Lecture1 data structure(introduction)

Lecture1 data structure(introduction)

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  1 Introduction to Data Structures Lecture1 Khalid Khankan and Abdisalam Issa-Salwe Taibah University College of Computer Science & Engineering Computer Science Department 2 Outline 1. Choosing Data Structures 2. Selecting Data Structure 3. Data Structure Philosophy 4. Abstract Data Types 5. Logical vs. Physical Form 6. Programs 7. Algorithm Properties 8. References
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  2 3 Data Structures A datastructure is a scheme for organizing data in the memory of a computer. Some of the more commonly used data structures include lists, arrays, stacks, queues, heaps, trees, and graphs. Binary Tree 4 Data Structures The way in which the data is organized affects the performance of a program for different tasks. Computer programmers decide which data structures to use based on the nature of the data and the processes that need to be performed on that data. Binary Tree
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  3 5 The Need forData 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. 6 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.
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  4 7 Example: A Queue Aqueue is an example of commonly used simple data structure. A queue has beginning and end, called the front and back of the queue. Data enters the queue at one end and leaves at the other. Because of this, data exits the queue in the same order in which it enters the queue, like people in a checkout line at a supermarket. 8 Example: A Binary Tree A binary tree is another commonly used data structure. It is organized like an upside down tree. Each spot on the tree, called a node, holds an item of data along with a left pointer and a right pointer. Binary Tree
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  5 9 Example: A BinaryTree The pointers are lined up so that the structure forms the upside down tree, with a single node at the top, called the root node, and branches increasing on the left and right as you go down the tree. Binary Tree 10 Choosing Data Structures By comparing the queue with the binary tree, you can see how the structure of the data affects what can be done efficiently with the data.
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  6 11 Choosing Data Structures Aqueue is a good data structure to use for storing things that need to be kept in order, such as a set of documents waiting to be printed on a network printer. . 12 Choosing Data Structures The jobs will be printed in the order in which they are received. Most network print servers maintain such a print queue. .
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  7 13 Choosing Data Structures Abinary tree is a good data structure to use for searching sorted data. The middle item from the list is stored in the root node, with lesser items to the left and greater items to the right. 14 Choosing Data Structures A search begins at the root. The computer either find the data, or moves left or right, depending on the value for which you are searching. Each move down the tree cuts the remaining data in half.
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  8 15 Choosing Data Structures Itemscan be located very quickly in a tree. Telephone directory assistance information is stored in a tree, so that a name and phone number can be found quickly. 16 Choosing Data Structures For some applications, a queue is the best data structure to use. For others, a binary tree is better. Programmers choose from among many data structures based on how the data will be used by the program.
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  9 17 The Need forData 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. 18 Selecting a Data Structure Select a data structure as follows: 1. Analyze the problem to determine the resource constraints a solution must meet. 2. Determine the basic operations that must be supported. Quantify the resource constraints for each operation. 3. Select the data structure that best meets these requirements.
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  10 19 Data Structure Philosophy Eachdata structure has costs and benefits. Rarely is one data structure better than another in all situations. A data structure requires: space for each data item it stores, time to perform each basic operation, programming effort. 20 Data Structure Philosophy Each data structure has costs and benefits. Rarely is one data structure better than another in all situations. A data structure requires: space for each data item it stores, time to perform each basic operation, programming effort.
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  11 21 Abstract Data Types AbstractData Type (ADT): a definition for a data type solely 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. 22 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.  In a OO language such as C++, an ADT and its implementation together make up a class.  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.
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  12 23 Abstract data types we also looked at how to use an array to model a list  we call a list an abstract data type  it has defined operations  add to the end of the list  find an item in the list  delete an item from the list etc.  it can be implemented in different ways  array, piece of paper, tree, linked list  the operations have the same effect no matter how the list is implemented  other examples of abstract data types  set, queue, stack, map 24 Sets  a set is an unordered group of elements  duplicates are not allowed  otherwise how would you tell them apart?  the set of "foods I like" contains the elements  cereal, chicken, chips, tomato soup, orange juice and chocolate Cereal Chicken Chocolate set of foods I like Chips Orange juice Tomato soup
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  13 25 Lists  a listis a group of elements arranged in some order  so we can talk about  the first element in the list  the last element in the list  element 3  the order could be meaningful  alphabetical  by size  by time  or it could simply be the order the elements were added  duplicates are allowed  they are distinguished by their position Things I ate today (in chronological order) 1. cereal 2. orange juice 3. chocolate 4. tomato soup 5. chocolate 6. chicken 7. chips 8. chocolate 26 Queue  a queue is a a group of items arranged in order of arrival  new items can only be added to the back of the queue  items can only be removed from the front of the queue  shoppers at supermarket check-out  taxis in rank  computer processes awaiting execution  first-in, first-out (FIFO)
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  14 27 Stacks  a stackis a group of items arranged in order  new items can only be added to the top of the stack  items can only be removed from the top of the stack  stack of chairs or books  plates in cafeteria  temporary data storage in CPU  last in, first out (LIFO) 28 Maps  A map is a collection of key/element pairs  each element is stored in a position determined by its key  can look up an element using its key  also known as a dictionary  key is the word  element is the definition algorithm confusion university
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  15 29 Data Structure  Adata structure is the physical implementation of an ADT.  Each operation associated with the ADT is implemented by one or more subroutines in the implementation.  In a OO language such as C++, an ADT and its implementation together make up a class.  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. 30 Labeling collections of objects Humans deal with complexity by assigning a label to an assembly of objects. An ADT manages complexity through abstraction.  Hierarchies of labels Ex1: transistors  gates  CPU. In a program, implement an ADT, then think only about the ADT, not its implementation.
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  16 31 Logical vs. PhysicalForm 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. 32 Data Type ADT: Type Operations Data Items: Logical Form Data Items: Physical Form Data Structure: Storage Space Subroutines
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  17 33 Problems, Algorithms andPrograms  Programmers deal with: problems, algorithms and computer programs. 34 Problems  Problem: a task to be performed. Best thought of as inputs and matching outputs. Problem definition should include constraints on the resources that may be consumed by any acceptable solution.
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  18 35 Problems (cont)  Problems mathematical functions A function is a matching between inputs (the domain) and outputs (the range). An input to a function may be single number, or a collection of information. The values making up an input are called the parameters of the function. A particular input must always result in the same output every time the function is computed. 36 Algorithms and Programs Algorithm: a method or a process followed to solve a problem.  A recipe: The algorithm gives us a “recipe” for solving the problem by performing a series of steps, where each step is completely understood and doable. An algorithm takes the input to a problem (function) and transforms it to the output.  A mapping of input to output. A problem can be solved by many algorithms.
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  19 37 A problem canhave many algorithms For example, the problem of sorting can be solved by the following algorithms:  Insertion sort  Bubble sort  Selection sort  Shell sort  Merge sort  Others 38 Algorithm Properties An algorithm possesses the following properties:  It must be correct.  It must be composed of a series of concrete steps.  There can be no ambiguity as to which step will be performed next.  It must be composed of a finite number of steps.  It must terminate. A computer program is an instance, or concrete representation, for an algorithm in some programming language.
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  20 39 Programs  A computerprogram is a concrete representation of an algorithm in some programming language.  Naturally, there are many programs that are instances of the same algorithms, since any modern programming language can be used to implement any algorithm. 40 References  Michael Main, Data Structures and Other Objects Using Java (Third Edition)