The binary search is faster than the sequential search. The complexity of binary search is O(log n) whereas the complexity of a sequential search is O(n). Stacks are used to evaluate algebraic or arithmetic expressions using prefix or postfix notations. Heap sort involves creating a max heap from the array and then replacing the root with the last element and rebuilding the heap for the remaining elements, repeating this process to sort the entire array.
This presentation is all about various built in
datastructures which we have in python.
List
Dictionary
Tuple
Set
and various methods present in each data structure
this presentation is made for the students who finds data structures a complex subject
this will help students to grab the various topics of data structures with simple presentation techniques
best regards
BCA group
(pooja,shaifali,richa,trishla,rani,pallavi,shivani)
linked list
singly linked list
insertion in singly linked list
DELETION IN SINGLY LINKED LIST
Searching a singly linked list
Doubly Linked List
insertion from Doubly linked list
DELETION from Doubly LINKED LIST
Searching a doubly linked list
Circular linked list
This presentation is all about various built in
datastructures which we have in python.
List
Dictionary
Tuple
Set
and various methods present in each data structure
this presentation is made for the students who finds data structures a complex subject
this will help students to grab the various topics of data structures with simple presentation techniques
best regards
BCA group
(pooja,shaifali,richa,trishla,rani,pallavi,shivani)
linked list
singly linked list
insertion in singly linked list
DELETION IN SINGLY LINKED LIST
Searching a singly linked list
Doubly Linked List
insertion from Doubly linked list
DELETION from Doubly LINKED LIST
Searching a doubly linked list
Circular linked list
Abstract data types (adt) intro to data structure part 2Self-Employed
Abstract Data type (ADT), Related to DATA STRUCTURE and ALGORITHMS STACK QUEUE ARRAY LINKED LIST ALGORITHMS AND INSERTION DELETION MERGE TRAVERSE MODIFY AND OTHER related operation in the algorithms of stack queue array and linked list as an ADT type
Data may be organized in many different ways; the logical or mathematical model of a particular organization of data is called "Data Structure". The choice of a particular data model depends on two considerations:
It must be rich enough in structure to reflect the actual relationships of the data in the real world.
The structure should be simple enough that one can effectively process the data when necessary.
Data Structure Operations
The particular data structure that one chooses for a given situation depends largely on the nature of specific operations to be performed.
The following are the four major operations associated with any data structure:
i. Traversing : Accessing each record exactly once so that certain items in the record may be processed.
ii. Searching : Finding the location of the record with a given key value, or finding the locations of all records which satisfy one or more conditions.
iii. Inserting : Adding a new record to the structure.
iv. Deleting : Removing a record from the structure.
Primitive and Composite Data Types
Primitive Data Types are Basic data types of any language. In most computers these are native to the machine's hardware.
Some Primitive data types are:
Integer
Abstract data types (adt) intro to data structure part 2Self-Employed
Abstract Data type (ADT), Related to DATA STRUCTURE and ALGORITHMS STACK QUEUE ARRAY LINKED LIST ALGORITHMS AND INSERTION DELETION MERGE TRAVERSE MODIFY AND OTHER related operation in the algorithms of stack queue array and linked list as an ADT type
Data may be organized in many different ways; the logical or mathematical model of a particular organization of data is called "Data Structure". The choice of a particular data model depends on two considerations:
It must be rich enough in structure to reflect the actual relationships of the data in the real world.
The structure should be simple enough that one can effectively process the data when necessary.
Data Structure Operations
The particular data structure that one chooses for a given situation depends largely on the nature of specific operations to be performed.
The following are the four major operations associated with any data structure:
i. Traversing : Accessing each record exactly once so that certain items in the record may be processed.
ii. Searching : Finding the location of the record with a given key value, or finding the locations of all records which satisfy one or more conditions.
iii. Inserting : Adding a new record to the structure.
iv. Deleting : Removing a record from the structure.
