The document discusses algorithm analysis and different searching and sorting algorithms. It introduces sequential search and binary search as simple searching algorithms. Sequential search, also called linear search, examines each element of a list sequentially until a match is found. It has average time complexity of O(n) as it may need to examine all n elements in the worst case.
The document provides an introduction to data structures and algorithms analysis. It discusses that a program consists of organizing data in a structure and a sequence of steps or algorithm to solve a problem. A data structure is how data is organized in memory and an algorithm is the step-by-step process. It describes abstraction as focusing on relevant problem properties to define entities called abstract data types that specify what can be stored and operations performed. Algorithms transform data structures from one state to another and are analyzed based on their time and space complexity.
This document provides an overview and introduction to the concepts taught in a data structures and algorithms course. It discusses the goals of reinforcing that every data structure has costs and benefits, learning commonly used data structures, and understanding how to analyze the efficiency of algorithms. Key topics covered include abstract data types, common data structures, algorithm analysis techniques like best/worst/average cases and asymptotic notation, and examples of analyzing the time complexity of various algorithms. The document emphasizes that problems can have multiple potential algorithms and that problems should be carefully defined in terms of inputs, outputs, and resource constraints.
This document discusses the complexity of algorithms and the tradeoff between algorithm cost and time. It defines algorithm complexity as a function of input size that measures the time and space used by an algorithm. Different complexity classes are described such as polynomial, sub-linear, and exponential time. Examples are given to find the complexity of bubble sort and linear search algorithms. The concept of space-time tradeoffs is introduced, where using more space can reduce computation time. Genetic algorithms are proposed to efficiently solve large-scale construction time-cost tradeoff problems.
This document discusses algorithm analysis and determining the time complexity of algorithms. It begins by defining an algorithm and noting that the efficiency of algorithms should be analyzed independently of specific implementations or hardware. The document then discusses analyzing the time complexity of various algorithms by counting the number of operations and expressing efficiency using growth functions. Common growth functions like constant, linear, quadratic, and exponential are introduced. The concept of asymptotic notation (Big O) for describing an algorithm's time complexity is also covered. Examples are provided to demonstrate how to determine the time complexity of iterative and recursive algorithms.
The document discusses algorithm analysis. It describes that the purpose of analysis is to determine an algorithm's performance in terms of time and space efficiency. Time efficiency, also called time complexity, measures how fast an algorithm solves a problem by determining the running time as a function of input size. Space efficiency measures an algorithm's storage requirements. Algorithm analysis approaches include empirical testing, analytical examination, and visualization techniques.
This document discusses data structures and their role in organizing data efficiently for computer programs. It defines key concepts like abstract data types, algorithms, and problems. It also provides examples to illustrate selecting the appropriate data structure based on the operations and constraints of a problem. A banking application is used to demonstrate how hash tables are suitable because they allow extremely fast searching by account numbers while also supporting efficient insertion and deletion. B-trees are shown to be better than hash tables for a city database because they enable fast range queries in addition to exact searches. Overall, the document emphasizes that each data structure has costs and benefits, and a careful analysis is needed to determine the best structure for a given problem.
The document provides an introduction to data structures and algorithms analysis. It discusses that a program consists of organizing data in a structure and a sequence of steps or algorithm to solve a problem. A data structure is how data is organized in memory and an algorithm is the step-by-step process. It describes abstraction as focusing on relevant problem properties to define entities called abstract data types that specify what can be stored and operations performed. Algorithms transform data structures from one state to another and are analyzed based on their time and space complexity.
This document provides an overview and introduction to the concepts taught in a data structures and algorithms course. It discusses the goals of reinforcing that every data structure has costs and benefits, learning commonly used data structures, and understanding how to analyze the efficiency of algorithms. Key topics covered include abstract data types, common data structures, algorithm analysis techniques like best/worst/average cases and asymptotic notation, and examples of analyzing the time complexity of various algorithms. The document emphasizes that problems can have multiple potential algorithms and that problems should be carefully defined in terms of inputs, outputs, and resource constraints.
This document discusses the complexity of algorithms and the tradeoff between algorithm cost and time. It defines algorithm complexity as a function of input size that measures the time and space used by an algorithm. Different complexity classes are described such as polynomial, sub-linear, and exponential time. Examples are given to find the complexity of bubble sort and linear search algorithms. The concept of space-time tradeoffs is introduced, where using more space can reduce computation time. Genetic algorithms are proposed to efficiently solve large-scale construction time-cost tradeoff problems.
This document discusses algorithm analysis and determining the time complexity of algorithms. It begins by defining an algorithm and noting that the efficiency of algorithms should be analyzed independently of specific implementations or hardware. The document then discusses analyzing the time complexity of various algorithms by counting the number of operations and expressing efficiency using growth functions. Common growth functions like constant, linear, quadratic, and exponential are introduced. The concept of asymptotic notation (Big O) for describing an algorithm's time complexity is also covered. Examples are provided to demonstrate how to determine the time complexity of iterative and recursive algorithms.
