Data structure in the context of Bit field


Data structure in the context of Bit field

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⭐ Core Definition: Data structure

In computer science, a data structure is a data organization and storage format that is usually chosen for efficient access to data. 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, i.e., it is an algebraic structure about data.

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👉 Data structure in the context of Bit field

A bit field is a data structure that maps to one or more adjacent bits which have been allocated for specific purposes, so that any single bit or group of bits within the structure can be set or inspected. A bit field is most commonly used to represent integral types of known, fixed bit-width, such as single-bit Booleans.

The meaning of the individual bits within the field is determined by the programmer; for example, the first bit in a bit field (located at the field's base address) is sometimes used to determine the state of a particular attribute associated with the bit field.

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Data structure in the context of Structure

A structure is an arrangement and organization of interrelated elements in a material object or system, or the object or system so organized. Physical structures include artifacts and objects such as buildings and machines and natural objects such as biological organisms, minerals and chemicals. Abstract structures include data structures in computer science and musical form. Types of structure include a hierarchy (a cascade of one-to-many relationships), a network featuring many-to-many links, or a lattice featuring connections between components that are neighbors in space.

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Data structure in the context of Computer science

Computer science is the study of computation, information, and automation. Included broadly in the sciences, computer science spans theoretical disciplines (such as algorithms, theory of computation, and information theory) to applied disciplines (including the design and implementation of hardware and software). An expert in the field is known as a computer scientist.

Algorithms and data structures are central to computer science.The theory of computation concerns abstract models of computation and general classes of problems that can be solved using them. The fields of cryptography and computer security involve studying the means for secure communication and preventing security vulnerabilities. Computer graphics and computational geometry address the generation of images. Programming language theory considers different ways to describe computational processes, and database theory concerns the management of repositories of data. Human–computer interaction investigates the interfaces through which humans and computers interact, and software engineering focuses on the design and principles behind developing software. Areas such as operating systems, networks and embedded systems investigate the principles and design behind complex systems. Computer architecture describes the construction of computer components and computer-operated equipment. Artificial intelligence and machine learning aim to synthesize goal-orientated processes such as problem-solving, decision-making, environmental adaptation, planning and learning found in humans and animals. Within artificial intelligence, computer vision aims to understand and process image and video data, while natural language processing aims to understand and process textual and linguistic data.

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Data structure in the context of Computer scientist

A computer scientist is a scientist who specializes in the academic study of computer science and technology.

Computer scientists typically work on the theoretical side of computation. Although computer scientists can also focus their work and research on specific areas (such as algorithm and data structure development and design, software engineering, information theory, database theory, theoretical computer science, numerical analysis, programming language theory, compiler, computer graphics, computer vision, robotics, computer architecture, operating system), their foundation is the theoretical study of computing from which these other fields derive.

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Data structure in the context of XML

Extensible Markup Language (XML) is a markup language and file format for storing, transmitting, and reconstructing data. It defines a set of rules for encoding documents in a format that is both human-readable and machine-readable. The World Wide Web Consortium's XML 1.0 Specification of 1998 and several other related specifications—all of them free open standards—define XML.

The design goals of XML emphasize simplicity, generality, and usability across the Internet. It is a textual data format with strong support via Unicode for different human languages. Although the design of XML focuses on documents, the language is widely used for the representation of arbitrary data structures, such as those used in web services.

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Data structure in the context of Crossover (genetic algorithm)

Crossover in evolutionary algorithms and evolutionary computation, also called recombination, is a genetic operator used to combine the genetic information of two parents to generate new offspring. It is one way to stochastically generate new solutions from an existing population, and is analogous to the crossover that happens during sexual reproduction in biology. New solutions can also be generated by cloning an existing solution, which is analogous to asexual reproduction. Newly generated solutions may be mutated before being added to the population. The aim of recombination is to transfer good characteristics from two different parents to one child.

Different algorithms in evolutionary computation may use different data structures to store genetic information, and each genetic representation can be recombined with different crossover operators. Typical data structures that can be recombined with crossover are bit arrays, vectors of real numbers, or trees.

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Data structure in the context of Hash table

In computer science, a hash table is a data structure that implements an associative array, also called a dictionary or simply map; an associative array is an abstract data type that maps keys to values. A hash table uses a hash function to compute an index, also called a hash code, into an array of buckets or slots, from which the desired value can be found. During lookup, the key is hashed and the resulting hash indicates where the corresponding value is stored. A map implemented by a hash table is called a hash map.

Most hash table designs employ an imperfect hash function. Hash collisions, where the hash function generates the same index for more than one key, therefore typically must be accommodated in some way. Common strategies to handle hash collisions include chaining, which stores multiple elements in the same slot using linked lists, and open addressing, which searches for the next available slot according to a probing sequence.

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Data structure in the context of Array data structure

In computer science, an array is a data structure consisting of a collection of elements (values or variables), of same memory size, each identified by at least one array index or key, a collection of which may be a tuple, known as an index tuple. In general, an array is a mutable and linear collection of elements with the same data type. An array is stored such that the position (memory address) of each element can be computed from its index tuple by a mathematical formula. The simplest type of data structure is a linear array, also called a one-dimensional array.

