•Download as PPTX, PDF•

3 likes•4,654 views

hashing is encryption process mostly used in programming language for security purpose. This presentation will you understand all about hashing and also different techniques used in it for encryption process

Report

Share

Report

Share

Hashing Technique In Data Structures

This document discusses different searching methods like sequential, binary, and hashing. It defines searching as finding an element within a list. Sequential search searches lists sequentially until the element is found or the end is reached, with efficiency of O(n) in worst case. Binary search works on sorted arrays by eliminating half of remaining elements at each step, with efficiency of O(log n). Hashing maps keys to table positions using a hash function, allowing searches, inserts and deletes in O(1) time on average. Good hash functions uniformly distribute keys and generate different hashes for similar keys.

Hashing

Hashing is a technique used to uniquely identify objects by assigning each object a key, such as a student ID or book ID number. A hash function converts large keys into smaller keys that are used as indices in a hash table, allowing for fast lookup of objects in O(1) time. Collisions, where two different keys hash to the same index, are resolved using techniques like separate chaining or linear probing. Common applications of hashing include databases, caches, and object representation in programming languages.

Hashing PPT

This document discusses hashing and different techniques for implementing dictionaries using hashing. It begins by explaining that dictionaries store elements using keys to allow for quick lookups. It then discusses different data structures that can be used, focusing on hash tables. The document explains that hashing allows for constant-time lookups on average by using a hash function to map keys to table positions. It discusses collision resolution techniques like chaining, linear probing, and double hashing to handle collisions when the hash function maps multiple keys to the same position.

Hashing in datastructure

The document discusses hash tables and hashing techniques. It describes how hash tables use a hash function to map keys to positions in a table. Collisions can occur if multiple keys map to the same position. There are two main approaches to handling collisions - open hashing which stores collided items in linked lists, and closed hashing which uses techniques like linear probing to find alternate positions in the table. The document also discusses various hash functions and their properties, like minimizing collisions.

linked list in data structure

This document discusses data structures and linked lists. It provides definitions and examples of different types of linked lists, including:
- Single linked lists, which contain nodes with a data field and a link to the next node.
- Circular linked lists, where the last node links back to the first node, forming a loop.
- Doubly linked lists, where each node contains links to both the previous and next nodes.
- Operations on linked lists such as insertion, deletion, traversal, and searching are also described.

Data Structures : hashing (1)

Hashing is the process of converting a given key into another value. A hash function is used to generate the new value according to a mathematical algorithm. The result of a hash function is known as a hash value or simply, a hash.

heap Sort Algorithm

define the heap algorithm and the Max Heap
and Min Heap with example for each type and the insertion for new item .

Data Structure and Algorithms Hashing

The document discusses hashing techniques for storing and retrieving data from memory. It covers hash functions, hash tables, open addressing techniques like linear probing and quadratic probing, and closed hashing using separate chaining. Hashing maps keys to memory addresses using a hash function to store and find data independently of the number of items. Collisions may occur and different collision resolution methods are used like open addressing that resolves collisions by probing in the table or closed hashing that uses separate chaining with linked lists. The efficiency of hashing depends on factors like load factor and average number of probes.

Hashing Technique In Data Structures

This document discusses different searching methods like sequential, binary, and hashing. It defines searching as finding an element within a list. Sequential search searches lists sequentially until the element is found or the end is reached, with efficiency of O(n) in worst case. Binary search works on sorted arrays by eliminating half of remaining elements at each step, with efficiency of O(log n). Hashing maps keys to table positions using a hash function, allowing searches, inserts and deletes in O(1) time on average. Good hash functions uniformly distribute keys and generate different hashes for similar keys.

Hashing

Hashing is a technique used to uniquely identify objects by assigning each object a key, such as a student ID or book ID number. A hash function converts large keys into smaller keys that are used as indices in a hash table, allowing for fast lookup of objects in O(1) time. Collisions, where two different keys hash to the same index, are resolved using techniques like separate chaining or linear probing. Common applications of hashing include databases, caches, and object representation in programming languages.

Hashing PPT

This document discusses hashing and different techniques for implementing dictionaries using hashing. It begins by explaining that dictionaries store elements using keys to allow for quick lookups. It then discusses different data structures that can be used, focusing on hash tables. The document explains that hashing allows for constant-time lookups on average by using a hash function to map keys to table positions. It discusses collision resolution techniques like chaining, linear probing, and double hashing to handle collisions when the hash function maps multiple keys to the same position.

Hashing in datastructure

The document discusses hash tables and hashing techniques. It describes how hash tables use a hash function to map keys to positions in a table. Collisions can occur if multiple keys map to the same position. There are two main approaches to handling collisions - open hashing which stores collided items in linked lists, and closed hashing which uses techniques like linear probing to find alternate positions in the table. The document also discusses various hash functions and their properties, like minimizing collisions.

linked list in data structure

This document discusses data structures and linked lists. It provides definitions and examples of different types of linked lists, including:
- Single linked lists, which contain nodes with a data field and a link to the next node.
- Circular linked lists, where the last node links back to the first node, forming a loop.
- Doubly linked lists, where each node contains links to both the previous and next nodes.
- Operations on linked lists such as insertion, deletion, traversal, and searching are also described.

Data Structures : hashing (1)

Hashing is the process of converting a given key into another value. A hash function is used to generate the new value according to a mathematical algorithm. The result of a hash function is known as a hash value or simply, a hash.

heap Sort Algorithm

define the heap algorithm and the Max Heap
and Min Heap with example for each type and the insertion for new item .

Data Structure and Algorithms Hashing

The document discusses hashing techniques for storing and retrieving data from memory. It covers hash functions, hash tables, open addressing techniques like linear probing and quadratic probing, and closed hashing using separate chaining. Hashing maps keys to memory addresses using a hash function to store and find data independently of the number of items. Collisions may occur and different collision resolution methods are used like open addressing that resolves collisions by probing in the table or closed hashing that uses separate chaining with linked lists. The efficiency of hashing depends on factors like load factor and average number of probes.

Hash table in data structure and algorithm

The document discusses hash tables and their use for efficient data retrieval. It begins by comparing the time complexity of different data structures for searching, noting that hash tables provide constant time O(1) search. It then provides examples of using hash tables to store student records and complaints by number. Key aspects covered include hash functions mapping data to table indices, minimizing collisions, open and closed addressing for collisions, and linked lists or probing as solutions. Types of hash functions and their parameters are defined. The document aims to explain the core concepts of hashing, hash functions, hash tables and approaches for handling collisions.

Hashing

Hashing is a technique used to store and retrieve information quickly by mapping keys to values in a hash table using a hash function. Common hash functions include division, mid-square, and folding methods. Collision resolution techniques like chaining, linear probing, quadratic probing, and double hashing are used to handle collisions in the hash table. Hashing provides constant-time lookup and is widely used in applications like databases, dictionaries, and encryption.