The document discusses algorithm analysis. It describes that the purpose of analysis is to determine an algorithm's performance in terms of time and space efficiency. Time efficiency, also called time complexity, measures how fast an algorithm solves a problem by determining the running time as a function of input size. Space efficiency measures an algorithm's storage requirements. Algorithm analysis approaches include empirical testing, analytical examination, and visualization techniques.
This document discusses data structures and their role in organizing data efficiently for computer programs. It defines key concepts like abstract data types, algorithms, and problems. It also provides examples to illustrate selecting the appropriate data structure based on the operations and constraints of a problem. A banking application is used to demonstrate how hash tables are suitable because they allow extremely fast searching by account numbers while also supporting efficient insertion and deletion. B-trees are shown to be better than hash tables for a city database because they enable fast range queries in addition to exact searches. Overall, the document emphasizes that each data structure has costs and benefits, and a careful analysis is needed to determine the best structure for a given problem.
This document provides an overview of algorithm analysis and asymptotic complexity. It discusses learning outcomes related to analyzing algorithm efficiency using Big O, Omega, and Theta notation. Key points covered include:
- Defining the problem size n and relating algorithm running time to n
- Distinguishing between best-case, worst-case, and average-case complexity
- Using asymptotic notation like Big O to give upper bounds on complexity rather than precise calculations
- Common asymptotic categories like O(n), O(n^2), O(n log n) that classify algorithm growth rates
The document discusses algorithms and their analysis. It defines an algorithm as a sequence of unambiguous steps to solve a problem within a finite time. Characteristics of algorithms include being unambiguous, having inputs/outputs, and terminating in finite time. Algorithm analysis involves determining theoretical and empirical time and space complexity as input size increases. Time complexity is analyzed by counting basic operations, while space complexity considers fixed and variable memory usage. Worst, best, and average cases analyze how efficiency varies with different inputs. Asymptotic analysis focuses on long-term growth rates to compare algorithms.
Fundamentals of the Analysis of Algorithm EfficiencySaranya Natarajan
This document discusses analyzing the efficiency of algorithms. It introduces the framework for analyzing algorithms in terms of time and space complexity. Time complexity indicates how fast an algorithm runs, while space complexity measures the memory required. The document outlines steps for analyzing algorithms, including measuring input size, determining the basic operations, calculating frequency counts of operations, and expressing efficiency in Big O notation order of growth. Worst-case, best-case, and average-case time complexities are also discussed.
This document provides information about the CS 331 Data Structures course. It includes the contact information for the professor, Dr. Chandran Saravanan, as well as online references and resources about data structures. It then covers topics like structuring and organizing data, different types of data structures suitable for different applications, basic principles of data structures, language support for data structures, selecting an appropriate data structure, analyzing algorithms, and provides an example analysis of a sample algorithm's runtime complexity.
This document discusses analyzing the efficiency of algorithms. It begins by explaining how to measure algorithm efficiency using Big O notation, which estimates how fast an algorithm's execution time grows as the input size increases. Common growth rates like constant, logarithmic, linear, and quadratic time are described. Examples are provided to demonstrate determining the Big O of various algorithms. Specific algorithms analyzed in more depth include binary search, selection sort, insertion sort, and Towers of Hanoi. The document aims to introduce techniques for developing efficient algorithms using approaches like dynamic programming, divide-and-conquer, and backtracking.
This document provides an overview of a lecture on designing and analyzing computer algorithms. It discusses key concepts like what an algorithm and program are, common algorithm design techniques like divide-and-conquer and greedy methods, and how to analyze algorithms' time and space complexity. The goals of analyzing algorithms are to understand their behavior, improve efficiency, and determine whether problems can be solved within a reasonable time frame.
This document provides an overview of data structures and algorithms analysis. It discusses big-O notation and how it is used to analyze computational complexity and asymptotic complexity of algorithms. Various growth functions like O(n), O(n^2), O(log n) are explained. Experimental and theoretical analysis methods are described and limitations of experimental analysis are highlighted. Key aspects like analyzing loop executions and nested loops are covered. The document also provides examples of analyzing algorithms and comparing their efficiency using big-O notation.
Data Structures and Algorithms Lecture 2: Analysis of Algorithms, Asymptotic ...TechVision8
This document discusses analyzing the running time of algorithms. It introduces pseudocode as a way to describe algorithms, primitive operations that are used to count the number of basic steps an algorithm takes, and asymptotic analysis to determine an algorithm's growth rate as the input size increases. The key points covered are using big-O notation to focus on the dominant term and ignore lower-order terms and constants, and analyzing two algorithms for computing prefix averages to demonstrate asymptotic analysis.
