Priority queues in data structures are an important form of ADTs (Abstract Data Types). Each element is allotted a priority, which serves as a characteristic for defining and arranging them.
ADTS is a part of the data science domain, wherein data structures are used as patterns of arrangement for storing information and managing operations like access, addition, search, and modification of data values. The methodologies used for this arrangement of data direct the way they are organised. Data structures also determine the direction of the data flow and the relationships shared within the elements of the system.
Experts have estimated that by the year 2025, the total global data could surpass 175 zettabytes. To manage such large amounts of data, data structures are utilized to handle large databases and indexing purposes efficiently. Various kinds of data structures such as stacks, queues, arrays, heaps, etc., are used during programming stages. Stacks and queues are a linear form of data structures, as the data is stored sequentially, one after the other. They do not have branches, and each element/data value has to be arranged in a straight line.
Arrangement of Stacks and Queues
A stack follows a LIFO (Last In First Out) approach for the arrangement of storage, whereas a queue follows a FIFO (First In First Out) arrangement. This is an important factor for differentiating between these two linear data structures. Their applications are decided based on their LIFO/FIFO approach, as they depend on their unique computational usage.
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For a queue, FIFO establishes that when multiple items are added to the system, the first added item would be the first one to be accessed/removed.
5 Basic Operations that can be Performed on a Queue
1. Enqueue: This operation is performed when we want to add an element to the queue.
2. Dequeue: This operator is used to remove an element from the queue.
3. IsEmpty: This operation is used to check whether the queue is empty, and no further dequeue is possible.
4. IsFull: This operator checks if the queue is full and cannot handle any further enqueue additions.
5. Peek: Peek operator simply recalls/displays the expected data value/element from the queue without removing it from its allocated sequence.
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Priority Queue in Data Structure
Priority queues have an additional priority associated with each of their elements. They do not follow FIFO approaches like traditional queues. Instead, a priority queue in data structure is arranged so that ‘high-priority’ elements are served before their ‘low-priority’ counterparts.
The value of the element is often taken into consideration while allocating the priority value to it. The priority queue differs from a traditional queue in a manner where the highest-priority element would be retrieved first when we try to remove the next element from the queue.
Another prerequisite of priority queues is that the data entered in these queues must be sequentially ordered. This means that the individual data elements must be comparable to one another in a way that their arrangement can be sequenced as lower to greater or greater to lower. This is necessary to allocate the elements of the queue with the relative priorities, based on comparison with each other.
The applications of the priority queue in data structure usually involve their combination with other unordered data structures such as heaps, arrays, linked lists, or BSTs. Heaps provide the most efficient form of combination due to the provision for implementing priority queues effectively.
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Operations Supported in a Priority Queue
Operations in a priority queue help to process the information entered, removed, viewed, and modified. These operations are also useful for traversing between the elements of the queue. They are as follows:
1. Is_empty: is_empty operation checks if the queue holds any element at the moment.
2. Insert_with_priority: This operation adds an element to the queue, along with the priority value that needs to be associated with it.
3. Pull_highest_priority_element: This operation removes the highest priority element from the queue while returning the value of that element.
4. Peek: Peek operation is used to ‘find-max,’ or ‘find-min,’ depending on the expected outcomes. This operation does not remove the max/min element and only returns it.
The Benefit of Using Heaps for Priority Queue in Data Structure
O(log n) performance is observed for inserts and removals when priority queues are based on a heap. This improves performance, and the O(n) function is built from an ‘n’ set of elements. Pairing heaps and Fibonacci heaps provide better bounds for priority queue operations.
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Priority Queue and Sorting Elements
Factoring in the computational complexity, priority queues correspond to the sorting algorithms due to their inherent property. For example, we must collect all the elements that require sorting, and then we should insert them in a priority queue.
Then, if we remove the elements sequentially, the outcome would be a sorted order of elements. Heapsort, Smoothsort, Selection Sort, Insertion Sort, and Tree sort are the names of some of the sorting algorithms that share an equivalent correlation to the priority queue in data structures.
Applications of Priority Queues
The priority queues in the data structure are usually implemented in combination with Heap data structures. They are used in simulations for sequencing, sorting, and keeping track of unexplored routes. The two types of priority queues: Ascending and Descending, have their own set of utilizations. Some of these applications are:
- Bandwidth Management
- Discrete Event Simulation
- Dijkstra’s Algorithm
- Huffman Coding
- Best-first Search Algorithm
- ROAM Triangulation Algorithm
- Prim’s Algorithm for minimum spanning tree
As of today, around 5 billion consumers are directly and indirectly connected to the data. By 2025, more than 6 billion people would be connected to big data. IDC predicts a 10 fold increase for data and projects high demands for data scientists. The Priority Queue in data structure is a significant concept for programmers and data scientists due to their close correlation and application with heap data structures.
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