12 Abstract Data Types

12 Abstract Data Types
12
Abstract
Data Types
12.1
Source: Foundations of Computer Science Cengage Learning
Objectives
After studying this chapter, the student should be able to:

Define the concept of an abstract data type (ADT).
 Define a stack, the basic operations on stacks, their applications
and how they can be implemented.
 Define a queue, the basic operations on queues, their
applications and how they can be implemented.
 Define a general linear list, the basic operations on lists, their
applications and how they can be implemented.
 Define a general tree and its application.
 Define a binary tree—a special kind of tree—and its
applications.
 Define a binary search tree (BST) and its applications.
 Define a graph and its applications.
12.2
1
12-1 BACKGROUND
Problem solving with a computer means processing data.
To process data, we need to define the data type and the
operation to be performed on the data. The definition
of the data type and the definition of the operation to be
applied to the data is part of the idea behind an abstract
data type (ADT) —to hide how the operation is
performed on the data. In other words, the user of an
ADT needs only to know that a set of operations are
available for the data type, but does not need to know
how they are applied.
12.3
Simple ADTs
Many programming languages already define some simple
ADTs as integral parts of the language. For example, the C
language defines a simple ADT as an integer. The type of
this ADT is an integer with predefined ranges. C also defines
several operations that can be applied to this data type
(addition, subtraction, multiplication, division and so on).
C explicitly defines these operations on integers and what we
expect as the results. A programmer who writes a C program
to add two integers should know about the integer ADT and
the operations that can be applied to it.
12.4
2
Complex ADTs
Although several simple ADTs, such as integer, real,
character, pointer and so on, have been implemented and are
available for use in most languages, many useful complex
ADTs are not. As we will see in this chapter, we need a list
ADT, a stack ADT, a queue ADT and so on. To be efficient,
these ADTs should be created and stored in the library of the
computer to be used.
The concept of abstraction means:
1. We know what a data type can do.
2. How it is done is hidden.
12.5
Definition
Let us now define an ADT. An abstract data type is a data
type packaged with the operations that are meaningful
for the data type. We then encapsulate the data and the
operations on the data and hide them from the user.
Abstract data type:
1. Definition of data.
2. Definition of operations.
3. Encapsulation of data and operation.
12.6
3
Model for an abstract data type
The ADT model is shown in Figure 12.1. Inside the ADT are
two different parts of the model: data structure and
operations (public and private).
12.7
Figure 12.1 The model for an ADT
Implementation
Computer languages do not provide complex ADT packages.
To create a complex ADT, it is first implemented and kept in
a library. The main purpose of this chapter is to introduce
some common user-defined ADTs and their applications.
However, we also give a brief discussion of each ADT
implementation for the interested reader. We offer the
pseudocode algorithms of the implementations as
challenging exercises.
12.8
4
12-2 STACKS
A stack is a restricted linear list in which all additions
and deletions are made at one end, the top. If we insert a
series of data items into a stack and then remove them,
the order of the data is reversed. This reversing attribute
is why stacks are known as last in, first out (LIFO)
data structures.
Figure 12.2 Three representations of stacks
12.9
Operations on stacks
There are four basic operations, stack, push, pop and
empty, that we define in this chapter.
The stack operation
The stack operation creates an empty stack. The following
shows the format.
stack(S)
Figure 12.3 Stack operation
12.10
5
The push operation
The push operation inserts an item at the top of the stack.
The following shows the format.
push (S,num)
{ top = top +1;
S[top] = num;
}
push(S,20)
push(S,78)
push(S,30)
Figure 12.4 Push operation
12.11
The pop operation
The pop operation deletes the item at the top of the stack.
The following shows the format.
pop(S,x); // x=30
12.12
pop (S)
{ num = S[top];
top = top -1;
return num;
}
Figure 12.5 Pop operation
6
The empty operation
The empty operation checks the status of the stack. The
following shows the format.
If empty(S)
This operation returns true if the stack is empty and false if
the stack is not empty.