This document provides an overview of algorithms and their analysis. It defines an algorithm as a finite sequence of unambiguous instructions that will terminate in a finite amount of time. Key aspects that algorithms must have are being input-defined, having output, being definite, finite, and effective. The document then discusses steps for designing algorithms like understanding the problem, selecting data structures, and verifying correctness. It also covers analyzing algorithms through evaluating their time complexity, which can be worst-case, best-case, or average-case, and space complexity. Common asymptotic notations like Big-O, Omega, and Theta notation are explained for describing an algorithm's efficiency. Finally, basic complexity classes and their properties are summarized.
The document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure to solve a problem and get a desired output. Key aspects of algorithms discussed include their time and space complexity, asymptotic analysis to determine best, average, and worst case running times, and common asymptotic notations like Big O that are used to analyze algorithms. Examples are provided to demonstrate how to determine the time and space complexity of different algorithms like those using loops, recursion, and nested loops.
Download Complete Material - https://www.instamojo.com/prashanth_ns/
This Data Structures and Algorithms contain 15 Units and each Unit contains 60 to 80 slides in it.
Contents…
• Introduction
• Algorithm Analysis
• Asymptotic Notation
• Foundational Data Structures
• Data Types and Abstraction
• Stacks, Queues and Deques
• Ordered Lists and Sorted Lists
• Hashing, Hash Tables and Scatter Tables
• Trees and Search Trees
• Heaps and Priority Queues
• Sets, Multi-sets and Partitions
• Dynamic Storage Allocation: The Other Kind of Heap
• Algorithmic Patterns and Problem Solvers
• Sorting Algorithms and Sorters
• Graphs and Graph Algorithms
• Class Hierarchy Diagrams
• Character Codes
This slides contains assymptotic notations, recurrence relation like subtitution method, iteration method, master method and recursion tree method and sorting algorithms like merge sort, quick sort, heap sort, counting sort, radix sort and bucket sort.
The document discusses algorithms and data structures. It begins by introducing common data structures like arrays, stacks, queues, trees, and hash tables. It then explains that data structures allow for organizing data in a way that can be efficiently processed and accessed. The document concludes by stating that the choice of data structure depends on effectively representing real-world relationships while allowing simple processing of the data.
Linear search examines each element of a list sequentially, one by one, and checks if it is the target value. It has a time complexity of O(n) as it requires searching through each element in the worst case. While simple to implement, linear search is inefficient for large lists as other algorithms like binary search require fewer comparisons.
This document discusses the analysis of algorithms and asymptotic notations. It begins by stating that analyzing an algorithm's complexity is essential for algorithm design. The two main factors for measuring an algorithm's performance are time complexity, which is the amount of time required to run the algorithm, and space complexity, which is the amount of memory required. The document then discusses analyzing best case, worst case, and average case scenarios. It concludes by introducing the asymptotic notations of Big O, Omega, and Theta, which are used to represent the upper and lower time complexity bounds of an algorithm.
Introduction to data structures and AlgorithmDhaval Kaneria
This document provides an introduction to data structures and algorithms. It discusses key concepts like algorithms, abstract data types (ADTs), data structures, time complexity, and space complexity. It describes common data structures like stacks, queues, linked lists, trees, and graphs. It also covers different ways to classify data structures, the process for selecting an appropriate data structure, and how abstract data types encapsulate both data and functions. The document aims to explain fundamental concepts related to organizing and manipulating data efficiently.
This document provides an introduction to data structures and algorithms. It discusses key concepts like abstract data types (ADTs), different types of data structures including linear and non-linear structures, analyzing algorithms to assess efficiency, and selecting appropriate data structures based on required operations and resource constraints. The document also covers topics like classifying data structures, properties of algorithms, analyzing time and space complexity, and examples of iterative and recursive algorithms and their complexity analysis.
This document provides an overview of algorithm analysis and asymptotic complexity. It discusses learning outcomes related to analyzing algorithm efficiency using Big O, Omega, and Theta notation. Key points covered include:
- Defining the problem size n and relating algorithm running time to n
- Distinguishing between best-case, worst-case, and average-case complexity
- Using asymptotic notation like Big O to give upper bounds on complexity rather than precise calculations
- Common asymptotic categories like O(n), O(n^2), O(n log n) that classify algorithm growth rates
The document discusses algorithms and their analysis. It defines an algorithm as a sequence of unambiguous steps to solve a problem within a finite time. Characteristics of algorithms include being unambiguous, having inputs/outputs, and terminating in finite time. Algorithm analysis involves determining theoretical and empirical time and space complexity as input size increases. Time complexity is analyzed by counting basic operations, while space complexity considers fixed and variable memory usage. Worst, best, and average cases analyze how efficiency varies with different inputs. Asymptotic analysis focuses on long-term growth rates to compare algorithms.
