Download presentation

1
**Advanced Data Structures**

Cpt S 223 Advanced Data Structures Washington State University

2
**Topics Data structures Algorithm design & analysis Queue Tree Graph**

Cpt S 223 Topics Data structures Algorithm design & analysis Queue Tree Graph Washington State University

3
**What is a “data structure”?**

4
**Why study data structures?**

Cpt S 223 Why study data structures? Example problem Given: a set of N numbers Goal: search for number k Solution Store numbers in an array of size N Linearly scan array until k is found or array is exhausted Number of checks Best case: 1 Worst case: N Average case: N/2 3 7 6 1 10 8 43 Washington State University

5
**Why study data structures?**

Cpt S 223 Why study data structures? Solution #2 Store numbers in a binary search tree Search tree until find k Number of checks Best case: 1 Worst case: log2N Average case: (log2N)/2 7 3 10 1 6 8 43 Washington State University

6
**Analysis Does it matter? N vs. log2N Cpt S 223**

Washington State University

7
**Analysis Does it matter? Assume 1 cycle per transaction O(N) algorithm**

Cpt S 223 Analysis Does it matter? Assume N = 1,000,000,000 1 billion (Walmart transactions in 100 days) 1 GHz processor = 109 cycles per second 1 cycle per transaction O(N) algorithm 1 billion transactions = > 1 billion clock cycles O(lg N) algorithm 1 billion transactions => 30 clock cycles Washington State University

8
**Example 2 Scheduling job in a printer**

Cpt S 223 Example 2 Scheduling job in a printer Write a code to manage the printer queue Functions to support Insert, delete Special accommodations needed for: Priority Dynamic update Scheduling challenges Washington State University

9
**Example 3 Exploring the Facebook connection network**

Cpt S 223 Example 3 Exploring the Facebook connection network Write a code to tell who is connected to who (directly or indirectly) through your Facebook profile 6-degrees of separation Washington State University

10
**Example 4 Pattern matching**

Cpt S 223 Example 4 Pattern matching Write a code to do Google search on your web database Washington State University

11
**Summary Keep the data organized Choice of data structures matters**

Cpt S 223 Summary Keep the data organized Choice of data structures matters Appropriate data structures ease design & improve performance Challenge Design appropriate data structure & associated algorithms for a problem Analyze to show improved performance Washington State University

12
Queue ADT Like a stack, a queue is also a list. However, with a queue, insertion is done at one end, while deletion is performed at the other end. Accessing the elements of queues follows a First In, First Out (FIFO) order. Like customers standing in a check-out line in a store, the first customer in is the first customer served.

13
**: Basic operations enqueue: insert an element at the rear of the list**

dequeue: delete the element at the front of the list

14
**Implementation of Queue**

Just as stacks can be implemented as arrays or linked lists, so with queues. Dynamic queues have the same advantages over static queues as dynamic stacks have over static stacks

15
**Trees Linear access time of linked lists is prohibitive**

Does there exist any simple data structure for which the running time of most operations (search, insert, delete) is O(log N)? Trees Basic concepts Tree traversal Binary tree Binary search tree and its operations

16
**Trees A tree T is a collection of nodes T can be empty**

(recursive definition) If not empty, a tree T consists of a (distinguished) node r (the root), and zero or more nonempty subtrees T1, T2, ...., Tk

17
Continue..

18
**Tree Traversal Used to print out the data in a tree in a certain order**

Pre-order traversal (Root, left, right) Print the data at the root Recursively print out all data in the leftmost sub tree Recursively print out all data in the rightmost sub tree

19
Continue…

20
**Continue…. Inorder traversal (left, root, right)**

Recursively print out all data in the leftmost sub tree Print the data at the root Recursively print out all data in the rightmost sub tree

21
Continue…

22
**Continue.. Post-order traversal (left, right, root)**

Recursively print out all data in the leftmost sub tree Recursively print out all data in the rightmost sub tree Print the data at the root

23
Continue..

24
Binary Trees A tree in which no node can have more than two children

25
Continue.. The depth of an “average” binary tree is considerably smaller than N, even though in the worst case, the depth can be as large as N – 1.

26
**Binary Search Trees (BST)**

A data structure for efficient searching, inser-tion and deletion Binary search tree property For every node X All the keys in its left subtree are smaller than the key value in X All the keys in its right subtree are larger than the key value in X

27
Continue..

28
Graph A Simple graph G = (V, E) consists of a nonempty set V of vertices and a possibly empty set E of edges, each edge being a set of two vertices from V. The number of vertices and edges are denoted by |V| and |E|, respectively.

29
**Graph Representation There are variety of ways to represent a graph.**

Adjacency lists. An adjacency matrix of graph G = (V, E) is a binary |V| x |V| matrix such that each entry of this matrix An incident matrix of graph G = (V, E) is a binary |V| x |E| matrix such that each entry of this matrix

30
Continue..

31
Continue.. Adjacency matrix Incident matrix

32
Graph Traversal As in trees, traversing a graph consists of visiting each vertex only one time. The simple traversal algorithm used for trees can not be applied here because graph may include cycles (result in to infinite loop) or, isolated vertices (left some nodes). The most popular algorithms for traversing in graphs are: Depth First Search Breadth First search

33
Thank you

Similar presentations

© 2021 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google