12.13
Stack ADT
We define a stack as an ADT as shown below:
12.14
7
Give a stack S as below,
find Top =
and S[top]=
after Pop(S),
Push(S,25), Push(S,33), Pop(S), Pop(S), and Pop(S).
1 2 3 4 5 6 7
S 75 56 43 68 92 14 28
8
9
Top
12.15
Example 12.1
Figure 12.6 shows a segment of an algorithm that applies the
previously defined operations on a stack S.
Figure 12.6 Example 12.1
12.16
8
Stack applications
Stack applications can be classified into four broad
categories: reversing data, pairing data, postponing data
usage and backtracking steps. We discuss the first two in
the sections that follow.
Reversing data items
Reversing data items requires that a given set of data items
be reordered so that the first and last items are exchanged,
with all of the positions between the first and last also being
relatively exchanged.
For example,
the list (2, 4, 7, 1, 6, 8) becomes (8, 6, 1, 7, 4, 2).
12.17
Example 12.2
Convert a decimal integer to binary and print the results
12.18
9
Example 12.2 (Continued)
We can use the reversing characteristic of a stack (LIFO structure)
to solve the problem.
Algorithm 12.1 shows the pseudocode to convert a decimal
integer to binary and print the result. We create an empty stack
first. Then we use a while loop to create the bits, but instead of
printing them, we push them into the stack. When all bits are
created, we exit the loop. Now we use another loop to pop the
bits from the stack and print them. Note that the bits are printed
in the reverse order to that in which they have been created.
12.19
//conversion of decimal to binary
// output the binary
12.20
10
Pairing data items
We often need to pair some characters in an expression.
For example, when we write a mathematical expression in a
computer language, we often need to use parentheses to
change the precedence of operators. The following
expression is evaluated because of the parentheses:
When we type an expression with a lot of parentheses, one of
the duties of a compiler is to do the checking for us. The
compiler uses a stack to check that all opening parentheses
are paired with a closing parentheses.
Example 12.3
Algorithm 12.2 shows how we can check if all opening
parentheses are paired with a closing parenthesis.
12.21
12.22
11
Stack implementation
At the ADT level, we use the stack and its four operations; at
the implementation level, we need to choose a data
structure to implement it. Stack ADTs can be implemented
using either an array or a linked list. Figure 12.7 shows an
example of a stack ADT with five items. The figure also
shows how we can implement the stack.
In our array implementation, we have a record that has two
fields. The first field can be used to store information about
the array. The linked list implementation is similar: we have
an extra node that has the name of the stack. This node also
has two fields: a counter and a pointer that points to the top
element.
12.23
Figure 12.7 Stack implementations
12.24
12
12-3 QUEUES
A queue is a linear list in which data can only be
inserted at one end, called the rear, and deleted from the
other end, called the front. These restrictions ensure that
the data is processed through the queue in the order in
which it is received. In other words, a queue is a first in,
first out (FIFO) structure.
Figure 12.8 Two representation of queues
12.25
Operations on queues
Although we can define many operations for a queue, four
are basic: queue, enqueue, dequeue and empty, as defined
below.
The queue operation
The queue operation creates an empty queue. The
following shows the format.
queue(Q)
Figure 12.9 The queue operation
12.26
13
The enqueue operation
The enqueue operation inserts an item at the rear of the
queue. The following shows the format.
enqueue(Q,20)
enqueue(Q,78)
enqueue(Q,34)
enqueue (Q,num)
{ rear = rear +1;
if (front ==-1) front = rear;
Q[rear] = num;
}
12.27
The dequeue operation
The dequeue operation deletes the item at the front of the
queue. The following shows the format.
dequeue (Q)
{ num = Q[front];
front = front +1;
if (front ==-1) rear = front;
return num;
}
dequeue(Q,x) // x=20
12.28
14
The empty operation
The empty operation checks the status of the queue. The
following shows the format.
if empty(Q)
This operation returns true if the queue is empty and false if
the queue is not empty.