Linear search-and-binary-search

This document provides an overview of linear search and binary search algorithms.
It explains that linear search sequentially searches through an array one element at a time to find a target value. It is simple to implement but has poor efficiency as the time scales linearly with the size of the input.
Binary search is more efficient by cutting the search space in half at each step. It works on a sorted array by comparing the target to the middle element and determining which half to search next. The time complexity of binary search is logarithmic rather than linear.

Graph traversals in Data Structures

Graph traversal techniques are used to search vertices in a graph and determine the order to visit vertices. There are two main techniques: breadth-first search (BFS) and depth-first search (DFS). BFS uses a queue and visits the nearest vertices first, producing a spanning tree. DFS uses a stack and visits vertices by going as deep as possible first, also producing a spanning tree. Both techniques involve marking visited vertices to avoid loops.

Binary Search - Design & Analysis of Algorithms

Binary search is an efficient algorithm for finding a target value within a sorted array. It works by repeatedly dividing the search range in half and checking the value at the midpoint. This eliminates about half of the remaining candidates in each step. The maximum number of comparisons needed is log n, where n is the number of elements. This makes binary search faster than linear search, which requires checking every element. The algorithm works by first finding the middle element, then checking if it matches the target. If not, it recursively searches either the lower or upper half depending on if the target is less than or greater than the middle element.

Chapter 12 ds

This document provides an overview of hash tables and collision resolution techniques for hash tables. It discusses separate chaining and open addressing as the two broad approaches for resolving collisions in hash tables. For separate chaining, items with the same hash are stored in linked lists. For open addressing, techniques like linear probing, quadratic probing and double hashing use arrays to resolve collisions by probing to different index locations. The document outlines common hashing functions, applications of hash tables, and situations where hash tables may not be suitable. It also includes examples and pseudocode.

Hashing

This document discusses hashing and hash tables. It begins by defining hashing as transforming a string into a shorter fixed-length value using a hash function. This value represents the original string and is used to index and retrieve items from a database faster than using the original value.
It then discusses hash tables, which use hashing to allow insertions, deletions and searches of items in constant average time. A hash table is an array containing items indexed by hash values between 0-TableSize-1 generated by a hash function. Common hash functions discussed include division, multiplication, truncation, folding and extraction methods.
The document concludes by discussing collision resolution methods for hash tables, including separate chaining which uses linked

sorting and its types

This document discusses different types of sorting algorithms. It describes internal sorting and external sorting, with internal sorting handling all data in memory and external sorting requiring external memory. Bubble sort, selection sort, and insertion sort are briefly explained as examples of sorting methods. Bubble sort works by comparing adjacent elements and swapping if out of order, selection sort finds the minimum element and selection sort inserts elements into the sorted position. Pseudocode and examples are provided for each algorithm.

Binary search

Binary search is an algorithm that finds the position of a target value within a sorted array. It works by recursively dividing the array range in half and searching only within the appropriate half. The time complexity is O(log n) in the average and worst cases and O(1) in the best case, making it very efficient for searching sorted data. However, it requires the list to be sorted for it to work.

Hashing algorithms and its uses

Hashing algorithms are used to access data in hash tables through a hash function that converts data into a hash value or key. This key is used to determine the position of data in the hash table, allowing for fast lookup. Collisions can occur if different data hashes to the same key, and are resolved through techniques like open addressing, chaining, or rehashing. Hashing provides efficient indexing and retrieval of data in many applications like databases, compilers, and blockchain.

Hashing and Hash Tables

The document provides an introduction to hashing techniques and their applications. It discusses hashing as a technique to distribute dataset entries across an array of buckets using a hash function. It then describes various hashing techniques like separate chaining and open addressing to resolve collisions. Some applications discussed include how Dropbox uses hashing to check for copyrighted content sharing and how subtree caching is used in symbolic regression.

B and B+ tree

This PPT is all about the Tree basic on fundamentals of B and B+ Tree with it's Various (Search,Insert and Delete) Operations performed on it and their Examples...

Lec 17 heap data structure

The document discusses heap data structures and their use in priority queues and heapsort. It defines a heap as a complete binary tree stored in an array. Each node stores a value, with the heap property being that a node's value is greater than or equal to its children's values (for a max heap). Algorithms like Max-Heapify, Build-Max-Heap, Heap-Extract-Max, and Heap-Increase-Key are presented to maintain the heap property during operations. Priority queues use heaps to efficiently retrieve the maximum element, while heapsort sorts an array by building a max heap and repeatedly extracting elements.

Hash table

This document discusses hashing techniques for implementing symbol tables. It begins by reviewing the motivation for symbol tables in compilers and describing the basic operations of search, insertion and deletion that a hash table aims to support efficiently. It then discusses direct addressing and its limitations when key ranges are large. The concept of a hash function is introduced to map keys to a smaller range to enable direct addressing. Collision resolution techniques of chaining and open addressing are covered. Analysis of expected costs for different operations on chaining hash tables is provided. Various hash functions are described including division and multiplication methods, and the importance of choosing a hash function to distribute keys uniformly is discussed. The document concludes by mentioning universal hashing as a technique to randomize the hash function

Hashing

Hashing is a technique for mapping data to array indices to allow for fast insertion and search operations in O(1) time on average. It works by applying a hash function to a key to obtain an array index, which may cause collisions that require resolution techniques like separate chaining or open addressing. Open addressing resolves collisions by probing alternative indices using functions like linear probing, quadratic probing, or double hashing to find the next available empty slot.

linear probing

Linear probing is a collision resolution technique for hash tables that uses open addressing. When a collision occurs by inserting a key-value pair, linear probing searches through consecutive table indices to find the next empty slot. This provides constant expected time for search, insertion, and deletion when using a random hash function. However, linear probing can cause clustering where many elements are stored near each other, degrading performance.

Quick sort-Data Structure

Quicksort is a divide and conquer sorting algorithm that works by partitioning an array around a pivot value. It then recursively sorts the sub-arrays on each side. The key steps are: 1) Choose a pivot element to split the array into left and right halves, with all elements on the left being less than the pivot and all on the right being greater; 2) Recursively quicksort the left and right halves; 3) Combine the now-sorted left and right halves into a fully sorted array. The example demonstrates quicksorting an array of 6 elements by repeatedly partitioning around a pivot until the entire array is sorted.

Binary Tree Traversal

This presentation is useful to study about data structure and topic is Binary Tree Traversal. This is also useful to make a presentation about Binary Tree Traversal.

Quick Sort

The document discusses sorting algorithms and randomized quicksort. It explains that quicksort is an efficient sorting algorithm that was developed by Tony Hoare in 1960. The quicksort algorithm works by picking a pivot element and reordering the array so that all smaller elements come before the pivot and larger elements come after. It then recursively applies this process to the subarrays. Randomized quicksort improves upon quicksort by choosing the pivot element randomly, making the expected performance of the algorithm good for any input.