Fundamentals of the Analysis of Algorithm EfficiencySaranya Natarajan
This document discusses analyzing the efficiency of algorithms. It introduces the framework for analyzing algorithms in terms of time and space complexity. Time complexity indicates how fast an algorithm runs, while space complexity measures the memory required. The document outlines steps for analyzing algorithms, including measuring input size, determining the basic operations, calculating frequency counts of operations, and expressing efficiency in Big O notation order of growth. Worst-case, best-case, and average-case time complexities are also discussed.
This document provides information about the CS 331 Data Structures course. It includes the contact information for the professor, Dr. Chandran Saravanan, as well as online references and resources about data structures. It then covers topics like structuring and organizing data, different types of data structures suitable for different applications, basic principles of data structures, language support for data structures, selecting an appropriate data structure, analyzing algorithms, and provides an example analysis of a sample algorithm's runtime complexity.
This document discusses analyzing the efficiency of algorithms. It begins by explaining how to measure algorithm efficiency using Big O notation, which estimates how fast an algorithm's execution time grows as the input size increases. Common growth rates like constant, logarithmic, linear, and quadratic time are described. Examples are provided to demonstrate determining the Big O of various algorithms. Specific algorithms analyzed in more depth include binary search, selection sort, insertion sort, and Towers of Hanoi. The document aims to introduce techniques for developing efficient algorithms using approaches like dynamic programming, divide-and-conquer, and backtracking.
This document provides an overview of a lecture on designing and analyzing computer algorithms. It discusses key concepts like what an algorithm and program are, common algorithm design techniques like divide-and-conquer and greedy methods, and how to analyze algorithms' time and space complexity. The goals of analyzing algorithms are to understand their behavior, improve efficiency, and determine whether problems can be solved within a reasonable time frame.
This document provides an overview of data structures and algorithms analysis. It discusses big-O notation and how it is used to analyze computational complexity and asymptotic complexity of algorithms. Various growth functions like O(n), O(n^2), O(log n) are explained. Experimental and theoretical analysis methods are described and limitations of experimental analysis are highlighted. Key aspects like analyzing loop executions and nested loops are covered. The document also provides examples of analyzing algorithms and comparing their efficiency using big-O notation.
Data Structures and Algorithms Lecture 2: Analysis of Algorithms, Asymptotic ...TechVision8
This document discusses analyzing the running time of algorithms. It introduces pseudocode as a way to describe algorithms, primitive operations that are used to count the number of basic steps an algorithm takes, and asymptotic analysis to determine an algorithm's growth rate as the input size increases. The key points covered are using big-O notation to focus on the dominant term and ignore lower-order terms and constants, and analyzing two algorithms for computing prefix averages to demonstrate asymptotic analysis.
This document provides an overview of algorithms and their analysis. It defines an algorithm as a finite sequence of unambiguous instructions that will terminate in a finite amount of time. Key aspects that algorithms must have are being input-defined, having output, being definite, finite, and effective. The document then discusses steps for designing algorithms like understanding the problem, selecting data structures, and verifying correctness. It also covers analyzing algorithms through evaluating their time complexity, which can be worst-case, best-case, or average-case, and space complexity. Common asymptotic notations like Big-O, Omega, and Theta notation are explained for describing an algorithm's efficiency. Finally, basic complexity classes and their properties are summarized.
The document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure to solve a problem and get a desired output. Key aspects of algorithms discussed include their time and space complexity, asymptotic analysis to determine best, average, and worst case running times, and common asymptotic notations like Big O that are used to analyze algorithms. Examples are provided to demonstrate how to determine the time and space complexity of different algorithms like those using loops, recursion, and nested loops.
Download Complete Material - https://www.instamojo.com/prashanth_ns/
This Data Structures and Algorithms contain 15 Units and each Unit contains 60 to 80 slides in it.
Contents…
• Introduction
• Algorithm Analysis
• Asymptotic Notation
• Foundational Data Structures
• Data Types and Abstraction
• Stacks, Queues and Deques
• Ordered Lists and Sorted Lists
• Hashing, Hash Tables and Scatter Tables
• Trees and Search Trees
• Heaps and Priority Queues
• Sets, Multi-sets and Partitions
• Dynamic Storage Allocation: The Other Kind of Heap
• Algorithmic Patterns and Problem Solvers
• Sorting Algorithms and Sorters
• Graphs and Graph Algorithms
• Class Hierarchy Diagrams
• Character Codes
This slides contains assymptotic notations, recurrence relation like subtitution method, iteration method, master method and recursion tree method and sorting algorithms like merge sort, quick sort, heap sort, counting sort, radix sort and bucket sort.
The document discusses algorithms and data structures. It begins by introducing common data structures like arrays, stacks, queues, trees, and hash tables. It then explains that data structures allow for organizing data in a way that can be efficiently processed and accessed. The document concludes by stating that the choice of data structure depends on effectively representing real-world relationships while allowing simple processing of the data.
