Binary trees are a non-linear data structure where each node has zero, one, or two child nodes. They are commonly used to represent hierarchical relationships. A binary tree has a root node at the top with child nodes below it. Binary trees can be empty or consist of a root node and left and right subtrees, which are also binary trees. They allow for efficient search, insert, and delete operations and can be represented using arrays or linked lists.
A threaded binary tree is a variant of a binary tree that stores in-order predecessor and successor pointers instead of null pointers to facilitate faster traversal without recursion or an auxiliary stack. There are two types: single-threaded stores successor pointers only, and double-threaded stores both predecessor and successor pointers. This decreases wasted space from null pointers but makes insertion and deletion more complex. Advantages include optimal memory usage and faster traversal, while disadvantages include more complex insertion/deletion and slightly more memory usage for thread indicators.
B+ tree is a data structure used to store (key, value) pairs in a tree-like structure for fast retrieval. It has a root node, intermediary nodes that point to leaf nodes but do not store data, and leaf nodes that store the actual records. Leaf nodes must contain a minimum and maximum number of values, and non-leaf nodes a minimum and maximum number of child nodes. B+ trees allow fast traversal and search via balanced structure and sorted nodes. Records are inserted and deleted by searching the tree to the appropriate leaf node and updating pointers and values.
The document discusses various indexing techniques used to improve data access performance in databases, including ordered indices like B-trees and B+-trees, as well as hashing techniques. It covers the basic concepts, data structures, operations, advantages and disadvantages of each approach. B-trees and B+-trees store index entries in sorted order to support range queries efficiently, while hashing distributes entries uniformly across buckets using a hash function but does not support ranges.
- A red-black tree is a self-balancing binary search tree where each node is colored red or black. It maintains the properties that the black height of each path is equal and there are no adjacent red nodes, ensuring O(log n) time for operations.
- Common operations like insertion, deletion, and searching on a red-black tree work similarly to a binary search tree but may require rotations and color changes to maintain the red-black properties.
- Red-black trees are widely used in applications that require efficient search structures for frequently updated data like process memory management and functional programming data structures.
An M-way tree is a search tree where each node can have between 0 and m subtrees, where m is the tree order. Each node contains either 0 to m subtrees and k-1 data entries, where k is the number of subtrees. Keys in the first subtree are less than the first data entry, and keys in other subtrees are greater than or equal to their parent entry. B-trees are a type of M-way search tree that maintain a minimum and maximum number of children per node to ensure balance.
Polynomial reppresentation using Linkedlist-Application of LL.pptxAlbin562191
Linked lists are useful for dynamic memory allocation and polynomial manipulation. They allow for efficient insertion and deletion by changing only pointers, unlike arrays which require shifting elements. Linked lists can represent polynomials by storing coefficient, exponent, and link fields in each node. Polynomial addition using linked lists involves traversing both lists simultaneously and adding coefficients of matching exponents or duplicating unmatched terms into the new list.
The document discusses B+ trees, which are self-balancing search trees used to store data in databases. It defines B+ trees, provides examples, and explains how to perform common operations like searching, insertion, and deletion on B+ trees in a way that maintains the tree's balanced structure. Key aspects are that B+ trees allow fast searching, maintain balance during operations, and improve performance over other tree structures for large databases.
The document discusses binary search trees (BSTs). It defines BSTs as data structures that allow maintaining a sorted list of numbers in a way that allows searching in O(log n) time. The key properties of BSTs are that the left subtree of a node contains only nodes with values smaller than the node, and the right subtree contains only larger values. The document outlines common BST operations like searching, insertion, and deletion and explains how they make use of the BST properties to achieve efficient O(log n) time complexity on average. Examples are provided to illustrate each operation. Applications of BSTs include indexing for databases and dynamic sorting.
Heap Sort in Design and Analysis of algorithmssamairaakram
Brief description of Heap Sort and its types.it includes Binary Tree and its types. analysis and algorithm of Heap Sort. comparison b/w Heap,Qucik and Merge Sort.