Data structure - Graph

The document defines and describes various graph concepts and data structures used to represent graphs. It defines a graph as a collection of nodes and edges, and distinguishes between directed and undirected graphs. It then describes common graph terminology like adjacent/incident nodes, subgraphs, paths, cycles, connected/strongly connected components, trees, and degrees. Finally, it discusses two common ways to represent graphs - the adjacency matrix and adjacency list representations, noting their storage requirements and ability to add/remove nodes.

Hashing

Hashing is a technique used to store and retrieve data efficiently. It involves using a hash function to map keys to integers that are used as indexes in an array. This improves searching time from O(n) to O(1) on average. However, collisions can occur when different keys map to the same index. Collision resolution techniques like chaining and open addressing are used to handle collisions. Chaining resolves collisions by linking keys together in buckets, while open addressing resolves them by probing to find the next empty index. Both approaches allow basic dictionary operations like insertion and search to be performed in O(1) average time when load factors are low.

Coalesced hashing / Hash Coalescido

Coalesced hashing is a collision resolution strategy for hash tables that combines aspects of separate chaining and open addressing. When collisions occur, items are stored in the first empty bucket in the table rather than in a linked list, as in separate chaining. This limits the length of chains and reduces clustering compared to open addressing. An optimization is to restrict the hash function's output to part of the table, reserving the rest for collision buckets. Coalesced hashing provides efficient searching similar to separate chaining while using less memory than either separate chaining or open addressing.

Hash table in data structure and algorithm

The document discusses hash tables and their use for efficient data retrieval. It begins by comparing the time complexity of different data structures for searching, noting that hash tables provide constant time O(1) search. It then provides examples of using hash tables to store student records and complaints by number. Key aspects covered include hash functions mapping data to table indices, minimizing collisions, open and closed addressing for collisions, and linked lists or probing as solutions. Types of hash functions and their parameters are defined. The document aims to explain the core concepts of hashing, hash functions, hash tables and approaches for handling collisions.

Hashing

Hashing is a technique used to store and retrieve information quickly by mapping keys to values in a hash table using a hash function. Common hash functions include division, mid-square, and folding methods. Collision resolution techniques like chaining, linear probing, quadratic probing, and double hashing are used to handle collisions in the hash table. Hashing provides constant-time lookup and is widely used in applications like databases, dictionaries, and encryption.

Linear search-and-binary-search

This document provides an overview of linear search and binary search algorithms.
It explains that linear search sequentially searches through an array one element at a time to find a target value. It is simple to implement but has poor efficiency as the time scales linearly with the size of the input.
Binary search is more efficient by cutting the search space in half at each step. It works on a sorted array by comparing the target to the middle element and determining which half to search next. The time complexity of binary search is logarithmic rather than linear.

Graph traversals in Data Structures

Graph traversal techniques are used to search vertices in a graph and determine the order to visit vertices. There are two main techniques: breadth-first search (BFS) and depth-first search (DFS). BFS uses a queue and visits the nearest vertices first, producing a spanning tree. DFS uses a stack and visits vertices by going as deep as possible first, also producing a spanning tree. Both techniques involve marking visited vertices to avoid loops.

Binary Search - Design & Analysis of Algorithms

Binary search is an efficient algorithm for finding a target value within a sorted array. It works by repeatedly dividing the search range in half and checking the value at the midpoint. This eliminates about half of the remaining candidates in each step. The maximum number of comparisons needed is log n, where n is the number of elements. This makes binary search faster than linear search, which requires checking every element. The algorithm works by first finding the middle element, then checking if it matches the target. If not, it recursively searches either the lower or upper half depending on if the target is less than or greater than the middle element.

Chapter 12 ds

This document provides an overview of hash tables and collision resolution techniques for hash tables. It discusses separate chaining and open addressing as the two broad approaches for resolving collisions in hash tables. For separate chaining, items with the same hash are stored in linked lists. For open addressing, techniques like linear probing, quadratic probing and double hashing use arrays to resolve collisions by probing to different index locations. The document outlines common hashing functions, applications of hash tables, and situations where hash tables may not be suitable. It also includes examples and pseudocode.

Hashing

This document discusses hashing and hash tables. It begins by defining hashing as transforming a string into a shorter fixed-length value using a hash function. This value represents the original string and is used to index and retrieve items from a database faster than using the original value.
It then discusses hash tables, which use hashing to allow insertions, deletions and searches of items in constant average time. A hash table is an array containing items indexed by hash values between 0-TableSize-1 generated by a hash function. Common hash functions discussed include division, multiplication, truncation, folding and extraction methods.
The document concludes by discussing collision resolution methods for hash tables, including separate chaining which uses linked

sorting and its types

This document discusses different types of sorting algorithms. It describes internal sorting and external sorting, with internal sorting handling all data in memory and external sorting requiring external memory. Bubble sort, selection sort, and insertion sort are briefly explained as examples of sorting methods. Bubble sort works by comparing adjacent elements and swapping if out of order, selection sort finds the minimum element and selection sort inserts elements into the sorted position. Pseudocode and examples are provided for each algorithm.

Binary search

Binary search is an algorithm that finds the position of a target value within a sorted array. It works by recursively dividing the array range in half and searching only within the appropriate half. The time complexity is O(log n) in the average and worst cases and O(1) in the best case, making it very efficient for searching sorted data. However, it requires the list to be sorted for it to work.

Hashing algorithms and its uses

Hashing algorithms are used to access data in hash tables through a hash function that converts data into a hash value or key. This key is used to determine the position of data in the hash table, allowing for fast lookup. Collisions can occur if different data hashes to the same key, and are resolved through techniques like open addressing, chaining, or rehashing. Hashing provides efficient indexing and retrieval of data in many applications like databases, compilers, and blockchain.

Hashing and Hash Tables

The document provides an introduction to hashing techniques and their applications. It discusses hashing as a technique to distribute dataset entries across an array of buckets using a hash function. It then describes various hashing techniques like separate chaining and open addressing to resolve collisions. Some applications discussed include how Dropbox uses hashing to check for copyrighted content sharing and how subtree caching is used in symbolic regression.

B and B+ tree

This PPT is all about the Tree basic on fundamentals of B and B+ Tree with it's Various (Search,Insert and Delete) Operations performed on it and their Examples...

Lec 17 heap data structure

The document discusses heap data structures and their use in priority queues and heapsort. It defines a heap as a complete binary tree stored in an array. Each node stores a value, with the heap property being that a node's value is greater than or equal to its children's values (for a max heap). Algorithms like Max-Heapify, Build-Max-Heap, Heap-Extract-Max, and Heap-Increase-Key are presented to maintain the heap property during operations. Priority queues use heaps to efficiently retrieve the maximum element, while heapsort sorts an array by building a max heap and repeatedly extracting elements.