Linear search examines each element of a list sequentially, one by one, and checks if it is the target value. It has a time complexity of O(n) as it requires searching through each element in the worst case. While simple to implement, linear search is inefficient for large lists as other algorithms like binary search require fewer comparisons.
This document discusses the analysis of algorithms and asymptotic notations. It begins by stating that analyzing an algorithm's complexity is essential for algorithm design. The two main factors for measuring an algorithm's performance are time complexity, which is the amount of time required to run the algorithm, and space complexity, which is the amount of memory required. The document then discusses analyzing best case, worst case, and average case scenarios. It concludes by introducing the asymptotic notations of Big O, Omega, and Theta, which are used to represent the upper and lower time complexity bounds of an algorithm.
Introduction to data structures and AlgorithmDhaval Kaneria
This document provides an introduction to data structures and algorithms. It discusses key concepts like algorithms, abstract data types (ADTs), data structures, time complexity, and space complexity. It describes common data structures like stacks, queues, linked lists, trees, and graphs. It also covers different ways to classify data structures, the process for selecting an appropriate data structure, and how abstract data types encapsulate both data and functions. The document aims to explain fundamental concepts related to organizing and manipulating data efficiently.
This document provides an introduction to data structures and algorithms. It discusses key concepts like abstract data types (ADTs), different types of data structures including linear and non-linear structures, analyzing algorithms to assess efficiency, and selecting appropriate data structures based on required operations and resource constraints. The document also covers topics like classifying data structures, properties of algorithms, analyzing time and space complexity, and examples of iterative and recursive algorithms and their complexity analysis.
Similar to Data Structure and Algorithm chapter two, This material is for Data Structure and Algorithm course. (20)
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdfflufftailshop
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
2. Algorithm analysis
Algorithm analysis refers to the process of determining how much
computing time and storage that algorithms will require.
In other words, it’s a process of predicting the resource
requirement of algorithms in a given environment.
In order to solve a problem, there are many possible algorithms. One
has to be able to choose the best algorithm for the problem at hand
using some scientific method.
To classify some data structures and algorithms as good, we need
precise ways of analyzing them in terms of resource requirement.
1/10/2024 Data Structures and Algorithms 2
3. The main resources:
• Running Time
• Memory Usage
• Communication Bandwidth
Note: Running time is the most important since computational time is the
most precious resource in most problem domains.
There are two approaches to measure the efficiency of algorithms:
1.Informal Approach
2.Formal Approach
1/10/2024 Data Structures and Algorithms 3
4. Informal Approach
Empirical vs Theoretical Analysis
Empirical Analysis
• it works based on the total running time of the program. It uses actual
system clock time.
Example:
t1(Initial time before the program starts)
for(int i=0; i<=10; i++)
cout<<i;
t2 (final time after the execution of the program is finished)
Running time taken by the above algorithm
(Total Time) = t2-t1;
1/10/2024
Data Structures and Algorithms
4
5. Cont…
It is difficult to determine efficiency of algorithms using this approach,
because clock-time can vary based on many factors.
For example:
a) Processor speed of the computer
b) Current processor load
c) Specific data for a particular run of the program
Input size
Input properties
d)Operating System
Multitasking Vs Single tasking
Internal structure
1/10/2024 Data Structures and Algorithms 5
6. Theoretical Analysis
• Determining the quantity of resources required using mathematical concept
by analyzing an algorithm according to the number of basic operations
(time units) required, rather than according to an absolute amount of time
involved.
Why we use operations in theoretical approach to determine the efficiency
of algorithm because:
The number of operation will not vary under different conditions.
It helps us to have a meaningful measure that permits comparison of
algorithms independent of operating platform.
It helps to determine the complexity of algorithm.
1/10/2024 Data Structures and Algorithms 6
7. Complexity Analysis
Complexity Analysis is the systematic study of the cost of computation,
measured either in:
Time units
Operations performed
The amount of storage space required.
Two important ways to characterize the effectiveness of an algorithm are
its Space Complexity and Time Complexity.
Time Complexity: Determine the approximate amount of time (number of
operations) required to solve a problem of size n. The limiting behavior of
time complexity as size increases is called the Asymptotic Time
Complexity.
1/10/2024
Data Structures and Algorithms
7
8. Cont…
Space Complexity: Determine the approximate memory required to
solve a problem of size n.
The limiting behavior of space complexity as size increases is called
the Asymptotic Space Complexity.
Asymptotic Complexity of an algorithm determines the size of
problems that can be solved by the algorithm.
1/10/2024
Data Structures and Algorithms
8
9. Factors affecting the running time of a program:
CPU type
Memory used
Computer used
• Programming Language C (fastest), C++ (faster), Java (fast) C is
relatively faster than Java, because C is relatively nearer to Machine
language, so, Java takes relatively larger amount of time for
interpreting/translation to machine code.