The document discusses query optimization by describing how a database system estimates the cost of different query evaluation plans using statistical information about relations. It covers topics like estimating the size of selections, joins, aggregations and other operations to choose the lowest cost plan using transformations and equivalence rules.
This document discusses different types of linked lists, including singly-linked lists where each node has a link to the next node, doubly-linked lists where each node has links to both the next and previous nodes, and circularly-linked lists where the first and last nodes are linked together. It also describes the basic node structure that makes up a linked list, with a data field and link to another node.
This PPT is about "Double Linked List" concept of Data Structure and Algorithm. It is very informatively describe its each concept with example and also pros and cons..
If anyone has Question related to this they may ask freely.
:)
- Threaded binary trees utilize empty link fields in standard binary trees to store additional pointers called "threads", improving certain operations.
- There are three types of threaded binary trees corresponding to inorder, preorder, and postorder traversals. This document focuses on inorder threaded binary trees.
- In an inorder threaded binary tree, if a node's right link is empty, it points to the inorder successor via a thread rather than being empty. This allows faster traversal and efficient determination of predecessors and successors.
This document discusses active database management systems. It defines active databases as database systems that can automatically respond to events inside or outside the system through the use of event-condition-action rules. These rules allow the database to monitor and react to specific events. The document outlines the key components of an active database architecture, including a knowledge model and execution model. It also discusses features, applications, strengths and weaknesses of active databases.
Abstract data types (ADTs) specify operations on data without defining implementation. Common ADTs include sets, lists, stacks, queues, maps, trees and graphs. Sets contain unique elements; lists are ordered; stacks use last-in first-out access; queues use first-in first-out access; maps store key-value pairs; trees link parent nodes to children; and graphs link vertices with edges. Core operations for collections include adding, removing and accessing elements.
The document discusses measures of query cost in database management systems. It explains that query cost can be measured by factors like the number of disk accesses, size of the table, and time taken by the CPU. It further breaks down disk access time into components like seek time, rotational latency, and sequential vs. random I/O. The document then provides an example formula to calculate estimated query cost based on these components.
Presentation On Binary Search Tree using Linked List Concept which includes Traversing the tree in Inorder, Preorder and Postorder Methods and also searching the element in the Tree
BCA DATA STRUCTURES SEARCHING AND SORTING MRS.SOWMYA JYOTHISowmya Jyothi
1. The document discusses various searching and sorting algorithms. It describes linear search, which compares each element to find a match, and binary search, which eliminates half the elements after each comparison in a sorted array.
2. It also explains bubble sort, which bubbles larger values up and smaller values down through multiple passes. Radix sort sorts elements based on individual digits or characters.
3. Selection sort and merge sort are also summarized. Merge sort divides the array into single elements and then merges the sorted sublists, while selection sort finds the minimum element and swaps it into place in each pass.
linear search and binary search, Class lecture of Data Structure and Algorithms and Python.
Stack, Queue, Tree, Python, Python Code, Computer Science, Data, Data Analysis, Machine Learning, Artificial Intellegence, Deep Learning, Programming, Information Technology, Psuedocide, Tree, pseudocode, Binary Tree, Binary Search Tree, implementation, Binary search, linear search, Binary search operation, real-life example of binary search, linear search operation, real-life example of linear search, example bubble sort, sorting, insertion sort example, stack implementation, queue implementation, binary tree implementation, priority queue, binary heap, binary heap implementation, object-oriented programming, def, in BST, Binary search tree, Red-Black tree, Splay Tree, Problem-solving using Binary tree, problem-solving using BST, inorder, preorder, postorder
1) Databases organize and store data efficiently using a storage hierarchy including cache, main memory, magnetic disks, optical disks, and tapes. Magnetic disks are commonly used secondary storage.
2) Indexing and file structures like B+ trees are data structures that allow efficient retrieval of records from database files based on indexed attributes. B+ trees in particular provide fast traversal and searching through a balanced tree structure.
3) RAID (Redundant Array of Independent Disks) uses multiple disks together to provide increased performance, redundancy, or both through techniques like disk striping and mirroring.