Hash table

This document discusses hashing techniques for implementing symbol tables. It begins by reviewing the motivation for symbol tables in compilers and describing the basic operations of search, insertion and deletion that a hash table aims to support efficiently. It then discusses direct addressing and its limitations when key ranges are large. The concept of a hash function is introduced to map keys to a smaller range to enable direct addressing. Collision resolution techniques of chaining and open addressing are covered. Analysis of expected costs for different operations on chaining hash tables is provided. Various hash functions are described including division and multiplication methods, and the importance of choosing a hash function to distribute keys uniformly is discussed. The document concludes by mentioning universal hashing as a technique to randomize the hash function

Hashing

Hashing is a technique for mapping data to array indices to allow for fast insertion and search operations in O(1) time on average. It works by applying a hash function to a key to obtain an array index, which may cause collisions that require resolution techniques like separate chaining or open addressing. Open addressing resolves collisions by probing alternative indices using functions like linear probing, quadratic probing, or double hashing to find the next available empty slot.

linear probing

Linear probing is a collision resolution technique for hash tables that uses open addressing. When a collision occurs by inserting a key-value pair, linear probing searches through consecutive table indices to find the next empty slot. This provides constant expected time for search, insertion, and deletion when using a random hash function. However, linear probing can cause clustering where many elements are stored near each other, degrading performance.

Quick sort-Data Structure

Quicksort is a divide and conquer sorting algorithm that works by partitioning an array around a pivot value. It then recursively sorts the sub-arrays on each side. The key steps are: 1) Choose a pivot element to split the array into left and right halves, with all elements on the left being less than the pivot and all on the right being greater; 2) Recursively quicksort the left and right halves; 3) Combine the now-sorted left and right halves into a fully sorted array. The example demonstrates quicksorting an array of 6 elements by repeatedly partitioning around a pivot until the entire array is sorted.

Binary Tree Traversal

This presentation is useful to study about data structure and topic is Binary Tree Traversal. This is also useful to make a presentation about Binary Tree Traversal.

Quick Sort

The document discusses sorting algorithms and randomized quicksort. It explains that quicksort is an efficient sorting algorithm that was developed by Tony Hoare in 1960. The quicksort algorithm works by picking a pivot element and reordering the array so that all smaller elements come before the pivot and larger elements come after. It then recursively applies this process to the subarrays. Randomized quicksort improves upon quicksort by choosing the pivot element randomly, making the expected performance of the algorithm good for any input.

Data structure - Graph

The document defines and describes various graph concepts and data structures used to represent graphs. It defines a graph as a collection of nodes and edges, and distinguishes between directed and undirected graphs. It then describes common graph terminology like adjacent/incident nodes, subgraphs, paths, cycles, connected/strongly connected components, trees, and degrees. Finally, it discusses two common ways to represent graphs - the adjacency matrix and adjacency list representations, noting their storage requirements and ability to add/remove nodes.

Hash table in data structure and algorithm

Hash table in data structure and algorithm

Hashing

Hashing

Linear search-and-binary-search

Linear search-and-binary-search

Graph traversals in Data Structures

Graph traversals in Data Structures

Binary Search - Design & Analysis of Algorithms

Binary Search - Design & Analysis of Algorithms

Chapter 12 ds

Chapter 12 ds

Hashing

Hashing

sorting and its types

sorting and its types

Binary search

Binary search

Hashing algorithms and its uses

Hashing algorithms and its uses

Hashing and Hash Tables

Hashing and Hash Tables

B and B+ tree

B and B+ tree

Lec 17 heap data structure

Lec 17 heap data structure

Hash table

Hash table

Hashing

Hashing

linear probing

linear probing

Quick sort-Data Structure

Quick sort-Data Structure

Binary Tree Traversal

Binary Tree Traversal

Quick Sort

Quick Sort

Data structure - Graph

Data structure - Graph

Hashing

Hashing is a technique used to store and retrieve data efficiently. It involves using a hash function to map keys to integers that are used as indexes in an array. This improves searching time from O(n) to O(1) on average. However, collisions can occur when different keys map to the same index. Collision resolution techniques like chaining and open addressing are used to handle collisions. Chaining resolves collisions by linking keys together in buckets, while open addressing resolves them by probing to find the next empty index. Both approaches allow basic dictionary operations like insertion and search to be performed in O(1) average time when load factors are low.

Coalesced hashing / Hash Coalescido

Coalesced hashing is a collision resolution strategy for hash tables that combines aspects of separate chaining and open addressing. When collisions occur, items are stored in the first empty bucket in the table rather than in a linked list, as in separate chaining. This limits the length of chains and reduces clustering compared to open addressing. An optimization is to restrict the hash function's output to part of the table, reserving the rest for collision buckets. Coalesced hashing provides efficient searching similar to separate chaining while using less memory than either separate chaining or open addressing.

08 Hash Tables

At the beginning, the number of elements in a set of numbers to be stored in a computer system used to be not so large or having a wide range. Then, using a
simple table T [0, 1, ..., m − 1]called, direct-address table, could be used to store those numbers. As the situation became more and more complex, and a new idea came to be:
Definition
An associative array, map, symbol table, or dictionary is an abstract data type composed of a collection of tuples {(key, value)}
This can bee seen in the example of dictionaries in any spoken language. The problem became more complex when the range of the possible values for the
keys at the tuples became unbounded. Therefore a new type of data structure is needed to avoid the sparsity problem in the data, the hash table.

Hashing Algorithm

This document discusses dictionaries and hashing techniques for implementing dictionaries. It describes dictionaries as data structures that map keys to values. The document then discusses using a direct access table to store key-value pairs, but notes this has problems with negative keys or large memory usage. It introduces hashing to map keys to table indices using a hash function, which can cause collisions. To handle collisions, the document proposes chaining where each index is a linked list of key-value pairs. Finally, it covers common hash functions and analyzing load factor and collision probability.

SEO Website Analysis - example report

This "real" example of a SEO / Search Engine Optimization Report identifies website topics of interest for high ranking results in the SERPS: ie - SITE Title, meta information(mainly description), PR, crawled date, indexed pages, amount of back links, 301 re-directs or not, use of analytic programs, Alexa, diggs, etc. Again - this analysis points out areas of interest when it comes to your WEBSITE search engine optimization Effectiveness.

Seo analysis report template (1)

The document is an SEO audit report that analyzes various aspects of a website's search engine optimization. It examines the domain information, hosting details, ability to be crawled, canonical issues, use of technologies like Flash, banned URLs, sitemaps, robots.txt file, status and indexing on search engines, on-page elements, keyword rankings, and social media presence. It then provides recommendations to improve the site's on-page and off-page SEO.

