Data Structures & Algorithms

Lec#1

Data Structures and
Algorithms

What is a data structure?
Data structures are used to store data in a computer in an

organized fashion.
The scheme of organizing related information that
considers not only the items stored, but also their
relationship to each other .
It is a group of data elements grouped together under one
name.
- These data elements are called members. They can have
different types and different lengths.
- Some of them store the data of same type while others
store different types of data.
In programming the term data structure refers to a scheme
for organizing related piece of information.
 Data Structure = Organized Data + Allowed Operations.
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Goals of this Course
1. Reinforce the concept that costs and benefits exist

for every data structure.
1. Learn the commonly used data structures.
 These form a programmer's basic data structure

``toolkit.'‘

1. Understand how to measure the cost of a data

structure or program.
 These techniques also allow you to judge the merits

of new data structures that you or others might
invent.

Areas in which data structures are applied
Compiler Design
 Operating System
 Database Management System
 Statistical analysis package
 Numerical Analysis
 Graphics
 Artificial Intelligence

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The Need for Data Structures
Data structures organize data
⇒ more efficient programs.
More powerful computers
⇒ more complex applications.
More complex applications demand more calculations.
Complex computing tasks are unlike our everyday
experience.

Organizing Data
Any organization for a collection of

records can be searched, processed
in any order, or modified.
The choice of data structure and
algorithm can make the difference
between a program running in a few
seconds or many days.

Efficiency
A solution is said to be efficient if it solves the

problem within its resource constraints.
Space
Time

The cost of a solution is the amount of resources

that the solution consumes.

Selecting a Data Structure
Select a data structure as follows:
1. Analyze the problem to determine the basic
operations that must be supported.
2. Quantify the resource constraints for each
operation.
3. Select the data structure that best meets these
requirements.

Costs and Benefits
Each data structure has costs and benefits.
Rarely is one data structure better than another in all

situations.
Any data structure requires:
space for each data item it stores,
time to perform each basic operation,
programming effort.

Costs and Benefits (cont)
Each problem has constraints on available

space and time.
Only after a careful analysis of problem
characteristics we can know the best data
structure for the task.
Bank example:
Start account: a few minutes
Transactions: a few seconds
Close account: overnight

Example 1.2
Problem: Create a database containing information

about cities and towns.
Tasks: Find by name or attribute or location
Exact match, range query, spatial query
Resource requirements: Times can be from a few
seconds for simple queries to a minute or two for
complex queries

Abstract Data Types
Abstract Data Type (ADT): defining a data type

in terms of a set of values and a set of operations
on that data type.
Each ADT operation is defined by its inputs and

outputs.
Encapsulation: Hide implementation details.

Data Structure
A data structure is the physical implementation of an

ADT.
Each operation associated with the ADT is

implemented by one or more subroutines in the
implementation.

Data structure usually refers to an organization for

data in main memory.
File structure: an organization for data on peripheral

storage, such as a disk drive.

Metaphors
An ADT manages complexity through abstraction:
metaphor.
Hierarchies of labels

Ex: transistors ⇒ gates ⇒ CPU.
In a program, implement an ADT, then think only about
the ADT, not its implementation.

Logical vs. Physical Form
Data items have both a logical and a physical form.
Logical form: definition of the data item within an

ADT.
Ex: Integers in mathematical sense: +, -

Physical form: implementation of the data item within

a data structure.
Ex: 16/32 bit integers, overflow.

Data Type
ADT:
Type
Operations

Data Items:
Logical Form

Data Structure:
Storage Space
Subroutines

Data Items:
Physical Form

Define a linear and non linear data
structure
• Linear data structure: A linear data structure
traverses the data elements sequentially
 Only one data element can directly be reached.
 Ex: Arrays, Linked Lists
• Non-Linear data structure: Every data item is
attached to several other data items in a way that
is specific for reflecting relationships.
• The data items are not arranged in a sequential
structure.
• Ex: Trees, Graphs
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What are the types of data structures?
The types of data structure are:
Arrays: A set of homogeneous values.
Lists: A group of similar items with
connectivity to the previous or/and next data
items.
Matrix: A way to store data in an organized
form in the form of rows and columns.
Linked list: Stored data in a linear fashion.
Stack: Works in first in last out order. The
element inserted first in stack is removed last
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Queues: First in First out order. The element

inserted first is removed first.
Records: A set of fields, where each field consists of
data belongs to one data type.
Trees: A data structure where the data is organized
in a hierarchical structure. This type of data structure
follows the sorted order of insertion, deletion and
modification of data items.
Tables: Data is persisted in the form of rows and
columns. These are similar to records, where the
result or manipulation of data is reflected for the
whole table
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Data Structures & Algorithms

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