Binary search trees (BSTs) are data structures that allow for efficient searching, insertion, and deletion. Nodes in a BST are organized so that all left descendants of a node are less than the node's value and all right descendants are greater. This property allows values to be found, inserted, or deleted in O(log n) time on average. Searching involves recursively checking if the target value is less than or greater than the current node's value. Insertion follows the search process and adds the new node in the appropriate place. Deletion handles three cases: removing a leaf, node with one child, or node with two children.
This document discusses binary search trees (BST), including:
- The definition and properties of a BST, with nodes organized based on key values.
- Common BST operations like insertion, deletion, and searching in logarithmic time.
- Advantages of BSTs like efficient searching and working compared to arrays/lists.
- Applications of BSTs in databases, dictionaries, and evaluating expressions.
- Visual examples of searching, inserting, and deleting nodes from a BST.
This document discusses different types of linked lists, including singly linked lists, circular linked lists, and doubly linked lists. It provides examples of circular linked lists and explains that in a circular linked list, the last node contains the address of the first node so that the list can be traversed repeatedly. The document also discusses common linked list operations like insertion and deletion of nodes. Finally, it covers stacks and queues, defining them as LIFO and FIFO data structures, respectively, and providing examples of their implementation and different types.
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.
The document discusses B+ trees, which are self-balancing search trees used to store data in databases. It defines B+ trees, provides examples, and explains how to perform common operations like searching, insertion, and deletion on B+ trees in a way that maintains the tree's balanced structure. Key aspects are that B+ trees allow fast searching, maintain balance during operations, and improve performance over other tree structures for large databases.
The document discusses binary search trees (BSTs). It defines BSTs as data structures that allow maintaining a sorted list of numbers in a way that allows searching in O(log n) time. The key properties of BSTs are that the left subtree of a node contains only nodes with values smaller than the node, and the right subtree contains only larger values. The document outlines common BST operations like searching, insertion, and deletion and explains how they make use of the BST properties to achieve efficient O(log n) time complexity on average. Examples are provided to illustrate each operation. Applications of BSTs include indexing for databases and dynamic sorting.
Heap Sort in Design and Analysis of algorithmssamairaakram
Brief description of Heap Sort and its types.it includes Binary Tree and its types. analysis and algorithm of Heap Sort. comparison b/w Heap,Qucik and Merge Sort.
The document discusses query optimization by describing how a database system estimates the cost of different query evaluation plans using statistical information about relations. It covers topics like estimating the size of selections, joins, aggregations and other operations to choose the lowest cost plan using transformations and equivalence rules.
This document discusses different types of linked lists, including singly-linked lists where each node has a link to the next node, doubly-linked lists where each node has links to both the next and previous nodes, and circularly-linked lists where the first and last nodes are linked together. It also describes the basic node structure that makes up a linked list, with a data field and link to another node.
This PPT is about "Double Linked List" concept of Data Structure and Algorithm. It is very informatively describe its each concept with example and also pros and cons..
If anyone has Question related to this they may ask freely.
:)
- Threaded binary trees utilize empty link fields in standard binary trees to store additional pointers called "threads", improving certain operations.
- There are three types of threaded binary trees corresponding to inorder, preorder, and postorder traversals. This document focuses on inorder threaded binary trees.
- In an inorder threaded binary tree, if a node's right link is empty, it points to the inorder successor via a thread rather than being empty. This allows faster traversal and efficient determination of predecessors and successors.
This document discusses active database management systems. It defines active databases as database systems that can automatically respond to events inside or outside the system through the use of event-condition-action rules. These rules allow the database to monitor and react to specific events. The document outlines the key components of an active database architecture, including a knowledge model and execution model. It also discusses features, applications, strengths and weaknesses of active databases.
Abstract data types (ADTs) specify operations on data without defining implementation. Common ADTs include sets, lists, stacks, queues, maps, trees and graphs. Sets contain unique elements; lists are ordered; stacks use last-in first-out access; queues use first-in first-out access; maps store key-value pairs; trees link parent nodes to children; and graphs link vertices with edges. Core operations for collections include adding, removing and accessing elements.