Hash tables

The document provides an overview of hash tables, including how they store key-value pairs using hashing to map keys to indexes. It discusses hashing functions and how they should be stable, uniform, efficient and possibly secure. It describes how hash tables handle collisions through open addressing or chaining and how items can be added, removed, searched for and enumerated from a hash table.

Html, CSS & Web Designing

A web designer creates presentations using coding languages like HTML and CSS that are delivered through browsers. They design the overall look and feel of a website and specify how pages are displayed. Web designers may work for companies, agencies, or freelance. They are responsible for graphics, layout, programming, and content. HTML is the basic language used to structure web pages using tags like <html> and <body>. CSS enhances HTML and is used to format text, backgrounds, borders, and more. CSS can be applied inline, with embedded stylesheets, or external linked stylesheets.

Data Structure & Algorithms | Computer Science

Data Structure is the specific method for sorting out the data in a system with the goal that it could be utilized efficiently. These can implement at least one specific abstract data types (ADT), which indicate the operations that can be performed on the data structure and the computational unpredictability of those operations. Copy the link given below and paste it in new browser window to get more information on Data Structure & Algorithms:- www.transtutors.com/homework-help/computer-science/data-structure-and-algorithms.aspx

Design & Analysis Of Algorithm

All Units Notes Question & Answer
created By Vijay kumar Sharma
MCA STUDENT (JAIPUR NATIONAL UNIVERSITY)

Web Development on Web Project Report

Web development on web was part of a project in the final year of Engineering to demonstrate the implementation and application of SaaS using Microsoft Silverlight.
The application facilitated creation of web pages without having a need to install any HTML editor based software.

E commerce project report

This project report discusses electronic commerce (e-commerce). It defines e-commerce as business conducted over electronic networks like the internet. The report explains how e-commerce allows companies to establish online market presence and provides advantages for both businesses and consumers. It outlines the scope of e-commerce and lists the 10 best e-commerce companies in India, concluding that e-commerce has evolved to enable faster transactions through advanced internet technologies.

Website analysis sample report

The document analyzes the website http://dmdhronaaz.com and identifies several positive and negative factors affecting its search engine optimization and rankings. Positives include good content and page structure, but negatives include a new domain registration, lack of relevant inbound links, missing social media integration, and low keyword density. The analysis provides recommendations to improve targeted keywords, on-page elements like meta tags and images, and off-page elements such as blog posts, videos and links to increase traffic.

Hashing

Hashing

Coalesced hashing / Hash Coalescido

Coalesced hashing / Hash Coalescido

08 Hash Tables

08 Hash Tables

Hashing Algorithm

Hashing Algorithm

SEO Website Analysis - example report

SEO Website Analysis - example report

Seo analysis report template (1)

Seo analysis report template (1)

Hash tables

Hash tables

Html, CSS & Web Designing

Html, CSS & Web Designing

Data Structure & Algorithms | Computer Science

Data Structure & Algorithms | Computer Science

Design & Analysis Of Algorithm

Design & Analysis Of Algorithm

Web Development on Web Project Report

Web Development on Web Project Report

E commerce project report

E commerce project report

Website analysis sample report

Website analysis sample report

Hashing

data structure notes for bca. only use for education purpose. it help u to understand data structure and its methods.

Hashing And Hashing Tables

Hashing is a technique to uniquely identify objects by assigning them keys via a hash function. This allows objects to be easily stored and retrieved from a hash table data structure. Collisions, where two objects have the same hash value, are resolved through techniques like separate chaining, linear probing, and double hashing. Hashing provides fast lookup times of O(1) and is widely used to implement caches, databases, associative arrays, and object storage in many programming languages.

Hashing techniques, Hashing function,Collision detection techniques

Hashing is a technique used to map data of arbitrary size to data of a fixed size. A hash table stores key-value pairs with the key being generated from a hash function. A good hash function uniformly distributes keys while minimizing collisions. Common hash functions include division, multiplication, and universal hashing. Collision resolution strategies like separate chaining and open addressing handle collisions by storing data in linked lists or probing for empty buckets. Hashing provides efficient average-case performance of O(1) for operations like insertion, search and deletion.

LECT 10, 11-DSALGO(Hashing).pdf

The document discusses different hashing techniques used to store and retrieve data in hash tables. It begins by motivating the need for hashing through the limitations of linear and binary search. It then defines hashing as a process to map keys of arbitrary size to fixed size values. Popular hash functions discussed include division, folding, and mid-square methods. The document also covers collision resolution techniques for hash tables, including open addressing methods like linear probing, quadratic probing and double hashing as well as separate chaining using linked lists.

Hashing

This document discusses hashing techniques used to store and retrieve data from hash tables. It begins by explaining why hashing is used and what a hash table is. It then describes four common hashing functions: division, multiplicative, mid square, and folded methods. The document finishes by covering techniques for resolving collisions in hash tables, including chaining, open addressing using linear probing, quadratic probing, double hashing, and rehashing.

Hash table

This document discusses hash tables, which are data structures that use a hash function to map keys to values in an array of buckets. Hash tables provide O(1) time performance for operations like insertion, search and deletion on average by distributing entries uniformly across the buckets. Collisions, where two keys hash to the same value, are resolved using techniques like separate chaining or open addressing. The document covers topics like choosing good hash functions, collision resolution methods, dynamic resizing, and applications of hash tables.

Unit viii searching and hashing

Unit – VIII discusses searching and hashing techniques. It describes linear and binary searching algorithms. Linear search has O(n) time complexity while binary search has O(log n) time complexity for sorted arrays. Hashing is also introduced as a technique to allow O(1) access time by mapping keys to array indices via a hash function. Separate chaining and open addressing like linear probing and quadratic probing are described as methods to handle collisions during hashing.

unit-1-dsa-hashing-2022_compressed-1-converted.pptx

1. Hashing is a technique that is used to map keys to values in a hash table using a hash function. It allows for fast lookup, insertion, and deletion of key-value pairs.
2. A hash table stores key-value pairs in an array. When looking up a value by its key, the hash function is applied to the key to get the index in the array where the value should be stored. Collisions can occur if two keys map to the same index.
3. Common collision resolution techniques include chaining, which stores multiple values at each index using linked lists, and open addressing techniques like linear probing, quadratic probing, and double hashing which search for the next available empty index.

introduction to trees,graphs,hashing

Trees are hierarchical data structures that consist of nodes connected by edges. They are used to store and access information efficiently. Binary trees are a type of tree where each node has at most two children. Graphs model relationships between objects using nodes connected by edges. Hash tables store key-value pairs and allow for very fast lookup, insertion, and deletion of data using hash functions, but collisions can decrease efficiency.

searching techniques.pptx

Searching is a fundamental operation in data structures and algorithms, and it involves locating a specific item within a collection of data. Various searching techniques exist, and the choice of which one to use depends on factors like the data structure, the nature of the data, and the efficiency requirements.