Primitive and Composite Data Types
Primitive Data Types are Basic data types of any language. In most computers these are native to the machine's hardware.
Some Primitive data types are:
Integer
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This tutorial explains about linked List concept. it contains types of linked list also. All possible graphical representations are included for better understanding.
Array is a container which can hold a fix number of items and these items should be of the same type. Most of the data structures make use of arrays to implement their algorithms. Following are the important terms to understand the concept of array.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
2. Which of the following is faster? A binary search of an ordered set of elements in an array or a sequential search of the elements?
3. The binary search is faster than the sequential search. The complexity of binary search is 'log n' whereas the complexity of a sequential search is 'n'. In a binary search, each time we proceed, we have to deal with only half of the elements of the array compared to the previous one. So the search is faster.
4. List out the areas in which data structures are applied extensively?
7. Stack. Because of its LIFO (Last In First Out) property it remembers its caller and hence knows where to return to when the function has to return. Recursion makes use of system stack for storing the return addresses of the function calls. Every recursive function has its equivalent iterative (non-recursive) function. Even when such equivalent iterative procedures are written, explicit stack is to be used.
9. True Tree defines the structure of an acyclic graph but does not disallow duplicates.
10. The size of a Tree is the number of nodes in the Tree : True (or) False?
11. True The size denotes the number of nodes, height denotes the longest path from leaf node to root node.
12. Ram is told to sort a set of Data using Data structure. He has been told to use one of the following Methods Insertion Selection Exchange Linear Now Ram says a Method from the above can not be used to sort. Which is the method?
13. Linear Using insertion we can perform insertion sort, using selection we can perform selection sort, and using exchange we can perform bubble sort. But no sorting method is possible using linear method; Linear is a searching method
14. Ashok is told to manipulate an Arithmetic Expression. What is the data structure he will use? Linked List Tree Graph Stack
15. Stack Stacks are used to evaluate the algebraic or arithmetic expressions using prefix or postfix notations
16. There are 8,15,13,14 nodes in 4 different trees. Which of them could form a full binary tree? 8 15 13 14
17. In general, there are 2n – 1 nodes in a full binary tree. By the method of elimination: Full binary tree contains odd number of nodes. So there cannot be a full binary tree with 8 or 14 nodes. With 13 nodes, you can form a complete binary tree but not a full binary tree. Full and complete binary trees are different All full binary trees are complete binary trees but not vice versa
18. A B C D E F G Full binary Tree: A binary tree is a full binary tree if and only if: Each non leaf node has exactly two child nodes All leaf nodes have identical path length It is called full since all possible node slots are occupied
19. A B C G D E F H I J K Complete binary Tree: A complete binary tree (of height h) satisfies the following conditions: Level 0 to h-1 represent a full binary tree of height h-1 One or more nodes in level h-1 may have 0, or 1 child nodes
20. How many null branches are there in a binary tree with 20 nodes?
21. 21 (null branches) Let’s consider a tree with 5 nodes So the total number of null nodes in a binary tree of n nodes is n+1 Null branches
22. Write an algorithm to detect loop in a linked list. You are presented with a linked list, which may have a "loop" in it. That is, an element of the linked list may incorrectly point to a previously encountered element, which can cause an infinite loop when traversing the list. Devise an algorithm to detect whether a loop exists in a linked list. How does your answer change if you cannot change the structure of the list elements?
23. One possible answer is to add a flag to each element of the list. You could then traverse the list, starting at the head and tagging each element as you encounter it. If you ever encountered an element that was already tagged, you would know that you had already visited it and that there existed a loop in the linked list. What if you are not allowed to alter the structure of the elements of the linked list?