12.29
Queue ADT
We define a queue as an ADT as shown below:
12.30
15
Example 12.4
Figure 12.12 shows a segment of an algorithm that applies the
previously defined operations on a queue Q.
Figure 12.12 Example 12.4
12.31
Please show the contents of the queue Q after
the operations of enqueue(Q,54), dequeue(Q),
enqueue(Q,71), dequeue(Q), dequeue(Q), and
enqueue(Q,33), and Front=? and Rear=?.
0 1 2 3
Q 42 69 57 35
Front
4
5
6
7
8
9
Rear
12.32
16
Queue applications
Queues are one of the most common of all data processing
structures. They are found in virtually every operating
system and network and in countless other areas. For
example, queues are used in online business applications
such as processing customer requests, jobs and orders. In
a computer system, a queue is needed to process jobs and for
system services such as print spools.
Example 12.5
Imagine we have a list of sorted data stored in the computer belonging
to two categories: less than 1000, and greater than 1000. We can use
two queues to separate the categories and at the same time maintain
the order of data in their own category. Algorithm 12.3 shows the
pseudocode for this operation.
12.33
12.34
17
Example 12.6
Another common application of a queue is to adjust and create
a balance between a fast producer of data and a slow
consumer of data. For example, assume that a CPU is connected
to a printer. The speed of a printer is not comparable with the
speed of a CPU. If the CPU waits for the printer to print some
data created by the CPU, the CPU would be idle for a long time.
The solution is a queue. The CPU creates as many chunks of data
as the queue can hold and sends them to the queue. The CPU is
now free to do other jobs. The chunks are dequeued slowly and
printed by the printer. The queue used for this purpose is
normally referred to as a spool queue.
12.35
Queue implementation
At the ADT level, we use the queue and its four operations at
the implementation level. We need to choose a data structure
to implement it. A queue ADT can be implemented using
either an array or a linked list. Figure 12.13 on page 329
shows an example of a queue ADT with five items. The
figure also shows how we can implement it. In the array
implementation we have a record with three fields. The first
field can be used to store information about the queue.
The linked list implementation is similar: we have an extra
node that has the name of the queue. This node also has three
fields: a count, a pointer that points to the front element and
a pointer that points to the rear element.
12.36
18
Figure 12.13 Queue implementation
12.37
12-4 GENERAL LINEAR LISTS
Stacks and queues defined in the two previous sections
are restricted linear lists. A general linear list is a list
in which operations, such as insertion and deletion, can
be done anywhere in the list—at the beginning, in the
middle or at the end. Figure 12.14 shows a general
linear list.
Figure 12.14 General linear list
12.38
19
Operations on general linear lists
Although we can define many operations on a general linear
list, we discuss only six common operations in this chapter:
list, insert, delete, retrieve, traverse and empty.
The list operation
The list operation creates an empty list. The following
shows the format:
12.39
The insert operation
Since we assume that data in a general linear list is sorted,
insertion must be done in such a way that the ordering of
the elements is maintained. To determine where the
element is to be placed, searching is needed. However,
searching is done at the implementation level, not at the ADT
level.
12.40
Figure 12.15 The insert operation
20
The delete operation
Deletion from a general list (Figure 12.16) also requires that
the list be searched to locate the data to be deleted. After the
location of the data is found, deletion can be done. The
following shows the format:
Figure 12.16 The dequeue operation
12.41
The retrieve operation
By retrieval, we mean access of a single element. Like
insertion and deletion, the general list should be first
searched, and if the data is found, it can be retrieved. The
format of the retrieve operation is:
Figure 12.17 The retrieve operation
12.42
21
The traverse operation
Each of the previous operations involves a single element in
the list, randomly accessing the list. List traversal, on the
other hand, involves sequential access. It is an operation in
which all elements in the list are processed one by one. The
following shows the format:
12.43
The empty operation
The empty operation checks the status of the list. The
following shows the format:
This operation returns true if the list is empty, or false if the
list is not empty.