Unit 8 searching and hashing

1. The document discusses searching and hashing algorithms. It describes linear and binary searching techniques. Linear search has O(n) time complexity, while binary search has O(log n) time complexity for sorted arrays.
2. Hashing is described as a technique to allow O(1) access time by mapping keys to table indexes via a hash function. Separate chaining and open addressing are two common techniques for resolving collisions when different keys hash to the same index. Separate chaining uses linked lists at each table entry while open addressing probes for the next open slot.

Presentation.pptx

The document discusses hashing techniques and collision resolution methods for hash tables. It covers:
- Hashing maps keys of variable length to smaller fixed-length values using a hash function. Hash tables use hashing to efficiently store and retrieve key-value pairs.
- Collisions occur when two keys hash to the same value. Common collision resolution methods are separate chaining, where each slot points to a linked list, and open addressing techniques like linear probing and double hashing.
- Bucket hashing groups hash table slots into buckets to improve performance. Records are hashed to buckets and stored sequentially within buckets or in an overflow bucket if a bucket is full. This reduces disk accesses when the hash table is stored

11_hashtable-1.ppt. Data structure algorithm

Hash tables provide constant time insertion, deletion and search by using a hash function to map keys to indexes in an array. Collisions occur when different keys hash to the same index. Separate chaining resolves collisions by storing keys in linked lists at each index. Open addressing resolves collisions by probing to the next index using functions like linear probing. The load factor and choice of hash function impact performance.

Hashing In Data Structure Download PPT i

1. The document discusses hashing techniques for implementing dictionaries and search data structures, including separate chaining and closed hashing (linear probing, quadratic probing, and double hashing).
2. Separate chaining uses a linked list at each index to handle collisions, while closed hashing searches for empty slots using a collision resolution function.
3. Double hashing is described as the best closed hashing technique, as it uses a second hash function to spread keys out and avoid clustering completely.

Hashing .pptx

Hashing is a technique for storing data in an array-like structure that allows for fast lookup of data based on keys. It improves upon linear and binary search by avoiding the need to keep data sorted. Hashing works by using a hash function to map keys to array indices, with collisions resolved through techniques like separate chaining or open addressing. Separate chaining uses linked lists at each index while open addressing resolves collisions by probing to alternate indices like linear, quadratic, or double hashing.

4.4 hashing

This document discusses hashing and hash tables. It begins by introducing hash tables and describing how hashing works by mapping keys to array indices using a hash function to allow for fast insertion, deletion and search operations in O(1) average time. However, hash tables do not support ordering of elements efficiently. The document then discusses issues with hash functions such as collisions when different keys map to the same index. It describes techniques for collision resolution including separate chaining, where each index points to a linked list, and open addressing techniques like linear probing, quadratic probing and double hashing that resolve collisions by probing alternate indices in the array.

Chapter 4.pptx

This chapter discusses various data structures including elementary data structures like stacks, queues, linked lists, binary trees, and hash tables. It then describes more complex data structures like red-black trees that balance binary search trees to guarantee logarithmic time operations. It also discusses augmenting data structures by adding size fields to support dynamic order statistics queries in logarithmic time.

L21_Hashing.pdf

This document discusses hash tables and their implementation. It begins by explaining that dictionaries map keys to values like arrays but allow any type for the key. It then discusses how dictionaries can be implemented using balanced binary search trees or hash tables. Hash tables offer constant-time lookup by using a hash function to map keys to array indices, but collisions must be handled when two keys hash to the same index. Various collision resolution techniques are presented like separate chaining, linear probing, and quadratic probing. The document provides examples and guidelines for choosing a hash function and table size to minimize collisions.

Lecture14_15_Hashing.pptx

Hash tables are data structures that use a hash function to map keys to values. Hash functions map variable length keys to fixed length values to be used as indices in an array. Collisions occur when two keys map to the same index. Common collision resolution techniques include chaining, where a linked list is stored at each index, and open addressing, where probes are used to find the next available empty index. Double hashing is an open addressing technique where a second hash function is used to determine probe distances.

Hashing

Hashing

Hashing And Hashing Tables

Hashing And Hashing Tables

Hashing techniques, Hashing function,Collision detection techniques

Hashing techniques, Hashing function,Collision detection techniques

hashing in data strutures advanced in languae java

hashing in data strutures advanced in languae java

LECT 10, 11-DSALGO(Hashing).pdf

LECT 10, 11-DSALGO(Hashing).pdf

Hashing

Hashing

Hash table

Hash table

Unit viii searching and hashing

Unit viii searching and hashing

unit-1-dsa-hashing-2022_compressed-1-converted.pptx

unit-1-dsa-hashing-2022_compressed-1-converted.pptx

introduction to trees,graphs,hashing

introduction to trees,graphs,hashing

searching techniques.pptx

searching techniques.pptx

Unit 8 searching and hashing

Unit 8 searching and hashing

Presentation.pptx

Presentation.pptx

11_hashtable-1.ppt. Data structure algorithm

11_hashtable-1.ppt. Data structure algorithm

Hashing In Data Structure Download PPT i

Hashing In Data Structure Download PPT i

Hashing .pptx

Hashing .pptx

4.4 hashing

4.4 hashing

Chapter 4.pptx

Chapter 4.pptx

L21_Hashing.pdf

L21_Hashing.pdf

Lecture14_15_Hashing.pptx

Lecture14_15_Hashing.pptx

First and follow set

The document discusses the FIRST and FOLLOW sets used in compiler construction for predictive parsing. FIRST(X) is the set of terminals that can begin strings derived from X. FOLLOW(A) is the set of terminals that can immediately follow A. Rules are provided to compute the FIRST and FOLLOW sets for a grammar. Examples demonstrate applying the rules to sample grammars and presenting the resulting FIRST and FOLLOW sets.

TCP/IP and UDP protocols

This document discusses the TCP/IP and UDP protocols. It begins with an introduction comparing the TCP/IP model to the OSI model. The TCP/IP model has four layers compared to seven in the OSI model. It then describes the two main host-to-host layer protocols in TCP/IP - TCP and UDP. TCP is connection-oriented and provides reliable, ordered delivery. It uses segments with a header containing fields like sequence numbers. UDP is connectionless and provides fast but unreliable delivery. It uses simpler segments with fewer header fields. The document concludes by explaining the end-to-end delivery process for packets using these protocols as they are transmitted between hosts via routers.

Report writing

Complete and descriptive presentation on Report Writing. It contains both formal and informal report writing.
This presentation will surely help you alot in your preparation.

UML constructs

The document discusses various UML constructs including generalization, specialization, categories, aggregation, association, realization, and dependency. It defines each construct and provides examples. Generalization extracts common characteristics from classes into a superclass. Specialization creates subclasses from an existing class. Aggregation represents a "has-a" relationship where objects have their own lifetimes, while composition represents a stronger ownership where child objects rely on the parent object. The document compares different relationship types using examples.