Algorithm used
Input size
• Note: Important factors for this course are Input size and Algorithm
used.
1/10/2024 Data Structures and Algorithms 9
10. Analysis Rule
Assignment operation Example:- i=1 (1 time unit)
Single arithmetic operation Example:- x+y (1 time unit)
Input/output operation Example:- cin>>a; or cout<<a; (1 time
unit)
Single Boolean operation Example:- i<=5; (1 time unit)
Function return Example:- return x; (1 time unit)
Function call Example:- add(); (1 time unit)
1/10/2024
Data Structures and Algorithms
10
11. Example 1
Q1. Write an algorithm and analyze the time complexity for the
problem adding two numbers.
Step 1. accept first number (1 TU)
Step2. accept the second number (1 TU)
Step3. add two number (1 TU)
Step4. print the result (1 TU)
T(n)=4 TU
Steps of algorithms are different based on programmer points of view.
1/10/2024
Data Structures and Algorithms
11
12. Looping statements
Running time for a loop is equal to the running time for the statements inside the
loop times number of iterations.
Example 1:
int n=5;
For(int i =1; i<=n; i++)
{
cout<<i;
return 0;
}
T(n)=1+(1+n+1+n+n+1)=3n+4
12
Wednesday, January 10, 2024
13. Examples: 2
{
int n;
int k=0;
cout<<“Enter an integer”;
cin>>n;
for(int i=0;i<n; i++)
}
T(n)=2n+5
13
Wednesday, January 10, 2024
18. Formal Approach to Analysis
• In the above examples we have seen that analyzing Loop statements is so complex.
• It can be simplified by using some formal approach in which case we can ignore
initializations, loop controls, and updates.
1. Simple Loops: Formally, for loop can be translated to a summation. The index and
bounds of the summation are the same as the index and bounds of the for loop.
• Suppose we count the number of additions that are done. There is 1
addition per iteration of the loop, hence n additions in total.
1/10/2024 Data Structures and Algorithms 18
for (int i = 1; i <= N; i++) {
sum = sum+i;
}
N
N
i
1
1
19. 2. Nested Loops:
Nested for loops translate into multiple summations, one for each for loop.
Again, count the number of additions. The outer summation is for the outer
for loop.
1/10/2024
Data Structures and Algorithms
19
20. 3. Consecutive Statements:
Add the running times of the separate blocks of your code.
1/10/2024
Data Structures and Algorithms
20
21. 4. Conditionals:
If (test) s1 else s2: Compute the maximum of the running time for s1
and s2.
1/10/2024
Data Structures and Algorithms
21
22. Categories of Algorithm Analysis
• Algorithms may be examined under different situations to correctly
determine their efficiency for accurate comparison.
Best Case Analysis
Worst Case Analysis
Average Case Analysis
1/10/2024
Data Structures and Algorithms
22
23. 1.Best Case Analysis
Best case analysis assumes the input data are arranged in the
most advantageous order for the algorithm.
It also takes the smallest possible set of inputs and causes
execution of the fewest number of statements.
Moreover, it computes the lower bound of T(n), where T(n) is
the complexity function.
Examples: For sorting algorithm
If the list is already sorted (data are arranged in the required
order).
For searching algorithm If the desired item is located at first
accessed position.
1/10/2024 COSC 709: Natural Language Processing 23
24. 2. Worst Case Analysis
It assumes the input data are arranged in the most disadvantageous order for
the algorithm.
Takes the worst possible set of inputs. Causes execution of the largest number
of statements. Computes the upper bound of T(n) where T(n) is the
complexity function.
Example:
While sorting, if the list is in opposite order.
While searching, if the desired item is located at the last position or is
missing.
Worst case analysis is the most common analysis because, it provides the
upper bound for all input (even for bad ones).
1/10/2024
Data Structures and Algorithms
24
25. 3. Average Case Analysis
It determine the average of the running time overall permutation of input
data.
Takes an average set of inputs.
It also assumes random input size.
It causes average number of executions.
Computes the optimal bound of T(n) where T(n) is the complexity
function.
Sometimes average cases are as bad as worst cases and as good as best
cases.
1/10/2024
Data Structures and Algorithms
25
26. Order of Magnitude
Order of Magnitude refers to the rate at which the storage or time grows as
a function of problem size.
It is expressed in terms of its relationship to some known functions.
This type of analysis is called Asymptotic analysis.
Asymptotic analysis
• Asymptotic Analysis is concerned with how the running time of an algorithm
increases with the size of the input in the limit, as the size of the input
increases without bound!
1/10/2024 Data Structures and Algorithms 26
27. Types of notations
There are five notations used to describe a running time
function. These are:
Big-Oh Notation (O)
Big-Omega Notation ()
Theta Notation ()
Little-o Notation (o)
Little-Omega Notation ()
Note: The complexity of an algorithm is a numerical function
of the size of the problem (instance or input size).
