You could use the time of creation as the key, or you could make each node represent a person in a database. For example, say you wanted to create a B-tree that contained records of game objects, each with a number of properties. Then a searching algorithm searches the tree for the data. Most B-trees use a single search key to order the data.
The interesting thing about trees is how fast the information can be searched.
The B-trees in Figure 11.7 are shown with their various orders. Moreover, we can talk of the order or number of levels of a tree, meaning how many layers it has. Each node has one or two children nodes (siblings) descending from it hence the term binary.
The most popular kind of tree is the binary tree, AKA B-tree or BST (Binary Search Tree), a tree data structure emanating from a single root that is composed of a collection of nodes. Trees were invented to help with the storage and searching of large amounts of data. Anyway, take a look at Figure 11.6 to see a number of different tree-like data structures. The next class of advanced data structures, trees, are processed by recursive algorithms, so that's why I took the preceding detour.
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