Mathematical induction and divisibility rules

A complete and enhanced presentation on mathematical induction and divisibility rules with out any calculation.
Here are some defined formulas and techniques to find the divisibility of numbers.

Features of-RC-quad-copter

This document provides information about a remote control quad copter. It lists the copter's features such as a 6-axis gyroscope, Wi-Fi and GPS capabilities, and an HD camera. It describes the aircraft, propellers, remote controller, intelligent battery, and drone flight. Safety tips are provided for propeller handling, battery use, and flight. Troubleshooting advice addresses issues like unresponsiveness, inability to lift off, and drifting. Maintenance instructions involve regular cleaning to prevent dirt buildup on the rotors.

7 cs of communication

All about 7 C's of communication.. Details and better understand ability with the help of examples.

Geography of pakistan

Pakistan has a population of approximately 193 million as of 2015, making it the 6th most populous country. The population grew rapidly from 32 million in 1951 to over 143 million in 2000, though the growth rate has declined to around 1.6% currently. Punjab province has the largest population share at 57%, while Baluchistan has the smallest at 6%. Around 67% of Pakistanis live in rural areas, with 33% in urban centers, the largest of which are Karachi, Lahore, Faisalabad, and Rawalpindi.

FET (Field Effect Transistors)

This document discusses field effect transistors (FETs) and provides details about junction FETs (JFETs). It describes the basic structure and operation of N-channel and P-channel JFETs. Key points covered include:
- FETs are voltage-controlled, unipolar devices that come in two main types: JFETs and MOSFETs.
- A JFET has two P-type regions diffused into an N-type (N-channel JFET) or two N-type regions into a P-type (P-channel JFET) material to form a channel. Applying a voltage to the gate controls the channel.
- The characteristics of a

Cyber crime.pptx

The document discusses cyber crime and cyber security. It defines cyber crime as crimes committed using computers or online. It discusses the history and evolution of cyber crimes, common types like financial fraud and hacking, and percentages of different crimes. The document also covers cyber threats, security measures, and cyber laws in Pakistan. The National Response Center for Cyber Crime was formed to monitor cyber criminals and Pakistan passed the Prevention of Electronic Crimes Ordinance in 2007 to address cyber crimes, though laws are still being developed.

BJT’s (bipolar junction transistor)

The document discusses the structure and operation of bipolar junction transistors (BJTs). It describes the three doped semiconductor regions (emitter, base, collector) and how they are arranged in NPN and PNP transistors. It explains that forward biasing the base-emitter junction and reverse biasing the base-collector junction is necessary for transistor amplification. Current flow is described, with the collector current being a multiple of the base current depending on the DC beta ratio. Circuit diagrams and equations for current and voltage are provided.

First and follow set

First and follow set

TCP/IP and UDP protocols

TCP/IP and UDP protocols

Report writing

Report writing

UML constructs

UML constructs

Mathematical induction and divisibility rules

Mathematical induction and divisibility rules

Features of-RC-quad-copter

Features of-RC-quad-copter

7 cs of communication

7 cs of communication

Geography of pakistan

Geography of pakistan

FET (Field Effect Transistors)

FET (Field Effect Transistors)

Cyber crime.pptx

Cyber crime.pptx

BJT’s (bipolar junction transistor)

BJT’s (bipolar junction transistor)

Chapter 4 - Islamic Financial Institutions in Malaysia.pptx

Chapter 4 - Islamic Financial Institutions in Malaysia.pptxMohd Adib Abd Muin, Senior Lecturer at Universiti Utara Malaysia

This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
DRUGS AND ITS classification slide share

Any substance (other than food) that is used to prevent, diagnose, treat, or relieve symptoms of a
disease or abnormal condition

How to Setup Warehouse & Location in Odoo 17 Inventory

In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.

How to Build a Module in Odoo 17 Using the Scaffold Method

Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.

The Diamonds of 2023-2024 in the IGRA collection

A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.

বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf

বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...

BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...

BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...Nguyen Thanh Tu Collection

https://app.box.com/s/y977uz6bpd3af4qsebv7r9b7s21935vdSouth African Journal of Science: Writing with integrity workshop (2024)

South African Journal of Science: Writing with integrity workshop (2024)Academy of Science of South Africa

A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.World environment day ppt For 5 June 2024

for world environment day

How to Manage Your Lost Opportunities in Odoo 17 CRM

Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM

A Independência da América Espanhola LAPBOOK.pdf

Lapbook sobre independência da América Espanhola.

ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...

Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION

Liberal Approach to the Study of Indian Politics.pdf

The Best topic of my Interest.

Digital Artifact 1 - 10VCD Environments Unit

Digital Artifact 1 - 10VCD Environments Unit - NGV Pavilion Concept Design

Film vocab for eal 3 students: Australia the movie

film vocab esl

PIMS Job Advertisement 2024.pdf Islamabad

advasitment of Punjab

Life upper-Intermediate B2 Workbook for student

English B2 for student at intermediate level

Chapter 4 - Islamic Financial Institutions in Malaysia.pptx

Chapter 4 - Islamic Financial Institutions in Malaysia.pptx

DRUGS AND ITS classification slide share

DRUGS AND ITS classification slide share

How to Setup Warehouse & Location in Odoo 17 Inventory

How to Setup Warehouse & Location in Odoo 17 Inventory

How to Build a Module in Odoo 17 Using the Scaffold Method

How to Build a Module in Odoo 17 Using the Scaffold Method

The Diamonds of 2023-2024 in the IGRA collection

The Diamonds of 2023-2024 in the IGRA collection

বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf

বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf

BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...

BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...

South African Journal of Science: Writing with integrity workshop (2024)

South African Journal of Science: Writing with integrity workshop (2024)

World environment day ppt For 5 June 2024

World environment day ppt For 5 June 2024

How to Manage Your Lost Opportunities in Odoo 17 CRM

How to Manage Your Lost Opportunities in Odoo 17 CRM

The basics of sentences session 6pptx.pptx

The basics of sentences session 6pptx.pptx

A Independência da América Espanhola LAPBOOK.pdf

A Independência da América Espanhola LAPBOOK.pdf

ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...

ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...

Liberal Approach to the Study of Indian Politics.pdf

Liberal Approach to the Study of Indian Politics.pdf

Digital Artifact 1 - 10VCD Environments Unit

Digital Artifact 1 - 10VCD Environments Unit

Film vocab for eal 3 students: Australia the movie

Film vocab for eal 3 students: Australia the movie

Smart-Money for SMC traders good time and ICT

Smart-Money for SMC traders good time and ICT

PIMS Job Advertisement 2024.pdf Islamabad

PIMS Job Advertisement 2024.pdf Islamabad

MARY JANE WILSON, A “BOA MÃE” .