1/10/2024 COSC 709: Natural Language Processing 27
28. 1. Big-Oh Notation
Definition: We say f(n)=O(g(n)), if there are positive constants no and c, such that to the right of no, the value
of f(n) always lies on or below c.g(n).
As n increases f(n) grows no faster than g(n). It’s only concerned with what happens for very large values of n.
It describes the worst case analysis. Gives an upper bound for a function to within a constant factor.
O-Notations are used to represent the amount of time an algorithm takes on the worst possible set of
inputs, “Worst-Case”.
1/10/2024 Data Structures and Algorithms 28
29. 2. Big-Omega (Ω)-Notation (Lower bound)
Definition: We write f(n)= Ω(g(n)) if there are positive constants no and c such that to the right of
no(k) the value of f(n) always lies on or above c.g(n).
As n increases f(n) grows no slower than g(n). It describes the best case analysis. Used to
represent the amount of time the algorithm takes on the smallest possible set of inputs-“Best
case”.
Big-Omega (Ω)-Notation (Lower bound)
1/10/2024
Data Structures and Algorithms
29
30. 3. Theta Notation (θ-Notation) (Optimal bound)
Definition: We say f(n)= θ(g(n)) if there exist positive constants no, c1 and c2 such that to the right of
no, the value of f(n) always lies between c1.g(n) and c2.g(n) inclusive, i.e., c1.g(n)<=f(n)<=c2.g(n), for all
n>=no.
As n increases f(n) grows as fast as g(n). It describes the average case analysis. To represent the
amount of time the algorithm takes on an average set of inputs- “Average case”.
1/10/2024
Data Structures and Algorithms
30
31. 4. Little-oh (small-oh) Notation
Definition: We say f(n)=o(g(n)), if there are positive constants no and
c such that to the right of no, the value of f(n) lies below c.g(n).
As n increases, g(n) grows strictly faster than f(n). It describes the
worst case analysis.
Denotes an upper bound that is not asymptotically tight.
Big O-Notation denotes an upper bound that may or may not be
asymptotically tight.
1/10/2024
Data Structures and Algorithms
31
32. 5. Little-Omega (ω) notation
Definition: We write f(n)=ω(g(n)), if there are positive constants no
and c such that to the right of no, the value of f(n) always lies above
c.g(n).
As n increases f(n) grows strictly faster than g(n).
It describes the best case analysis and denotes a lower bound that is
not asymptotically tight.
Big Ω-Notation denotes a lower bound that may or may not be
asymptotically tight.
1/10/2024 Data Structures and Algorithms 32
33. Arrangement of common functions by growth rate. List of typical growth rates
1/10/2024
Data Structures and Algorithms
33
34. Chapter two lesson 2
Sorting and Searching
1/10/2024
Data Structures and Algorithms
34
35. Simple Sorting and Searching Algorithm
Why do we study sorting and searching algorithms?
These algorithms are the most common and useful tasks operated by
computer system. Computers spend a lot of time searching and sorting.
1. Simple Searching algorithms
Searching:- is a process of finding an element in a list of items or
determining that the item is not in the list.
To keep things simple, we shall deal with a list of numbers. A search
method looks for a key, arrives by parameter.
By convention, the method will return the index of the element
corresponding to the key or, if unsuccessful, the value -1.
1/10/2024
Data Structures and Algorithms
35
36. 1. Simple Searching algorithms
There are two simple searching algorithms:
Sequential Search
Binary Search
Sequential Searching (Linear)
The most natural way of searching an item. Easy to understand and
implement.
Algorithm:
In a linear search, we start with top (beginning) of the list, and compare the
element at top with the key.
If we have a match, the search terminates and the index number is returned.
If not, we go on the next element in the list.
If we reach the end of the list without finding a match, we return -1.
1/10/2024 Data Structures and Algorithms 36
37. Sequential Searching (Linear)
6 3 11 15 9
1/10/2024 Data Structures and Algorithms 37
Searching for the value 15, linear search examines 6, 3, 11, and 15
Array num list contains:
Benefits:
Easy algorithm to understand
Array can be in any order
Disadvantages:
Inefficient (slow): for array of N elements, examines N/2 elements on average for value in array, N
elements for value not in array.
38. Implementation:
int LinearSearch(int list[ ], int key);
int main()
{
int list[] = {6, 3, 11, 15, 9};
int k = 15;
int i = LinearSearch(list, k);
if(i == -1)
cout << "the search item is not found" << endl;
else
cout << "The value is found at index position " << i << endl;
return 0; }
int LinearSearch(int list[ ], int key) {
int index = -1;
for(int i=0; i < n; i++) {
if(list[i]==key) {
index=i;
break;
} }
return index;
}
1/10/2024
Data Structures and Algorithms
38
39. Binary Searching
It assumes the data is sorted it also uses divide and conquer strategy
(approach).