The document discusses measures of query cost in database management systems. It explains that query cost can be measured by factors like the number of disk accesses, size of the table, and time taken by the CPU. It further breaks down disk access time into components like seek time, rotational latency, and sequential vs. random I/O. The document then provides an example formula to calculate estimated query cost based on these components.
Presentation On Binary Search Tree using Linked List Concept which includes Traversing the tree in Inorder, Preorder and Postorder Methods and also searching the element in the Tree
BCA DATA STRUCTURES SEARCHING AND SORTING MRS.SOWMYA JYOTHISowmya Jyothi
1. The document discusses various searching and sorting algorithms. It describes linear search, which compares each element to find a match, and binary search, which eliminates half the elements after each comparison in a sorted array.
2. It also explains bubble sort, which bubbles larger values up and smaller values down through multiple passes. Radix sort sorts elements based on individual digits or characters.
3. Selection sort and merge sort are also summarized. Merge sort divides the array into single elements and then merges the sorted sublists, while selection sort finds the minimum element and swaps it into place in each pass.
linear search and binary search, Class lecture of Data Structure and Algorithms and Python.
Stack, Queue, Tree, Python, Python Code, Computer Science, Data, Data Analysis, Machine Learning, Artificial Intellegence, Deep Learning, Programming, Information Technology, Psuedocide, Tree, pseudocode, Binary Tree, Binary Search Tree, implementation, Binary search, linear search, Binary search operation, real-life example of binary search, linear search operation, real-life example of linear search, example bubble sort, sorting, insertion sort example, stack implementation, queue implementation, binary tree implementation, priority queue, binary heap, binary heap implementation, object-oriented programming, def, in BST, Binary search tree, Red-Black tree, Splay Tree, Problem-solving using Binary tree, problem-solving using BST, inorder, preorder, postorder
1) Databases organize and store data efficiently using a storage hierarchy including cache, main memory, magnetic disks, optical disks, and tapes. Magnetic disks are commonly used secondary storage.
2) Indexing and file structures like B+ trees are data structures that allow efficient retrieval of records from database files based on indexed attributes. B+ trees in particular provide fast traversal and searching through a balanced tree structure.
3) RAID (Redundant Array of Independent Disks) uses multiple disks together to provide increased performance, redundancy, or both through techniques like disk striping and mirroring.
Binary search trees (BSTs) are data structures that allow for efficient searching, insertion, and deletion. Nodes in a BST are organized so that all left descendants of a node are less than the node's value and all right descendants are greater. This property allows values to be found, inserted, or deleted in O(log n) time on average. Searching involves recursively checking if the target value is less than or greater than the current node's value. Insertion follows the search process and adds the new node in the appropriate place. Deletion handles three cases: removing a leaf, node with one child, or node with two children.
This document discusses binary search trees (BST), including:
- The definition and properties of a BST, with nodes organized based on key values.
- Common BST operations like insertion, deletion, and searching in logarithmic time.
- Advantages of BSTs like efficient searching and working compared to arrays/lists.
- Applications of BSTs in databases, dictionaries, and evaluating expressions.
- Visual examples of searching, inserting, and deleting nodes from a BST.
This document discusses different types of linked lists, including singly linked lists, circular linked lists, and doubly linked lists. It provides examples of circular linked lists and explains that in a circular linked list, the last node contains the address of the first node so that the list can be traversed repeatedly. The document also discusses common linked list operations like insertion and deletion of nodes. Finally, it covers stacks and queues, defining them as LIFO and FIFO data structures, respectively, and providing examples of their implementation and different types.
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.
The document discusses binary search trees (BSTs). It defines a BST as a binary tree where the value of each left node is smaller than its parent and the value of each right node is larger. BSTs allow for faster insertion and deletion than arrays or linked lists. The key operations on a BST are search, insert, and delete. An example is provided to create a BST from a sample data set and demonstrate these operations.
Binary search trees are binary trees where all left descendants of a node are less than the node's value and all right descendants are greater. This structure allows for efficient search, insertion, and deletion operations. The document provides definitions and examples of binary search tree properties and operations like creation, traversal, searching, insertion, deletion, and finding minimum and maximum values. Applications include dynamically maintaining a sorted dataset to enable efficient search, insertion, and deletion.