MARY JANE WILSON, A “BOA MÃE” .

Life upper-Intermediate B2 Workbook for student

Life upper-Intermediate B2 Workbook for student

- 2. HASHING
- 3. UNIVERSITY OF AZAD JAMMU & KASHMIR MUZAFFARABAD DEPARTMENT OF CS&IT DATA STRUCTURE • Dawood Faheem Abbasi BSCS-III-05
- 4. OUTLINE1. Searching methods – Linear search – Binary search – Hashing • Introduction • Dictionary • Hash table • Working of hash table • Hash functions – Division method – Mid square method – Folding method • Algorithm • Characteristics of good hash function • Collision • Collision resolution – Separate chaining – Closed hashing • Applications of hashing
- 5. SEARCHING METHODS • Sequential or Linear Searching. • Binary Search. • Hashing.
- 6. SEQUENTIAL SEARCH • Searches on unordered and ordered tables in sequential manner until the desired record is not found or table end is not reached. • It is simple and good for small arrays. • Mostly used when data is not sorted. • Less efficient if array size is large. • Consumes a lot of time as compared to other techniques. • Not efficient on sorted arrays.
- 7. BINARY SORT • This technique works better on sorted arrays and can be applied only on sorted arrays. • Not applied on Linked Lists. • Requires less number of comparisons than linear search. • Efficiency: O(log2n). • Logic behind the technique: Second HalfFirst Half mid=(low+high)/2 Last ValueFirst Value
- 8. EFFICIENCY: • Best – O(1) Constant time • Average – O(n/2) Here, n represents the number of elements in array • Worst – O(n) Here, n represents the number of elements in array Apply NowApply NowApply Now
- 9. ILLUSTRATION OF ZIP FILE
- 10. HASHING
- 11. INTRODUCTION Hashing is the transformation of long strings or information into a usually shorter fixed-length value or key that represents the original string or information. Hashing is used to index and retrieve items in a database because it is faster to find the item using the shorter hashed key than to find it using the original value. It is also used in many encryption algorithms. A table of records in which a key is used for retrieval is often called a SEARCH TABLE or DICTIONARY.
- 12. DICTIONARY • A dictionary is a collection of elements • Each element has a field called key – (key, value) • Every key is usually distinct. • Typical dictionary operations are: – Insert a pair into the dictionary – Search the pair with a specified key – Delete the pair with a specified key
- 13. HASH TABLE • A hash table is a data structure that stores elements and allows insertions, lookups, and deletions to be performed in O(1) time. • A hash table is an alternative method for representing a dictionary • In a hash table, a hash function is used to map keys into positions in a table. This act is called hashing • Hash Table Operations – Search: compute f(k) and see if a pair exists – Insert: compute f(k) and place it in that position – Delete: compute f(k) and delete the pair in that position • In ideal situation, hash table search, insert or delete takes (1)
- 14. WORKING OF HASH TABLE • The table part is just an ordinary array, it is the Hash that we are interested in. • The Hash is a function that transforms a key into address or index of array(table) where the record will be stored. If the size of the table is N, then the integer will be in the range 0 to N-1. The integer is used as an index into the array. Thus, in essence, the key itself indexes the array. • If h is a hash function and k is key then h(k) is called the hash of the key and is the index at which a record with the key k should be placed. • The hash function generates this address by performing some simple arithmetic or logical operations on the key
- 15. HASH FUNCTIONS • It Is the technique of using key to determine the address of a record so that no space is wasted. • Division method or function • Mid-square method or function • Folding method or function
- 16. DIVISION METHOD • Division method computes the value of Hash as the remainder when the key is divided by a specified prime number or it can be the size of the table. • The hash function is defined as H(k)=K( mod m) Where K is the key and m is a prime number or size of the table.
- 18. MIDSQUARE METHOD • In this method we squared the KEY and then the HASH FUNCTION is defined as: H(K)=I Where I is the value which we obtain by deleting equal digits from both sides ok K(square).
- 19. EXAMPLE • Followings are some keys k= 4012 8090 K (sqr)= 16096144 65448100 H( k)= 96 48
- 20. FOLDING METHOD • In this method we divide the key value in two parts. And then we add them. FOR EXAMPLE: K= 4012 1027 40+12=52 10+27=37 J=52 J=37 Here J is the value which we obtain by adding the KEY value.
- 21. ALGORITHM • Our goal in choosing any hashing algorithm is to spread out the record as uniformly as possible over the range of addresses available • It has three steps • Represent the key in any numerical form • Fold and add • Divide by the size of the address space and use the reminder as an address.
- 22. EXAMPLE • Pairs are: (22,a),(33,c),(3,d),(72,e),(85,f)--(key, value) pairs • Hash table is ht[0:7], m = 8 (where m is the number of positions in the hash table) • Hash function h is k % m = k % 8 • Where are the pairs stored? [0] [1] [2] [3] [4] [5] [6] [7] [0] [1] [2] [3] [4] [5] [6] [7] (72,e) (33,c) (85,f) (22,a)(3,d)
- 23. CHARACTERISTICS OF GOOD HASH FUNCTION The hash value is fully determined by the data being hashed. The hash function uses all the input data. The hash function "uniformly" distributes the data across the entire set of possible hash values. The hash function generates very different hash values for similar strings.
- 24. COLLISIONS AND THEIR RESOLUTION • A collision occurs when two different keys hash to the same value – E.g. For Table Size = 17, the keys 18 and 35 hash to the same value – 18 mod 17 = 1 and 35 mod 17 = 1 • Cannot store both data records in the same slot in array!
- 25. COLLISION RESOLUTION A method that is used to resolve the collision in Hash function is called Collision Resolution. • Two different methods for collision resolution: – Separate Chaining: Use a dictionary data structure (such as a linked list) to store multiple items that hash to the same slot – Closed Hashing (or probing): search for empty slots using a second function and store item in first empty slot that is found
- 26. SEPARATE CHAINING • Put a little dictionary at each entry – choose type as appropriate – common case is unordered linked list (chain) • Properties – performance degrades with length of chains – can be greater than 1 3 2 1 0 6 5 4 a d e b c h(a) = h(d) h(e) = h(b)
- 27. CLOSED HASHING Problem with separate chaining: Memory consumed by pointers – What if we only allow one Key at each entry? – two objects that hash to the same spot can’t both go there – first one there gets the spot – next one must go in another spot • Properties – performance degrades with difficulty of finding right spot a c e 3 2 1 0 6 5 4 h(a) = h(d) h(e) = h(b) d b
- 28. APPLICATIONS • File management- working out where to store records • Comparing complex values • Cryptography- creating digital signatures and passwords. • Dictionaries • Security systems
- 29. THANK YOUT A K E A S M I L E , T H E Y A R E F R E E