Algorithm:
In a binary search, we look for the key in the middle of the list. If we get
a match, the search is over.
If the key is greater than the element in the middle of the list, we make
the top (upper) half the list to search.
If the key is smaller, we make the bottom (lower) half the list to search.
Repeat the above steps (I,II and III) until one element remains.
If this element matches return the index of the element, else
return -1 index. (-1 shows that the key is not in the list).
1/10/2024
Data Structures and Algorithms
39
40. Binary Search - Example
L mid R
Data= 59 n=10 index=n-1
1/10/2024
Data Structures and Algorithms
40
5 9 17 23 25 45 59 63 71 89
Data = a[mid]
Data < a [mid]
Data > a[mid]
L R mid
0 9 4
5 9 7
5 6 5
6 6 6
Benefits:
Much more efficient than linear search.
For array of N elements, performs at most log2N comparisons
Disadvantages:
Requires that array elements be sorted
41. Implementation
int BinarySearch(int list[ ], int key);
int main()
{
int list[] = {5,9,17,23,25,45,59,63,71,89};
int k = 59;
int i = BinarySearch(list, k);
if(i== -1)
cout << "the search item is not found" << endl;
else
cout << "The value is found at index position " << i << endl;
return 0;
}
int BinarySearch(int list[ ], int key)
{
int found=0,index=0;
int L=9, R=0, middle;
do
{
middle=(R + L)/2;
if(key==list[middle])
found=1;
else
{
if(key < list[middle])
R=middle-1;
else L=middle+1;
}
}while(found==0 && R >=L );
if(found==0)
index=-1;
else
index=middle;
return index;
}
1/10/2024
Data Structures and Algorithms
41
42. Simple Sorting Algorithms
What is sorting?
Sorting: is a process of reordering a list of items in either increasing or
decreasing order. Ordering a list of items is fundamental problem of
computer science. Sorting is the most important operation performed by
computers. Sorting is the first step in more complex algorithms.
Importance of sorting
• To represent data in more readable format.
• Optimize data searching to high level.
The most common sorting algorithms are:
• Bubble Sort
• Selection Sort
• Insertion Sort
1/10/2024
Data Structures and Algorithms
42
43. Bubble Sort
[NOTE: In each pass, the largest item “bubbles” down the list until it settles in its final position. This is where
bubble sort gets its name.]
Example,
Suppose we have a list of array of 5 elements A[5]={40,50,30,20,10}. We have to sort this array using bubble
sort algorithm.
Complexity Analysis:
• Analysis involves number of comparisons and swaps.
• How many comparisons? 1+2+3+…+(n-1)= O(n2)
• How many swaps? 1+2+3+…+(n-1)= O(n2)
1/10/2024
Data Structures and Algorithms
43
44. Selection Sort
Selection sort is an in-place comparison sort algorithm. In this algorithm, we repeatedly select the smallest
remaining element and move it to the end of a growing sorted list. It is one of the simplest sorting algorithm.
In this algorithm we have to find the minimum value in the list first. Then, Swap it with the value in the first
position.
After that, Start from the second position and repeat the steps above for remainder of the list.
Advantage: Simple and easy to implement.
Disadvantage: Inefficient for larger lists.
1/10/2024
Data Structures and Algorithms
44
45. Insertion Sort
Insertion sort algorithm somewhat resembles Selection Sort and Bubble sort.
Array is imaginary divided into two parts - sorted one and unsorted one.
At the beginning, sorted part contains first element of the array and unsorted one
contains the rest.
At every step, algorithm takes first element in the unsorted part and inserts it to
the right place of the sorted one.
When unsorted part becomes empty, algorithm stops.
In more detail:
• Consider the first item to be a sorted sublist (of one item)
• Insert the second item into the sorted sublist, shifting the first item as needed to make room
to insert the new addition
• Insert the third item into the sorted sublist (of two items), shifting items as necessary
• Repeat until all values are inserted into their proper positions
1/10/2024
Data Structures and Algorithms
45
46. Insertion Sort
It is a simple algorithm where a sorted sub list is maintained by entering on element at
a time.
An element which is to be inserted in this sorted sub-list has to find its appropriate
location and then it has to be inserted there. That is the reason why it is named so.
Example
1/10/2024 Data Structures and Algorithms 46
47. Cont…
It is reasonable to use binary search algorithm to find a proper place for
insertion.
Insertion sort is simply like playing cards: To sort the cards in your hand, you
extract a card, shift the remaining cards and then insert the extracted card in
the correct place.
This process is repeated until all the cards are in the correct sequence. Is
over twice as fast as the bubble sort and is just as easy to implement as the
selection sort.
Advantage: Relatively simple and easy to implement.
Disadvantage: Inefficient for large lists.
1/10/2024
Data Structures and Algorithms
47