Table of Contents
In this article, we have comprehensively defined data structure, what are and why are data structures important, basic and common types of data structure, and their features, and uses.
What is Data Structure?
Data structures are used to receive data by the computer to implement and execute algorithms. But algorithms are instructions that are applied to data. It is a good program that should have a proper data structure and use more optimal algorithms.
For example, suppose you want to write a program that takes 50 integers from the input and sorts them, and displays them in the output. In this example, the best data structure we can use is an array (because the number of input numbers is fixed and defined).
But to sort these numbers, we can use different algorithms such as bubble sort, insertion sort, quick sort, selection sort, or other sorting algorithms. Therefore, programmers must have sufficient mastery of various types of data structures.
Types of Data Structure
Following are the major types of data structure:
Primitive Data Types
A primitive data type is the basic type of data structure. The common primitive data types are as follows:
Boolean: This type of data can only take two values, true and false. This type of data is usually used in conditions and loops.
Integer: A variable of the Integer data type is a variable that is used to store integers that do not have decimals. The Integer data type can store a different range of numbers with different sizes. For example, a signed 8-bit integer holds values between -128 and 127, and an unsigned 32-bit integer. (unsigned long 32-bit integer) holds values between 0 and 4,294,967,295
Floating-Point Numbers: Formulaic representation stores real numbers in the computer.
Fixed-Point Numbers: It is used in some programming languages and stores a real number in such a way that it stores its integer value and its decimal value separately.
Character: This type of data can store different characters in itself. This type of data is usually defined by the word char in programming languages and can store characters such as ‘A’, ‘b’, ” (space), ‘%’, or any other character.
String: This data can store a string of characters in itself. For example, this data model can store the strings “Data Structure” or “13579” in itself.
If we put these basic data types together in different forms, more complex data structures such as arrays are formed. For example, if we simply put 10 integers together, a block or an organization of data is formed, which is called an array.
Common Data Structures
There are different types of data structure (the same data structure) that are generally built on simpler primitive data types
- Linked List
- Stack or Push Down List or Pile
- Simple trees (Binary Tree)
- Binary Search Tree
An array is the most common data storage structure and most programming languages use it. Likewise, some of the most important data structures such as lists, stacks, and queues can be designed and implemented with arrays. Also, the array is used in designing many algorithms and solving many problems as a Lookup Table.
An array is a collection of data that has the following characteristics:
- All of the same data type
- Be placed in contiguous memory houses
- All these data elements can be directly accessed and addressed using the address of the first element and the index of the desired element.
The linked list is a data structure that consists of a number of nodes. Each node contains a record containing data and the address of the next node, which is called a node that points to the next node. In the linked list, each node can be at any address from memory.
The stack data structure is a dynamic set of data that when removing an element from this set. The last element added to the set is removed from it, called LIFO (Last In First Out) or LCFS (Last Come First Serve) data removal
method. will be Therefore, the stack controls the order in which elements are exited. As a result, to access an element, you must first remove the elements that were added to the stack after that.
A queue data structure is an ordered list with new elements added at the end and removed from the beginning. The beginning of the queue is called the head of the queue. The queue uses the FIFO (First In First Out) algorithm to manage the queue of elements, according to which any element that enters the queue first gets service first.
An operation performed on a queue
- ENQUEUE (Q,X): add element x to the end of the queue Q
- DEQUEUE (Q): Remove an element from the beginning of the Q queue and
return that element as output.
The tree data structure, or tree, in short, represents a hierarchical structure that has found wide applications in computer science. The tree is used to analyze electronic circuits, express relationships, organize data in databases and express grammatical structures in compilers.
A tree is a collection of elements called nodes, one of these nodes is located at the root of the tree and other nodes are located below the root. A node can contain any data including numbers, letters, strings of letters, etc.
Binary Search Tree or BST
A binary search tree is a binary tree that can be empty, and if it is not empty, it must have the following conditions.
- Each node must have exactly one key.
- The key of all nodes under the tree on the left side of each node must be less than or equal to the key of that node.
- The key of all nodes under the tree on the right side of each node must be greater than the key of that element.
All algorithms of searching, inserting, deleting, finding the minimum, finding the maximum, and finding the next and previous element of a specific element is all of the height order of the BST tree.
Heap Tree or Pyramid
MaxHeap or Binary MaxHeap is a complete tree in which there is one number inside each node, and the larger number is equal to its children.
Features Of Data Structure
Data structures are often classified based on their characteristics. For example, to answer the question of what is the best feature of the data structure, these three features can be considered:
Linear or Non-Linear: This characteristic describes whether the data items are properly ordered or not; as an array or in an unordered sequence, as in a graph.
Homogeneous or Heterogeneous: This attribute describes whether all data elements in a given repository are of the same type. An example is a set of elements in an array. Like an abstract data type defined as a structure in C or a class specification in Java.
Static or Dynamic: This specification describes how data structures are compiled. Static data structures have fixed sizes, structures, and memory locations at compile time, while dynamic data structures have sizes, structures, and memory locations that can be scaled up or down depending on the application.
Applications of Data Structure
- Using tree and graph search in routing algorithm for implementation in artificial intelligence applications.
- Using spanning trees to make routing decisions in computer networks.
- Code generation, such as Huffman code using trees.
- Implementation of hierarchical structures in computer systems, such as directories.
- Managing the collection of records in the database.
- A polynomial implementation for arithmetic operations and storing matrices with arrays.
- Use the linked list when the user uses the alt+tab key combination to view the opened programs to select the desired program.
- Using a double link list to implement forward and back buttons in the browser to move back and forth in the pages of a website.
Important Uses of Data Structures
Data Storage: Use data structures to efficiently persist data, such as specifying a set of attributes and related structures used to store records in a database management system.
Management of Resources and Services: Operating system (OS) resources and services are enabled by using data structures, such as linked lists for memory allocation, file list management, and file structure trees, as well as process scheduling queues.
Indexing: More complex data structures such as B-trees are used to index objects, such as those stored in a database.
Scalability of Big Data Applications: Data structures are used to allocate and manage data storage in distributed storage locations that ensure scalability and performance. Some big data programming environments, such as Apache Spark, provide data structures that reflect the underlying structure of database records to simplify querying.
Main Operations in Data Structure
- Navigation: Accessing the information of a record exactly once is called navigation so that some fields of the record can be processed.
- Search: The act of finding the location of a record with a certain key value or finding the location of all records that apply to one or more conditions is called search.
- Adding: adding a new record to the data structure.
- Delete: Delete a record from the data structure.
Importance of Data Structure
Knowledge of basic data types, such as integers or decimals, which are available in most computer programming languages, is generally not sufficient on their own to process and use data. However, programs that receive, manipulate, and generate data must understand how the data should be organized to simplify processing.
Data structures group data elements together in a logical way and facilitate the effective use, persistence, and sharing of data. They provide a model that describes how data elements are organized.
Not only is it important to use data structures, but it is also important to choose the right data structure for each task. Choosing an inappropriate data structure can slow down the execution time or the responsiveness of the code. Here are five factors to consider when choosing a data structure:
- What kind of information will be stored? It means that the information that we intend to store is of what type, is it an integer or rational number between 0 and 1 or…?
- How will that information be used?
- Where should data be stored after creation, in RAM or on disk or…?
- What is the best way to organize data? Which data structure is the best option for organizing data according to our goals and limitations, such as time order in search or in implementation, etc.?
- What aspects of storage and memory management should be considered? For example, is it more useful to use memory with FIFO management or LIFO?
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