Search results
Results from the WOW.Com Content Network
A variation of the quantized bucket queue data structure, the calendar queue, has been applied to scheduling of discrete-event simulations, where the elements in the queue are future events prioritized by the time within the simulation that the events should happen. In this application, the ordering of events is critical, so the priorities ...
A queue or queueing node can be thought of as nearly a black box. Jobs (also called customers or requests, depending on the field) arrive to the queue, possibly wait some time, take some time being processed, and then depart from the queue. A black box. Jobs arrive to, and depart from, the queue.
Such simulators require a good and efficient data structure as time spent on queue management can be significant. The calendar queue (with optimum bucket size) can approach O(1) average performance. Calendar queues are closely related to bucket queues but differ from them in how they are searched and in being dynamically resized.
Queues are common in computer programs, where they are implemented as data structures coupled with access routines, as an abstract data structure or in object-oriented languages as classes. A queue has two ends, the top, which is the only position at which the push operation may occur, and the bottom, which is the only position at which the pop ...
Representation of a FIFO queue with enqueue and dequeue operations. Depending on the application, a FIFO could be implemented as a hardware shift register, or using different memory structures, typically a circular buffer or a kind of list. For information on the abstract data structure, see Queue (data structure).
Here are time complexities [5] of various heap data structures. The abbreviation am. indicates that the given complexity is amortized, otherwise it is a worst-case complexity. For the meaning of "O(f)" and "Θ(f)" see Big O notation. Names of operations assume a min-heap.
The binary search tree may be any balanced binary search tree data structure, such as a red–black tree; all that is required is that insertions, deletions, and searches take logarithmic time. Similarly, the priority queue may be a binary heap or any other logarithmic-time priority queue; more sophisticated priority queues such as a Fibonacci ...
This makes the min-max heap a very useful data structure to implement a double-ended priority queue. Like binary min-heaps and max-heaps, min-max heaps support logarithmic insertion and deletion and can be built in linear time. [3] Min-max heaps are often represented implicitly in an array; [4] hence it's referred to as an implicit data structure.