12.44
22
General linear list ADT
We define a general linear list as an ADT as shown below:
12.45
Example 12.7
Figure 12.18 shows a segment of an algorithm that applies the
previously defined operations on a list L. Note that the third and
fifth operation inserts the new data at the correct position,
because the insert operation calls the search algorithm at the
implementation level to find where the new data should be
inserted. The fourth operation does not delete the item with value
3 because it is not in the list.
12.46
Figure 12. 18 Example 12.7
23
General linear list applications
General linear lists are used in situations in which the
elements are accessed randomly or sequentially. For
example, in a college a linear list can be used to store
information about students who are enrolled in each
semester.
12.47
Example 12.8
A college has a general linear list that holds information about the
students and that each data element is a record with three fields: ID,
Name and Grade. Algorithm 12.4 shows an algorithm that helps a
professor to change the grade for a student. The delete operation
removes an element from the list, but makes it available to the program
to allow the grade to be changed. The insert operation inserts the
changed element back into the list. The element holds the whole record
for the student, and the target is the ID used to search the list.
12.48
24
Example 12.8 (Continued)
12.49
Example 12.9
Continuing with Example 12.8, assume that the tutor wants to
print the record of all students at the end of the semester.
Algorithm 12.5 can do this job. We assume that there is an
algorithm called Print that prints the contents of the record. For
each node, the list traverse calls the Print algorithm and passes
the data to be printed to it.
12.50
25
General linear list implementation
At the ADT level, we use the list and its six operations but at
the implementation level we need to choose a data structure
to implement it. A general list ADT can be implemented
using either an array or a linked list. Figure 12.19 shows
an example of a list ADT with five items. The figure also
shows how we can implement it.
The linked list implementation is similar: we have an extra
node that has the name of the list. This node also has two
fields, a counter and a pointer that points to the first element.
12.51
Figure 12.19 General linear list implementation
12.52
26
12-5 TREES
A tree consists of a finite set of elements, called nodes
(or vertices) and a finite set of directed lines, called
arcs, that connect pairs of the nodes.
Figure 12.20 Tree representation
12.53
We can divided the vertices in a tree into three categories:
the root, leaves and the internal nodes. Table 12.1 shows the
number of outgoing and incoming arcs allowed for each
type of node.
12.54
27
Each node in a tree may have a subtree. The subtree of each
node includes one of its children and all descendents of that
child. Figure 12.21 shows all subtrees for the tree in Figure
12.20.
Figure 12.21 Subtrees
12.55
Physical structure of a tree, each node includes one of its
children and all descendents of that child.
Root
John
Mary
Ton
Rom
Jessy
Pat
12.56
28
12-6 BINARY TREES
A binary tree is a tree in which no node can have more
than two subtrees. In other words, a node can have zero,
one or two subtrees.
Figure 12.22 A binary tree
12.57
Recursive definition of binary trees
In Chapter 8 we introduced the recursive definition of an
algorithm. We can also define a structure or an ADT
recursively. The following gives the recursive definition of a
binary tree. Note that, based on this definition, a binary tree
can have a root, but each subtree can also have a root.
12.58
29
Figure 12.23 shows eight trees, the first of which is an empty
binary tree (sometimes called a null binary tree).
Figure 12.23 Examples of binary trees
12.59
Operations on binary trees
The six most common operations defined for a binary tree
are tree (creates an empty tree), insert, delete, retrieve,
empty and traversal. The first five are complex and beyond
the scope of this book. We discuss binary tree traversal in
this section.
Root
*
-
35
6
9
12.60
30
Binary tree traversals
A binary tree traversal requires that each node of the tree
be processed once and only once in a predetermined
sequence. The two general approaches to the traversal
sequence are depth-first and breadth-first traversal.
Figure 12.24 Depth-first traversal of a binary tree
12.61
Example 12.10
Figure 12.25 shows how we visit each node in a tree using preorder
traversal. The figure also shows the walking order. In preorder
traversal we visit a node when we pass from its left side. The nodes
are visited in this order: A, B, C, D, E, F.