24. The following algorithm will find the loop: Start with two pointers ptr1 and ptr2. Set ptr1 and ptr2 to the head of the linked list. Traverse the linked list with ptr1 moving twice as fast as ptr2 (for every two elements that ptr1 advances within the list, advance ptr2 by one element). Stop when ptr1 reaches the end of the list, or when ptr1 = ptr2. If ptr1 and ptr2 are ever equal, then there must be a loop in the linked list. If the linked list has no loops, ptr1 should reach the end of the linked list ahead of ptr2
25. The Operation that is not allowed in a binary search tree is Location Change Search Deletion Insertion
41. If every node u in Graph (G) is adjacent to every other node v in G, it is called as _____ graph. Directed Graph Complete Graph Connected Graph Multi Graph
45. How do you chose the best algorithm among available algorithms to solve a problem Based on space complexity Based on programming requirements Based on time complexity All the above
49. Choose the limitation of an array from the below options. Memory Management is very poor Searching is slower Insertion and deletion are costlier Insertion and Deletion is not possible
54. Use a temp stack Data In into queue Push the element into the original stack Data Out from queue Pop all the elements from stack into a temp stackpop out the first element from the temp stack
55. Write a C program to compare two linked lists.
57. Write a C program to return the nth node from the end of a linked list.
58. Suppose one needs to get to the 6th node from the end in the LL. First, just keep on incrementing the first pointer (ptr1) till the number of increments cross n (which is 6 in this case) STEP 1 : 1(ptr1,ptr2) -> 2 -> 3 -> 4 -> 5 -> 6 -> 7 -> 8 -> 9 -> 10 STEP 2 : 1(ptr2) -> 2 -> 3 -> 4 -> 5 -> 6(ptr1) -> 7 -> 8 -> 9 -> 10 Now, start the second pointer (ptr2) and keep on incrementing it till the first pointer (ptr1) reaches the end of the LL. STEP 3 : 1 -> 2 -> 3 -> 4(ptr2) -> 5 -> 6 -> 7 -> 8 -> 9 -> 10 (ptr1) So here you have the 6th node from the end pointed to by ptr2!
59. struct node { int data; struct node *next; }mynode; mynode * nthNode(mynode *head, int n /*pass 0 for last node*/) { mynode *ptr1,*ptr2; int count; if(!head) { return(NULL); } ptr1 = head; ptr2 = head; count = 0;
60. while(count < n) { count++; if((ptr1=ptr1->next)==NULL) { //Length of the linked list less than n. Error. return(NULL); } } while((ptr1=ptr1->next)!=NULL) { ptr2=ptr2->next; } return(ptr2); }
61. Write a C program to insert nodes into a linked list in a sorted fashion?
62. The solution is to iterate down the list looking for the correct place to insert the new node. That could be the end of the list, or a point just before a node which is larger than the new node. Let us assume the memory for the new node has already been allocated and a pointer to that memory is being passed to this function. // Special case code for the head end void linkedListInsertSorted(struct node** headReference, struct node* newNode) { // Special case for the head end if (*headReference == NULL || (*headReference)->data >= newNode->data){ newNode->next = *headReference;
63. *headReference = newNode; } else { // Locate the node before which the insertion is to happen! struct node* current = *headReference; while (current->next!=NULL && current->next->data < newNode->data){ current = current->next; } newNode->next = current->next; current->next = newNode; } }
64. Write a C program to remove duplicates from a sorted linked list?
65. As the linked list is sorted, we can start from the beginning of the list and compare adjacent nodes. When adjacent nodes are the same, remove the second one. There's a tricky case where the node after the next node needs to be noted before the deletion. // Remove duplicates from a sorted list void RemoveDuplicates(struct node* head) { struct node* current = head; if (current == NULL) return; // do nothing if the list is empty // Compare current node with next node while(current->next!=NULL) {
66. if (current->data == current->next->data) { struct node* nextNext = current->next->next; free(current->next); current->next = nextNext; } else { current = current->next; // only advance if no deletion } } }