This document discusses various data structures and concepts related to databases. It begins by providing an analogy comparing a bike engine and gears to a database engine. It then discusses sequential search and binary search algorithms that databases can use. Binary trees and B-trees are introduced as common tree data structures used in databases. The document concludes by defining indexes in databases and their purpose in improving data retrieval speed.
Search tree,Tree and binary tree and heap treezia eagle
This document contains definitions and explanations of search trees and heaps. It defines search trees as data structures used for locating specific keys within a set where each node's key must be greater than keys in its left subtree and less than keys in its right subtree. It provides examples of constructing and performing operations on binary search trees like insertion, searching, and deletion. It defines heaps as binary trees that follow the shape and order property, with max heaps having each node greater than its children and min heaps having each node less than its children. It explains representing heaps in arrays and operations like deleting and inserting elements through reheapifying.
This document describes binary trees, including their definition, applications, operations like insertion, deletion and traversal. Binary trees store data in nodes that can have up to two child nodes. Common operations include inserting nodes, searching for values, and traversing the tree in different orders like inorder, preorder and postorder. The document provides code examples for implementing a binary tree class including functions for these operations.
A binary search tree (BST) is a binary tree where the key in each internal node is greater than or equal to any key in its left subtree and less than the key in its right subtree. The document defines BSTs and describes their basic operations like searching, inserting, deleting, and traversing nodes. It provides algorithms for each operation, such as comparing the search key to the root node then recursively searching left or right subtrees, inserting new nodes at leaf locations, and traversing trees in pre-order, in-order, and post-order sequences.
The document discusses binary search trees (BSTs), which are trees that maintain the property that the key in each internal node is greater than or equal to all keys in its left subtree and less than or equal to all keys in its right subtree. This divides the tree into ordered segments. The basic operations on a BST include searching for a node by key, inserting a new node, and traversing the tree in various orders. Searching and inserting involve recursively searching the tree by comparing keys to determine which subtree to explore next.
This document discusses linear search and binary search algorithms. Linear search sequentially checks each element of an unsorted array to find a target value, resulting in O(n) time complexity. Binary search works on a sorted array, comparing the target to the middle element and recursively searching half the array, requiring O(log n) time. The document provides pseudocode for both algorithms and compares their performance on different sized inputs. It also discusses properties of greedy algorithms and provides an example of when a greedy solution fails to find the optimal result.
This document discusses binary trees and binary search trees. It defines key terminology for trees like root, leaf, edge. It explains that a binary tree can have at most two children per node and describes traversal methods for binary trees like preorder, inorder and postorder. The document also covers constructing a binary search tree from a sorted array, inserting and deleting nodes, and applications of binary trees like file systems and network routing.
How to Clean Your Contacts Using the Deduplication Menu in Odoo 18Celine George
In this slide, we’ll discuss on how to clean your contacts using the Deduplication Menu in Odoo 18. Maintaining a clean and organized contact database is essential for effective business operations.
Struggling with your botany assignments? This comprehensive guide is designed to support college students in mastering key concepts of plant biology. Whether you're dealing with plant anatomy, physiology, ecology, or taxonomy, this guide offers helpful explanations, study tips, and insights into how assignment help services can make learning more effective and stress-free.
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How to Configure Public Holidays & Mandatory Days in Odoo 18Celine George
In this slide, we’ll explore the steps to set up and manage Public Holidays and Mandatory Days in Odoo 18 effectively. Managing Public Holidays and Mandatory Days is essential for maintaining an organized and compliant work schedule in any organization.
How to Create Kanban View in Odoo 18 - Odoo SlidesCeline George
The Kanban view in Odoo is a visual interface that organizes records into cards across columns, representing different stages of a process. It is used to manage tasks, workflows, or any categorized data, allowing users to easily track progress by moving cards between stages.
*"Sensing the World: Insect Sensory Systems"*Arshad Shaikh
Insects' major sensory organs include compound eyes for vision, antennae for smell, taste, and touch, and ocelli for light detection, enabling navigation, food detection, and communication.
