that is defined indirectly by the operations that may be performed on it, and the mathematical properties of those operations (including their space and time cost).[citation needed] Examples[edit] Main article: List of data structures There are numerous types of data structures, generally built upon simpler primitive data types:[6]

An array is a number of elements in a specific order, typically all of the same type. Elements are accessed using an integer index to specify which element is required (Depending on the language, individual elements may either all be forced to be the same type, or may be of almost any type). Typical implementations allocate contiguous memory words for the elements of arrays (but this is not always a necessity). Arrays may be fixed-length or resizable. A linked list (also just called list) is a linear collection of data elements of any type, called nodes, where each node has itself a value, and points to the next node in the linked list. The principal advantage of a linked list over an array, is that values can always be efficiently inserted and removed without relocating the rest of the list. Certain other operations, such as random access to a certain element, are however slower on lists than on arrays. A record (also called tuple or struct) is an aggregate data structure. A record is a value that contains other values, typically in fixed number and sequence and typically indexed by names. The elements of records are usually called fields or members. A union is a data structure that specifies which of a number of permitted primitive types may be stored in its instances, e.g. float or long integer. Contrast with a record, which could be defined to contain a float and an integer; whereas in a union, there is only one value at a time. Enough space is allocated to contain the widest member datatype. A tagged union (also called variant, variant record, discriminated union, or disjoint union) contains an additional field indicating its current type, for enhanced type safety. A class is a data structure that contains data fields, like a record, as well as various methods which operate on the contents of the record. In the context of object-oriented programming, records are known as plain old data structures to distinguish them from classes.[citation needed]

In addition, graphs and binary trees are other commonly used data structures. Language support[edit] Most assembly languages and some low-level languages, such as BCPL (Basic Combined Programming Language), lack built-in support for data structures. On the other hand, many high-level programming languages and some higher-level assembly languages, such as MASM, have special syntax or other built-in support for certain data structures, such as records and arrays. For example, the C (a direct descendant of BCPL) and Pascal languages support structs and records, respectively, in addition to vectors (one-dimensional arrays) and multi-dimensional arrays.[7][8] Most programming languages feature some sort of library mechanism that allows data structure implementations to be reused by different programs. Modern languages usually come with standard libraries that implement the most common data structures. Examples are the C++ Standard Template
Template
Library, the Java Collections Framework, and the Microsoft
Microsoft
.NET Framework.[citation needed] Modern languages also generally support modular programming, the separation between the interface of a library module and its implementation. Some provide opaque data types that allow clients to hide implementation details. Object-oriented programming
Object-oriented programming
languages, such as C++, Java, and Smalltalk, typically use classes for this purpose. Many known data structures have concurrent versions which allow multiple computing threads to access a single concrete instance of a data structure simultaneously.[citation needed] See also[edit]

Book: Data structures

Abstract data type Concurrent data structure Data model Dynamization Linked data structure List of data structures Persistent data structure Plain old data structure

References[edit]

^ Cormen, Thomas H.; Leiserson, Charles E.; Rivest, Ronald L.; Stein, Clifford (2009). Introduction to Algorithms, Third Edition (3rd ed.). The MIT Press. ISBN 0262033844.  ^ Black (ed.), Paul E. (2004-12-15). Entry for data structure in Dictionary of Algorithms and Data Structures. Online version. U.S. National Institute of Standards and Technology, 15 December 2004. Retrieved on 2009-05-21 from http://xlinux.nist.gov/dads/HTML/datastructur.html. ^ Encyclopædia Britannica
Encyclopædia Britannica
(2009). Entry data structure in the Encyclopædia Britannica
Encyclopædia Britannica
(2009). Retrieved on 2009-05-21 from http://www.britannica.com/EBchecked/topic/152190/data-structure. ^ Wegner, Peter; Reilly, Edwin D. Encyclopedia of Computer Science. Chichester, UK: John Wiley and Sons Ltd. pp. 507–512. ISBN 0470864125.  ^ Gavin Powell (2006). "Chapter 8: Building Fast-Performing Database Models". Beginning Database
Database
Design ISBN 978-0-7645-7490-0. Wrox Publishing.  ^ Seymour,, Lipschutz, (2014). Data structures (Revised First ed.). New Delhi: McGraw Hill Education (India) Private Limited. ISBN 9781259029967. OCLC 927793728.  ^ "The GNU C Manual". Free Software Foundation. Retrieved 2014-10-15.  ^ "Free Pascal: Reference Guide". Free Pascal. Retrieved 2014-10-15. 

