6.3. Sequential Tree Representations (optional)¶
6.3.1. Sequential Tree Representations¶
Next we consider a fundamentally different approach to implementing trees. The goal is to store a series of node values with the minimum information needed to reconstruct the tree structure. This approach, known as a sequential tree representation, has the advantage of saving space because no pointers are stored. It has the disadvantage that accessing any node in the tree requires sequentially processing all nodes that appear before it in the node list. In other words, node access must start at the beginning of the node list, processing nodes sequentially in whatever order they are stored until the desired node is reached. Thus, one primary virtue of the other implementations discussed in this section is lost: efficient access (typically \(\Theta(\log n)\) time) to arbitrary nodes in the tree. Sequential tree implementations are ideal for archiving trees on disk for later use because they save space, and the tree structure can be reconstructed as needed for later processing.
Sequential tree implementations can be used to serialize a tree structure. Serialization is the process of storing an object as a series of bytes, typically so that the data structure can be transmitted between computers. This capability is important when using data structures in a distributed processing environment.
A sequential tree implementation typically stores the node values as they would be enumerated by a preorder traversal, along with sufficient information to describe the tree’s shape. If the tree has restricted form, for example if it is a full binary tree, then less information about structure typically needs to be stored. A general tree, because it has the most flexible shape, tends to require the most additional shape information. There are many possible sequential tree implementation schemes. We will begin by describing methods appropriate to binary trees, then generalize to an implementation appropriate to a general tree structure.
Because every node of a binary tree is either a leaf or has two (possibly empty) children, we can take advantage of this fact to implicitly represent the tree’s structure. The most straightforward sequential tree implementation lists every node value as it would be enumerated by a preorder traversal. Unfortunately, the node values alone do not provide enough information to recover the shape of the tree. In particular, as we read the series of node values, we do not know when a leaf node has been reached. However, we can treat all non-empty nodes as internal nodes with two (possibly empty) children. Only NULL values will be interpreted as leaf nodes, and these can be listed explicitly. Such an augmented node list provides enough information to recover the tree structure.
Figure 6.4.1: Sample binary tree for sequential tree implementation examples.
6.3.2. Alternative Sequential Representation¶
To illustrate the difficulty involved in using the sequential tree representation for processing, consider searching for the right child of the root node. We must first move sequentially through the node list of the left subtree. Only at this point do we reach the value of the root’s right child. Clearly the sequential representation is space efficient, but not time efficient for descending through the tree along some arbitrary path.
Assume that each node value takes a constant amount of space.
An example would be if the node value is a positive integer and
null is indicated by the value zero.
From the
Full Binary Tree Theorem,
we know that the size of the node list will be about twice the number
of nodes (i.e., the overhead fraction is 1/2).
The extra space is required by the null pointers.
We should be able to store the node list more compactly.
However, any sequential implementation must recognize when a leaf node
has been reached, that is, a leaf node indicates the end of a subtree.
One way to do this is to explicitly list with each node whether it is
an internal node or a leaf.
If a node \(X\) is an internal node, then we know that its two
children (which may be subtrees) immediately follow \(X\) in the
node list.
If \(X\) is a leaf node, then the next node in the list is the
right child of some ancestor of \(X\), not the right child
of \(X\).
In particular, the next node will be the child of \(X\) ‘s most
recent ancestor that has not yet seen its right child.
However, this assumes that each internal node does in fact have two
children, in other words, that the tree is
full.
Empty children must be indicated in the node list explicitly.
Assume that internal nodes are marked with a prime (‘) and that
leaf nodes show no mark.
Empty children of internal nodes are indicated by “/”, but the (empty)
children of leaf nodes are not represented at all.
Note that a full binary tree stores no null values with this
implementation, and so requires less overhead.
Storing \(n\) extra bits can be a considerable savings over
storing \(n\) null values.
In the example above, each node was shown with a
mark if it is internal, or no mark if it is a leaf.
This requires that each node value has space to store the mark bit.
This might be true if, for example, the node value were stored as a
4-byte integer but the range of the values sored was small enough so
that not all bits are used.
An example would be if all node values must be positive.
Then the high-order (sign) bit of the integer value could be used as
the mark bit.
6.3.3. Bit Vector Representation¶
Another approach is to store a separate bit vector to represent the status of each node. In this case, each node of the tree corresponds to one bit in the bit vector. A value of “1” could indicate an internal node, and “0” could indicate a leaf node.
6.3.4. General Tree Sequential Representation¶
Storing general trees by means of a sequential implementation requires that more explicit structural information be included with the node list. Not only must the general tree implementation indicate whether a node is leaf or internal, it must also indicate how many children the node has. Alternatively, the implementation can indicate when a node’s child list has come to an end. The next example dispenses with marks for internal or leaf nodes. Instead it includes a special mark (we will use the “)” symbol) to indicate the end of a child list. All leaf nodes are followed by a “)” symbol because they have no children. A leaf node that is also the last child for its parent would indicate this by two or more successive “)” symbols.
Note that this representation for serializing general trees cannot be used for binary trees. This is because a binary tree is not merely a restricted form of general tree with at most two children. Every binary tree node has a left and a right child, though either or both might be empty. So this representation cannot let us distinguish whether node \(D\) in Figure 6.4.1 is the left or right child of node \(B\).