How to Manage Upselling in Odoo 18 SalesCeline George
In this slide, we’ll discuss on how to manage upselling in Odoo 18 Sales module. Upselling in Odoo is a powerful sales technique that allows you to increase the average order value by suggesting additional or more premium products or services to your customers.
*"The Segmented Blueprint: Unlocking Insect Body Architecture"*.pptxArshad Shaikh
Insects have a segmented body plan, typically divided into three main parts: the head, thorax, and abdomen. The head contains sensory organs and mouthparts, the thorax bears wings and legs, and the abdomen houses digestive and reproductive organs. This segmentation allows for specialized functions and efficient body organization.
Redesigning Education as a Cognitive Ecosystem: Practical Insights into Emerg...Leonel Morgado
Slides used at the Invited Talk at the Harvard - Education University of Hong Kong - Stanford Joint Symposium, "Emerging Technologies and Future Talents", 2025-05-10, Hong Kong, China.
Ancient Stone Sculptures of India: As a Source of Indian HistoryVirag Sontakke
This Presentation is prepared for Graduate Students. A presentation that provides basic information about the topic. Students should seek further information from the recommended books and articles. This presentation is only for students and purely for academic purposes. I took/copied the pictures/maps included in the presentation are from the internet. The presenter is thankful to them and herewith courtesy is given to all. This presentation is only for academic purposes.
Transform tomorrow: Master benefits analysis with Gen AI today webinar
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2. What is a Binary Search Tree?
• The binary search tree is an algorithm used for analyzing the node, its
left and right branches, which are modeled in a tree structure and
returning the value.
• The BST is devised on the architecture of a basic binary search
algorithm; hence it enables faster lookups, insertions, and removals of
nodes.
• This makes the program really fast and accurate.
3. • A BST is made of multiple nodes and consists of the following attributes:
• Nodes of the tree are represented in a parent-child relationship
• Each parent node can have zero child nodes or a maximum of two subnodes.
• Every sub-tree, also known as a binary search tree, has sub-branches on the
right and left of themselves.
• The keys of the nodes present on the left subtree are smaller than the keys of
their parent node
5. • BST is commonly utilized to implement complex searches, robust
game logics, auto-complete activities, and graphics.
• The algorithm efficiently supports operations like search, insert, and
delete.
6. • BST primarily offers the following three types of operations for your
usage:
• Search: searches the element from the binary tree
• Insert: adds an element to the binary tree
• Delete: delete the element from a binary tree
7. Search Operation
• Always start analyzing tree at the root node.
• Then move further to either the right or left subtree of the root node
depending on the element to be located is either less or greater than the
root.
9. • The element to be searched is 10
• Compare the element with the root node 12, 10 < 12, hence you move
to the left subtree. No need to analyze the right-subtree
• Now compare 10 with node 7, 10 > 7, so move to the right-subtree
• Then compare 10 with the next node, which is 9, 10 > 9, look in the
right subtree child
• 10 matches with the value in the node, 10 = 10, return the value to the
user.
10. Pseudo Code for Searching in BST
• search(element, root)
• if !root
• return -1
• if root.value == element
• return 1
• if root.value < element
• search(element, root.right)
• else
• search(element, root.left)
11. Insert Operation
• This is a very straight forward operation.
• First, the root node is inserted, then the next value is compared with
the root node. If the value is greater than root, it is added to the right
subtree, and if it is lesser than the root, it is added to the left subtree.
12. • There is a list of 6 elements that need to be inserted in a BST in order
from left to right ( 12, 7, 9, 19, 5, 10)
• Insert 12 as the root node and compare next values 7 and 9 for
inserting accordingly into the right and left subtree
• Compare the remaining values 19, 5, and 10 with the root node 12 and
place them accordingly. 19 > 12 place it as the right child of 12, 5 < 12
& 5 < 7, hence place it as left child of 7.
• Now compare 10, 10 is < 12 & 10 is > 7 & 10 is > 9, place 10 as right
subtree of 9.
14. • You should practice in lab (tomorrow) about different ways of
Delete operation of BST