Data structure

(computer science) A collection of data components that are constructed in a regular and characteristic way.

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A means of storing a collection of data. Computer science is in part the study of methods for effectively using a computer to solve problems, or in other words, determining exactly the problem to be solved. This process entails (1) gaining an understanding of the problem; (2) translating vague descriptions, goals, and contradictory requests, and often unstated desires, into a precisely formulated conceptual solution; and (3) implementing the solution with a computer program. This solution typically consists of two parts: algorithms and data structures.

Relation to algorithms

An algorithm is a concise specification of a method for solving a problem. A data structure can be viewed as consisting of a set of algorithms for performing operations on the data it stores. Thus algorithms are part of what constitutes a data structure. In constructing a solution to a problem, a data structure must be chosen that allows the data to be operated upon easily in the manner required by the algorithm.

Data may be arranged and managed at many levels, and the variability in algorithm design generally arises in the manner in which the data for the program are stored, that is (1) how data are arranged in relation to each other, (2) which data are calculated as needed, (3) which data are kept in memory, and (4) which data are kept in files, and the arrangement of the files. An algorithm may need to put new data into an existing collection of data, remove data from a collection, or query a collection of data for a specific purpose. See also Algorithm.

Abstract data types

Each data structure can be developed around the concept of an abstract data type that defines both data organization and data handling operations. Data abstraction is a tool that allows each data structure to be developed in relative isolation from the rest of the solution. The study of data structure is organized around a collection of abstract data types that includes lists, trees, sets, graphs, and dictionaries. See also Abstract data type.

Primitive and nonprimitive structures

Data can be structured at the most primitive level, where they are directly operated upon by machine-level instructions. At this level, data may be character or numeric, and numeric data may consist of integers or real numbers.

Nonprimitive data structures can be classified as arrays, lists, and files. An array is an ordered set which contains a fixed number of objects. No deletions or insertions are performed on arrays. At best, elements may be changed. A list, by contrast, is an ordered set consisting of a variable number of elements to which insertions and deletions can be made, and on which other operations can be performed. When a list displays the relationship of adjacency between elements, it is said to be linear; otherwise it is said to be nonlinear. A file is typically a large list that is stored in the external memory of a computer. Additionally, a file may be used as a repository for list items (records) that are accessed infrequently.

File structures

Not all information that is processed by a computer necessarily resides in immediately accessible memory because some programs and their data cannot fit into the main memory of the computer. Large volumes of data or records and archival data are commonly stored in external memory as entities called files. Any storage other than main memory may be loosely defined as external storage. This includes tapes, disks, and so forth. See also Computer storage technology.

Virtual memory

This is a system that provides an extension to main memory in a logical sense. In a virtual system, all currently active programs and data are allocated space or virtual addresses in virtual memory. The program and data may not in fact reside in main memory but in an external storage. References to virtual addresses are translated dynamically by the operating system into real addresses in main memory. See also Digital computer.

The physical layout of data. Data fields, memo fields, fixed length fields, variable length fields, records, word processing documents, spreadsheets, data files, database files and indexes are all examples of data structures.

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In Geographic Information Systems, a representation of the data model as a diagram, list, or array, showing the human implementation orientation of the data.

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In computer science, a data structure is a particular way of storing and organizing data in a computer so that it can be used efficiently.^[1][2]

Different kinds of data structures are suited to different kinds of applications, and some are highly specialized to specific tasks. For example, B-trees are particularly well-suited for implementation of databases, while compiler implementations usually use hash tables to look up identifiers.

Data structures are used in almost every program or software system. Specific data structures are essential ingredients of many efficient algorithms, and make possible the management of huge amounts of data, such as large databases and internet indexing services. Some formal design methods and programming languages emphasize data structures, rather than algorithms, as the key organizing factor in software design.

Contents 1 Basic principles 2 Abstract data structures 3 Language support 4 See also 5 References 6 External links

Basic principles

Data structures are generally based on the ability of a computer to fetch and store data at any place in its memory, specified by an address — a bit string that can be itself stored in memory and manipulated by the program. Thus the record and array 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).

Abstract data structures

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 moves 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).

Language support

Assembly languages and some low-level languages such as BCPL generally lack support for data structures. Many high-level programming languages, on the other hand, have special syntax or other built-in support for certain data structures, such as vectors (one-dimensional arrays) in the C programming language, multi-dimensional arrays in Pascal, linked lists in Common Lisp, and hash tables in Perl. Many languages also provide basic facilities such as references and the definition record data types, that programmers can use to build arbitrarily complex structures.

Most programming languages feature some sort of library mechanism that allows data structure implementations to be reused by different programs. Modern programming languages usually come with standard libraries that implement the most common data structures. Examples are the C++ Standard Template Library, the Java Collections Framework, and Microsoft's .NET Framework.

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 and pictures. Object-oriented programming languages, such as C++ and Java, use classes for this purpose.

See also

Wikipedia:Books has a book on: Data structures

• Linked data structure
• List of data structures
• Comparison of data structures
• Data model
• Data modeling
• Dynamization
• Persistent data structure

References

1. ^ Paul E. Black (ed.), entry for data structure in Dictionary of Algorithms and Data Structures. U.S. National Institute of Standards and Technology. 15 December 2004. Online version Accessed 2009-05-21.
2. ^ Entry data structure in the Encyclopædia Britannica (2009) Online entry accessed on 2009-05-21.

• Niklaus Wirth, Algorithms and Data Structures
• Donald Knuth, The Art of Computer Programming, vol. 1. Addison-Wesley.
• Bill Viola, Will There Be Condominiums in Data Space?

External links

Wikibooks has a book on the topic of Data Structures

Wikimedia Commons has media related to: Data structures

• Descriptions from the Dictionary of Algorithms and Data Structures
• http://www.cse.unr.edu/~bebis/CS308/
• Data structures course with animations
• data structure tutorials with animations
• An Examination of Data Structures

v • d • e Data structures Types Collection · Container Arrays Associative array · Set · Multiset · Hash table Lists Double-ended queue · Linked list · Queue · Stack · Circular Queue/Buffer Trees B-tree · Binary search tree · Heap Graphs Directed graph · Directed acyclic graph · Binary decision diagram List of data structures

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