67. Write a C program to find the depth or height of a tree.
69. Write C code to determine if two trees are identical
70. structBintree { int element; structBintree *left; structBintree *right; }; typedefstructBintree* Tree; int CheckIdentical( Tree T1, Tree T2 ) { if(!T1 && !T2) // If both tree are NULL then return true return 1;
71. else if((!T1 && T2) || (T1 && !T2)) //If either of one is NULL, return false return 0; else return ((T1->element == T2->element) && CheckIdentical(T1->left, T2-i>left) && CheckIdentical(T1->right, T2->right)); // if element of both tree are same and left and right tree is also same then both trees are same }
74. Which of the following are called siblings Children of the same parent All nodes in the given path upto leaf node All nodes in a sub tree Children, Grand Children
80. Data structure using sequential allocation is called Linear Data Structure Non-Linear Data Structure Non-primitive Data Structure Sequence Data Structure
92. If you are using C language to implement the heterogeneous linked list, what pointer type will you use?
93. The heterogeneous linked list contains different data types in its nodes and we need a pointer to connect them. It is not possible to use ordinary pointers for this. So we use void pointer. Void pointer is capable of storing pointer to anytype of data (eg., integer or character) as it is a generic pointer type.
95. A Heap is an almost complete binary tree. In this tree, if the maximum level is i, then, upto the (i-1)th level should be complete. At level i, the number of nodes can be less than or equal to 2^i. If the number of nodes is less than 2^i, then the nodes in that level should be completely filled, only from left to right The property of an ascending heap is that, the root is the lowest and given any other node i, that node should be less than its left child and its right child. In a descending heap, the root should be the highest and given any other node i, that node should be greater than its left child and right child.
96. To sort the elements, one should create the heap first. Once the heap is created, the root has the highest value. Now we need to sort the elements in ascending order. The root can not be exchanged with the nth element so that the item in the nth position is sorted. Now, sort the remaining (n-1) elements. This can be achieved by reconstructing the heap for (n-1) elements.
97. heapsort() { n = array(); // Convert the tree into an array. makeheap(n); // Construct the initial heap. for(i=n; i>=2; i--) { swap(s[1],s[i]); heapsize--; keepheap(i); } } makeheap(n) { heapsize=n; for(i=n/2; i>=1; i--) keepheap(i); } keepheap(i) { l = 2*i; r = 2*i + 1; p = s[l]; q = s[r]; t = s[i];
98. if(l<=heapsize && p->value > t->value) largest = l; else largest = i; m = s[largest]; if(r<=heapsize && q->value > m->value) largest = r; if(largest != i) { swap(s[i], s[largest]); keepheap(largest); } }
99. Implement the bubble sort algorithm. How can it be improved? Write the code for selection sort, quick sort, insertion sort.
101. To improvise this basic algorithm, keep track of whether a particular pass results in any swap or not. If not, you can break out without wasting more cycles. void bubble_sort(int a[], int n) { int i, j, temp; int flag; for(j = 1; j < n; j++) { flag = 0; for(i = 0; i < (n - j); i++) { if(a[i] >= a[i + 1]) { //Swap a[i], a[i+1] flag = 1; } } if(flag==0)break; } }
102. Selection Sort Algorithm void selection_sort(int a[], int n) { int i, j, small, pos, temp; for(i = 0; i < (n - 1); i++) { small = a[i]; pos = i; for(j = i + 1; j < n; j++) { if(a[j] < small) { small = a[j]; pos = j; } } temp = a[pos]; a[pos] = a[i]; a[i] = temp; } }
103. Quick Sort Algorithm int partition(int a[], int low, int high) { int i, j, temp, key; key = a[low]; i = low + 1; j = high; while(1) { while(i < high && key >= a[i])i++; while(key < a[j])j--; if(i < j) { temp = a[i]; a[i] = a[j]; a[j] = temp; } else { temp = a[low]; a[low] = a[j]; a[j] = temp; return(j); } } }
104. void quicksort(int a[], int low, int high) { int j; if(low < high) { j = partition(a, low, high); quicksort(a, low, j - 1); quicksort(a, j + 1, high); } } int main() { // Populate the array a quicksort(a, 0, n - 1); }