For example, an array of ten 32-bit (4-byte) integer variables, with indices 0 through 9, may be stored as ten words at memory addresses 2000, 2004, 2008, ..., 2036, (in hexadecimal: 0x7D0, 0x7D4, 0x7D8, ..., 0x7F4) so that the element with index i has the address 2000 + (i × 4).The memory address of the first element of an array is called first address, foundation address, or base address.

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Data structure in the context of Frame (artificial intelligence)

Frames are an artificial intelligence data structure used to divide knowledge into substructures by representing "stereotyped situations".

They were proposed by Marvin Minsky in his 1974 article "A Framework for Representing Knowledge". Frames are the primary data structure used in artificial intelligence frame languages; they are stored as ontologies of sets.

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Data structure in the context of Algorithmic information theory

Algorithmic information theory (AIT) is a branch of theoretical computer science that concerns itself with the relationship between computation and information of computably generated objects (as opposed to stochastically generated), such as strings or any other data structure. In other words, it is shown within algorithmic information theory that computational incompressibility "mimics" (except for a constant that only depends on the chosen universal programming language) the relations or inequalities found in information theory. According to Gregory Chaitin, it is "the result of putting Shannon's information theory and Turing's computability theory into a cocktail shaker and shaking vigorously."

Besides the formalization of a universal measure for irreducible information content of computably generated objects, some main achievements of AIT were to show that: in fact algorithmic complexity follows (in the self-delimited case) the same inequalities (except for a constant) that entropy does, as in classical information theory; randomness is incompressibility; and, within the realm of randomly generated software, the probability of occurrence of any data structure is of the order of the shortest program that generates it when running on a universal machine.

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Data structure in the context of Record (computer science)

In computer science, a record (also called a structure, struct, user-defined type (UDT), or compound data type) is a composite data structure – a collection of fields, possibly of different data types, typically fixed in number and sequence.

For example, a date could be stored as a record containing a numeric year field, a month field represented as a string, and a numeric day-of-month field. A circle record might contain a numeric radius and a center that is a point record containing x and y coordinates.

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Data structure in the context of Intermediate representation

An intermediate representation (IR) is the data structure or code used internally by a compiler or virtual machine to represent source code. An IR is designed to be conducive to further processing, such as optimization and translation. A "good" IR must be accurate – capable of representing the source code without loss of information – and independent of any particular source or target language. An IR may take one of several forms: an in-memory data structure, or a special tuple- or stack-based code readable by the program. In the latter case it is also called an intermediate language.

A canonical example is found in most modern compilers. For example, the CPython interpreter transforms the linear human-readable text representing a program into an intermediate graph structure that allows flow analysis and re-arrangement before execution. Use of an intermediate representation such as this allows compiler systems like the GNU Compiler Collection and LLVM to be used by many different source languages to generate code for many different target architectures.

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Data structure in the context of Voxel

In computing, a voxel is a representation of a value on a three-dimensional regular grid, akin to the two-dimensional pixel. Voxels are frequently used in the visualization and analysis of medical and scientific data (e.g. geographic information systems (GIS)). Voxels also have technical and artistic applications in video games, largely originating with surface rendering in Outcast (1999). Minecraft (2011) makes use of an entirely voxelated world to allow for a fully destructible and constructable environment. Voxel art, of the sort used in Minecraft and elsewhere, is a style and format of 3D art analogous to pixel art.

As with pixels in a 2D bitmap, voxels themselves do not typically have their position (i.e. coordinates) explicitly encoded with their values. Instead, rendering systems infer the position of a voxel based upon its position relative to other voxels (i.e., its position in the data structure that makes up a single volumetric image). Some volumetric displays use voxels to describe their resolution. For example, a cubic volumetric display might be able to show 512×512×512 (or about 134 million) voxels.

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Data structure in the context of Abstract data type

In computer science, an abstract data type (ADT) is a mathematical model for data types, 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 mathematical model contrasts with data structures, which are concrete representations of data, and are the point of view of an implementer, not a user. For example, a stack has push/pop operations that follow a Last-In-First-Out rule, and can be concretely implemented using either a list or an array. Another example is a set which stores values, without any particular order, and no repeated values. Values themselves are not retrieved from sets; rather, one tests a value for membership to obtain a Boolean "in" or "not in".

ADTs are a theoretical concept, used in formal semantics and program verification and, less strictly, in the design and analysis of algorithms, data structures, and software systems. Most mainstream computer languages do not directly support formally specifying ADTs. However, various language features correspond to certain aspects of implementing ADTs, and are easily confused with ADTs proper; these include abstract types, opaque data types, protocols, and design by contract. For example, in modular programming, the module declares procedures that correspond to the ADT operations, often with comments that describe the constraints. This information hiding strategy allows the implementation of the module to be changed without disturbing the client programs, but the module only informally defines an ADT. The notion of abstract data types is related to the concept of data abstraction, important in object-oriented programming and design by contract methodologies for software engineering.

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Data structure in the context of Node (computer science)

A node is a basic unit of a data structure, such as a linked list or tree data structure. Nodes contain data and also may link to other nodes. Links between nodes are often implemented by pointers.

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