Figure 12.25 Example 12.10
12.62
31
Example 12.11
Figure 12.26 shows how we visit each node in a tree using breadthfirst traversal. The figure also shows the walking order. The traversal
order is A, B, E, C, D, F.
Figure 12.26 Example 12.11
12.63
Binary tree applications
Binary trees have many applications in computer science. In
this section we mention only two of them: Huffman coding
and expression trees.
Huffman coding
Huffman coding is a compression technique that uses
binary trees to generate a variable length binary code from
a string of symbols. We discuss Huffman coding in detail in
Chapter 15.
12.64
32
Expression trees
An arithmetic expression can be represented in three
different formats: infix, postfix and prefix. In an infix
notation, the operator comes between the two operands. In
postfix notation, the operator comes after its two operands,
and in prefix notation it comes before the two operands.
These formats are shown below for addition of two operands
A and B.
Root
+
Prefix: +AB
Infix: A+B
Postfix: AB+
A
B
12.65
Root
*
-
35
6
9
12.66
33
Figure 12.27 Expression tree
12.67
Root
78
55
42
92
60
119
12.68
34
12-7 BINARY SEARCH TREES
A binary search tree (BST) is a binary tree with one
extra property: the key value of each node is greater
than the key values of all nodes in each left subtree and
smaller than the value of all nodes in each right subtree.
Figure 12.28 shows the idea.
Figure 12.28 Binary search tree (BST)
12.69
Example 12.12
Figure 12.29 shows some binary trees that are BSTs and some that
are not. Note that a tree is a BST if all its subtrees are BSTs and
the whole tree is also a BST.
Figure 12.29 Example 12.12
12.70
35
A very interesting property of a BST is that if we apply the
inorder traversal of a binary tree, the elements that are visited
are sorted in ascending order. For example, the three BSTs in
Figure 12.29, when traversed in order, give the lists
(3, 6, 17), (17, 19) and (3, 6, 14, 17, 19).
An inorder traversal of a BST creates a list that is
sorted in ascending order.
12.71
Another feature that makes a BST interesting is that we can
use a version of the binary search we used in Chapter 8 for a
binary search tree. Figure 12.30 shows the UML for a BST
search.
Figure 12.30 Inorder traversal of a binary search tree
12.72
36
Binary search tree ADTs
The ADT for a binary search tree is similar to the one we
defined for a general linear list with the same operation. As a
matter of fact, we see more BST lists than general linear lists
today. The reason is that searching a BST is more efficient
than searching a linear list: a general linear list uses
sequential searching, but BSTs use a version of binary
search.
12.73
BST implementation
BSTs can be implemented using either arrays or linked lists.
However, linked list structures are more common and
more efficient. The implementation uses nodes with two
pointers, left and right.
Figure 12.31 A BST implementation
12.74
37
12-8 GRAPHS
A graph is an ADT made of a set of nodes, called
vertices, and set of lines connecting the vertices, called
edges or arcs. Whereas a tree defines a hierarchical
structure in which a node can have only one single
parent, each node in a graph can have one or more
parents. Graphs may be either directed or undirected. In
a directed graph, or digraph, each edge, which connects
two vertices, has a direction from one vertex to the other.
In an undirected graph, there is no direction. Figure
12.32 shows an example of both a directed graph (a)
and an undirected graph (b).
12.75
Figure 12.32 Graphs
12.76
38
Example 12.13
A map of cities and the roads connecting the cities can be
represented in a computer using an undirected graph. The cities
are vertices and the undirected edges are the roads that connect them.
If we want to show the distances between the cities, we can use
weighted graphs, in which each edge has a weight that represents
the distance between two cities connected by that edge.
Example 12.14
Another application of graphs is in computer networks (Chapter 6).
The vertices can represent the nodes or hubs, the edges can
represent the route. Each edge can have a weight that defines the
cost of reaching from one hub to an adjacent hub. A router can use
graph algorithms to find the shortest path between itself and the
final destination of a packet.
12.77
39
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Download PDF

advertisement