Terminology for a priority queue data structure?

Question

I've been using a data structure which the original developer called a heap, it's used to implement a priority queue.

While there is a lot written about binary trees, (min/max) heaps seem less well defined (details vary between implementations).

Some characteristics I've noticed that don't necessarily apply to binary-tree structures.

• The same element can be in the queue multiple times without causing complication in execution or implementation.
• Searching (while possible and faster than an exhaustive search), is not so efficient (since the child elements of each node don't have to be balanced).
• Since searching is not efficient and there is a potential for duplicates, removal may require storing a reference to the node, instead of using a key to look-up the node (which is common practice for binary trees).
• Changing priorities in the heap is trivial, compared to a binary tree where it's most common to delete+insert.
  (with a better best case and a worse worst case compared to binary-trees)

Is there more detailed terminology for data structures that match these characteristics?
Or is it simply a min/max heap which happens to be used as a priority-queue?

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Note, heres a link to a min-heap that has the characteristics described above.

Answer 1

A binary heap is a concrete implementation of the priority queue abstract data structure. It's popular because it's easy to implement, memory efficient, and reasonably fast: O(log n) insertion, and O(log n) removal of the root (smallest in a min heap, largest in a max heap) element. Most implementations also provide a peek method that allows viewing the root element without removing it.

A binary heap doesn't do anything else particularly well. Contrary to your observation, finding a particular item in a binary heap requires a sequential scan. Although the nodes are ordered (not sorted), the order doesn't lend itself well to searching.

Typical implementation of a binary heap is in an array. Due to the shape property (the structure can be viewed as a perfect (or complete) binary tree), which means that the relationship between parent and child is represented implicitly. The items are stored in the array in breadth-first order.

As user templatetypedef pointed out in his answer, a binary heap is a specific kind of binary tree, and shouldn't be confused with a binary search tree, which is specifically designed for quick insertion and deletion of items, and locating items by key.

Although changing the priority of an item in the heap, or removing an arbitrary item from the heap, are very easy, the problem as you pointed out is locating the item to be operated on. In a typical binary heap, finding the item to be modified requires a sequential search. If you need the ability to move items around in the heap, you would typically marry the binary with a dictionary or hash map that's indexed by the item's key. The value is the index of the item in the array. That index is updated every time an item is moved. This slows heap operations by a constant factor, but gives you the ability to locate an item in O(1).

There's also something called a Min-max heap, which is a type of binary heap that gives you O(1) access to both the min and max item. The implementation is very similar to the implementation of a standard binary min heap.

To confuse matters even more, there also exists the d-ary heap, which is a heap that contains more than two children per node. For example, a trinary heap has three children per node. These, too, are implemented in arrays with implicit child pointers.

There are other data structures that are commonly called heaps, but are in fact not really related to the heap at all except that they're different implementations of the priority queue data structure. The most popular seem to be Pairing heap, Fibonacci heap, and Binomial heap, all of which can also be implemented with binary trees. (Again, not binary search trees.)

I wrote a somewhat guided introduction to binary heaps (and d-ary heaps) in my blog a few years ago. If you're interested, check out this entry, which lists all of the articles in that series.