Introduction to Data Structure Chapter 1 Introduction to Data Structure

What is Data  computers run programs  data is the ‘raw material’ that computer programs process  there must be some way to store the data so that they could be processed

How to store /organize data in a program? Single Variables Data Structure

Understand program execution & data INPUT OUTPUT PROCESS var 1 data 1 data2 data3 data4 (from keyboard / files / other input devices) (to screen / files / other output devices) var 2 PROGRAM var 3 var 4 In a simple program - Data is stored in simple variables

Types of data  Most programming language requires variables to be declared by relating them with the type of data that it contains  eg. float number;  There are many primitive data types (lowest data representation) in a language: int, float, char, double etc  Depending on the type:  different operations allowed on the data  different internal representation

Introduction to Data Type In Java programming, all variables are required to have a data type. Java Data Types can be divided into: Fundamental /Primitive /Base Type Abstract Data Type

Fundamental /Primitive /Base Type A variable of the fundamental type simply store a value of that type (one value at a time). 8 fundamental types int : 32-bit signed two’s complement integer float :32-bit floating-point number double : 64-bit floating-point number long :64-bit signed two’s complement integer short :16-bit signed two’s complement integer char :16-bit unicode character boolean:boolean value – true or false byte :8-bit signed two’s complement integer

Abstract Data Type (ADT) Definition: A set of data values and associated operations that are precisely specified independent of any particular implementation. Paul E. Black (2004)

Abstract Data Type (ADT) A data type that specifies the logical properties without the implementation details An ADT is presented as a class type and treated as a single entity that encapsulates its data together with its method An ADT provide information hiding ADT kept the implementation details of the operations and the data from the users of ADT The user of ADT can use the operations on an ADT without knowing how the operation is implemented

Abstract Data Type (ADT) ADT is used to describe the characteristics of a data structure An ADT has A set of values (data) A set of operations (methods) The data representation and actual algorithms for the operations are unspecified Classes are used to represent objects in Object Oriented Programming (OOP) approach. OOP uses classes to encapsulate data (attributes) and methods (behaviors). Encapsulate enables objects to hide their implementation from other objects.

Abstract Data Type (ADT) User is interested in ADT, given as interfaces, while developer focuses on data structures, namely classes. We have Java collections framework’s interfaces E.g: List interface – one of the classes that implement that interface is LinkedList (user’s view of List as an ADT)

Abstract Data Type (ADT) ADT is also referred to as an object. ADT contains more than one fundamental data types. ADT also contains the methods as an interface to manipulate the data. Each ADT is an encapsulation of data and methods. In Java, ADT or object is defined using Class. Eg: class Computer { private int code; private String brand; private double price; public Computer () { } public Computer (int c, String b, double p) {…} public int getCode () { return code; } public String getBr () { return brand; } public double getPr() { return price; }; }

Data Structure is a particular way of storing and organizing data in a computer so it can be used efficiently. is a systematic way of organizing a collection of data. is a representation of data and the operations allowed on the data. is known as a collection of related data items. Every data structure needs a variety of algorithms for processing the data in it (insertion, deletion, retrieval, etc) Algorithms are used to manipulate the data contained in these data structures as in searching and sorting. Different kinds of data structures are suited to different kinds of applications.

Data Structure Example of Data Structure Arrays Lists Queues Stacks Trees Indexes The choice on which data structure to be used for a system will affect the system design and contribute to the effectiveness of that system. The correct choice of data structure allows major improvement in program efficiency 2 types: Static – fix size (array, record) Dynamic – grow and shrink at runtime (list, queue, stack, binary tree)

Data Structure Static VS Dynamic Data Structure Besides time and space efficiency another important criterion for choosing a data structure is whether the number of data items it is able to store can adjust to our needs or is bounded. This distinction leads to the notions of dynamic data structures vs. static data structures. Dynamic data structures grow or shrink during run-time to fit current requirements e.g., a structure used in modeling traffic flow. Static data structures are fixed at the time of creation. e.g., a structure used to store a postcode or credit card number (which have a fixed format).

Data Structure Advantages of Static Data Structure ease of specification Programming languages usually provide an easy way to create static data structures of almost arbitrary size no memory allocation overhead Since static data structures are fixed in size, there are no operations that can be used to extend static structures; such operations would need to allocate additional memory for the structure (which takes time)

Data Structure Disadvantages of Static Data Structure must make sure there is enough capacity the number of data items we can store in a static data structure is fixed, once it is created, we have to make sure that this number is large enough for all our needs more elements? (errors), fewer elements? (waste) However, when our program tries to store more data items in a static data structure than it allows, this will result in an error (e.g. ArrayIndexOutOfBoundsException) On the other hand, if fewer data items are stored, then parts of the static data structure remain empty, but the memory has been allocated and cannot be used to store other data.

Data Structure Advantages of Dynamic Data Structure There is no requirement to know the exact number of data items Since dynamic data structures can shrink and grow to fit exactly the right number of data items, there is no need to know how many data items we will need to store Efficient use of memory space if we only extend a dynamic data structure in size whenever we need to add data items have exactly the right size and no memory space is wasted

Data Structure Disadvantages of Dynamic Data Structure Memory allocation/de-allocation overhead Whenever a dynamic data structure grows or shrinks, then memory space allocated to the data structure has to be added or removed (which requires time)

Data Structure A good approach to determine which data structure that best suit your application is to study the strengths and weaknesses of each of the data structure available.

Data Structure Application Array A data structure that holds a collection of data in sequential order Is the simplest data structure and easy to use Can be 1 dimensional and multi-dimensional 2 limitations: Once an array is created its size cannot be altered An array does not provide adequate support for insertion and deletion operation

Data Structure Application List A dynamic data structure where size can be changed A list is a collection of data stored sequentially It supports insertion and deletion anywhere in the list Two types of list : Sequential list (array list) – arrays that store data in a contiguous memory (elements next to each other) Linked list – a linked structure that store data where one element refers to another, anywhere in memory

Data Structure Application Stack A stack can be perceived as a special type of list where insertions and deletions take place only one end, refererred to as top of the stack Known as LIFO (Last in First Out) Example: Stack of plates

Data Structure Application Queue A queue represent a waiting list, where insertions take place at the back of the queue Adding at the back and deleting in front Known as FIFO (First In First Out) Example: queuing for the bus, taxi, etc

Data Structure Application Binary tree A binary tree is a data structure that supports searching, sorting, inserting and deleting data efficiently Data in a hierarchical relationship

ADT & Data Structure ADT consist of a collection of values, together with collection of operations on those values. Using Java, a user of a class can: Create an instance of that class (value) Invoke the public methods of the class However, classes have additional properties (inheritance and polymorphism) not normally associated with ADTs

ADT & Data Structure A Data Structure is the implementation of an abstract data type. In OO languages, a developer implements an interface with class. In another word is as following association : General term Object Oriented Term Abstract Data Type interface Data Structure class

ADT & Data Structure ADT is the logical picture of the data and the operations to manipulate the component elements of the data Data structure is the actual representation of the data during the implementation and the algorithms to manipulate the data elements ADT is the logical level and data structure is in the implementation level Conclusion: Data structure is how we implement the data in an abstract data type.