Bibliography[edit]

This article lacks ISBNs for the books listed in it. Please make it easier to conduct research by listing ISBNs. If the Cite book or citation templates are in use, you may add ISBNs automatically, or discuss this issue on the talk page. (September 2016)

Peter Brass, Advanced Data Structures, Cambridge University Press, 2008, ISBN 978-0521880374 Donald Knuth, The Art of Computer Programming, vol. 1. Addison-Wesley, 3rd edition, 1997, ISBN 978-0201896831 Dinesh Mehta and Sartaj Sahni, Handbook of Data Structures and Applications, Chapman and Hall/CRC Press, 2007. Niklaus Wirth, Algorithms and Data Structures, Prentice Hall, 1985.

Further reading[edit]

Alfred Aho, John Hopcroft, and Jeffrey Ullman, Data Structures and Algorithms, Addison-Wesley, 1983, ISBN 0-201-00023-7 G. H. Gonnet and R. Baeza-Yates, Handbook of Algorithms and Data Structures - in Pascal and C, second edition, Addison-Wesley, 1991, ISBN 0-201-41607-7 Book Ellis Horowitz
Ellis Horowitz
and Sartaj Sahni, Fundamentals of Data Structures in Pascal, Computer Science Press, 1984, ISBN 0-914894-94-3

External links[edit]

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Descriptions from the Dictionary of Algorithms and Data Structures Data structures course An Examination of Data Structures from .NET perspective Schaffer, C. Data Structures and Algorithm
Algorithm
Analysis

v t e

Data structures

Types

Collection Container

Abstract

Associative array

Multimap

List Stack Queue

Double-ended queue

Priority queue

Double-ended priority queue

Set

Multiset Disjoint-set

Arrays

Bit
Bit
array Circular buffer Dynamic array Hash table Hashed array tree Sparse matrix

Linked

Association list Linked list Skip list Unrolled linked list XOR linked list

Trees

B-tree Binary search tree

AA tree AVL tree Red–black tree Self-balancing tree Splay tree

Heap

Binary heap Binomial heap Fibonacci heap

R-tree

R* tree R+ tree Hilbert R-tree

Trie

Hash tree

Graphs

Binary decision diagram Directed acyclic graph Directed acyclic word graph

List of data structures

v t e

Data types

Uninterpreted

Bit Byte Trit Tryte Word Bit
Bit
array

Numeric

Arbitrary-precision or bignum Complex Decimal Fixed point Floating point

Double precision Extended precision Half precision Long double Minifloat Octuple precision Quadruple precision Single precision

Integer

signedness

Interval Rational

Pointer

Address

physical virtual

Reference

Text

Character String

null-terminated

Composite

Algebraic data type

generalized

Array Associative array Class Dependent Equality Inductive List Object

metaobject

Option type Product Record Set Union

tagged

Other

Boolean Bottom type Collection Enumerated type Exception Function type Opaque data type Recursive data type Semaphore Stream Top type Type class Unit type Void

Related topics

Abstract data type Data structure Generic Kind

metaclass

Parametric polymorphism Primitive data type Protocol

interface

Subtyping Type constructor Type conversion Type system Type theory

See also platform-dependent and independent units of information

v t e

Data model

Main

Architecture Modeling Structure

Schemas

Conceptual Logical Physical

Types

Database Data structure
Data structure
diagram Entity–relationship model
Entity–relationship model
(enhanced) Geographic Generic Semantic

Related models

Data flow diagram Information model Object model Object-role modeling Unified Modeling Language

See also

Database
Database
design Business process modeling Core architecture data model Enterprise modelling Function model Process modeling XML schema Data Format Description Language

Authority control

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