this is an presentation about Big O Notation. Big O notaion is very useful to check the limitation and effeciecy of an algorithm in its worst cases.in these slides the examples about O(1),O(n),O(n^2) and O(n!) with some example algorithms in C++.
Big O notation is used in Computer Science to describe the performance or complexity of an algorithm. Big O specifically describes the worst-case scenario, and can be used to describe the execution time required or the space used (e.g. in memory or on disk) by an algorithm.
For further information
https://github.com/ashim888/dataStructureAndAlgorithm
References:
https://www.khanacademy.org/computing/computer-science/algorithms/asymptotic-notation/a/asymptotic-notation
http://web.mit.edu/16.070/www/lecture/big_o.pdf
https://rob-bell.net/2009/06/a-beginners-guide-to-big-o-notation/
https://justin.abrah.ms/computer-science/big-o-notation-explained.html
this is a briefer overview about the Big O Notation. Big O Notaion are useful to check the Effeciency of an algorithm and to check its limitation at higher value. with big o notation some examples are also shown about its cases and some functions in c++ are also described.
1. Introduction to time and space complexity.
2. Different types of asymptotic notations and their limit definitions.
3. Growth of functions and types of time complexities.
4. Space and time complexity analysis of various algorithms.
this is an presentation about Big O Notation. Big O notaion is very useful to check the limitation and effeciecy of an algorithm in its worst cases.in these slides the examples about O(1),O(n),O(n^2) and O(n!) with some example algorithms in C++.
Big O notation is used in Computer Science to describe the performance or complexity of an algorithm. Big O specifically describes the worst-case scenario, and can be used to describe the execution time required or the space used (e.g. in memory or on disk) by an algorithm.
For further information
https://github.com/ashim888/dataStructureAndAlgorithm
References:
https://www.khanacademy.org/computing/computer-science/algorithms/asymptotic-notation/a/asymptotic-notation
http://web.mit.edu/16.070/www/lecture/big_o.pdf
https://rob-bell.net/2009/06/a-beginners-guide-to-big-o-notation/
https://justin.abrah.ms/computer-science/big-o-notation-explained.html
this is a briefer overview about the Big O Notation. Big O Notaion are useful to check the Effeciency of an algorithm and to check its limitation at higher value. with big o notation some examples are also shown about its cases and some functions in c++ are also described.
1. Introduction to time and space complexity.
2. Different types of asymptotic notations and their limit definitions.
3. Growth of functions and types of time complexities.
4. Space and time complexity analysis of various algorithms.
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.
hashing is encryption process mostly used in programming language for security purpose.
This presentation will you understand all about hashing and also different techniques used in it for encryption process
Algorithms Lecture 1: Introduction to AlgorithmsMohamed Loey
We will discuss the following: Algorithms, Time Complexity & Space Complexity, Algorithm vs Pseudo code, Some Algorithm Types, Programming Languages, Python, Anaconda.
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.
hashing is encryption process mostly used in programming language for security purpose.
This presentation will you understand all about hashing and also different techniques used in it for encryption process
Algorithms Lecture 1: Introduction to AlgorithmsMohamed Loey
We will discuss the following: Algorithms, Time Complexity & Space Complexity, Algorithm vs Pseudo code, Some Algorithm Types, Programming Languages, Python, Anaconda.
Data structure and algorithm using javaNarayan Sau
This presentation created for people who like to go back to basics of data structure and its implementation. This presentation mostly helps B.Tech , Bsc Computer science students as well as all programmer who wants to develop software in core areas.
TIME EXECUTION OF DIFFERENT SORTED ALGORITHMSTanya Makkar
what is Algorithm and classification and its complexity
Time Complexity
Time Space trade-off
Asymptotic time complexity of algorithm and its notation
Why do we need to classify running time of algorithm into growth rates?
Big O-h notation and example
Big omega notation and example
Big theta notation and its example
best among the 3 notation
finding complexity f(n) for certain cases
1. Average case
2.Best case
3.Worst case
Searching
Sorting
complexity of Sorting
Conclusion
Questi sono i miei appunti di informatica sviluppati durante i lockdown. Di fatto costituiscono il libro di testo dei miei corsi. La grafica è ispirata a D&D 5e nella speranza di accattivarmi l'interesse dei ragazzi.
Questa parte è adatta ai ragazzi di 1-2° liceo SSA e di 2°-3° ITIS (in particolare ad indirizzo informatico)
Questi sono i miei appunti di informatica sviluppati durante i lockdown. Di fatto costituiscono il libro di testo dei miei corsi. La grafica è ispirata a D&D 5e nella speranza di accattivarmi l'interesse dei ragazzi.
Questa parte è adatta ai ragazzi di 1-2° liceo SSA e di 2°-3° ITIS (in particolare ad indirizzo informatico)
Una primissima introduzione al TDD per chi è a digiuno di test in generale e di TDD in particolare. Usa Java/Junit, ma è facimente adattabile ad altri linguaggi. 40-60 minuti.
Introduzione al linguaggio Java per chi ha esperienza di C++. Non si parla di OOP, solo di linguaggio.
Codice sorgente dell'esercizio finale qui: https://pastebin.com/R4yZGQcy
Queste slide dal titolo provocatorio cercano di dare l'idea che la stupidità e la pigrizia possono avere un effetto positivo nela programmazione per la ricerca di soluzioni semplici. Nello specifico caso parliamo di funzioni in C
Slides presentate all'incontro Didamatica 2016 a Udine. Si parla dei vantaggi didattici di una introduzione precoce dei principi Agili nella didattica informatica quotidiana (un mio pallino).
Slides di una breve conferenza che ho tenuto a scuola. Ritengo che lo stereotipo del programmatore brutto, scontrorso e antisociale sia in declino, ma perché ciò si realizzi davvero occorre affinare nuove abilità, le abilità sociali. Sia Online che Offline
Basato in parte sul lavoro seguente
http://www.slideshare.net/mastorey/msr-2012-keynote-storey-slideshare
Dopo molti anni mi sono ritrovato a insegnare Informatica in una terza. Questo è una breve slide che ho fatto per spiegare il ciclo for
C'è un errore nelle slides 7---9. ho scritto erroneamente una "," al posto del ";".
Introduzione al Controllo di versione (in generale) e al funzionamento di Git (in particolare). Upgrade di un'altra presentazione simile nelle basi ma incentrata su SVN.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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
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/
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
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
JMeter webinar - integration with InfluxDB and Grafana
Big O Notation
1. Big O Notation
Hamza Mushtaq
Islamia University Bahwalpur
+Marcello Missiroli
IIS Corni, Modena
A Brief Overview
2. Time Complexity
In the time complexity analysis we define the time as a
function of the problem size and try to estimate the growth
of the execution time with the growth in problem size.
3. Memory Space
•The space require by the program to save input data and results in memory
(RAM).
4. History of Big-O-Notation
•Big O notation (with a capital letter O, not a zero), also called Landau's
symbol, is a symbolism used in complexity theory, computer science, and
mathematics to describe the basic behavior of functions. Basically, it tells
you how fast a function grows or declines.
•Landau's symbol comes from the name of the German mathematician
Edmund Landau who invented the notation.
•The letter O is used because the rate of growth of a function is also called
its order.
6. It describes
•Efficiency of an Algorithm
•Time Factor of an Algorithm
•Space Complexity of an Algorithm
It is represented by O(n) where n is the number of operations.
7. Calculations
•Estimate all base operations (assignments,
operations, I/O, Memory access)
•Sum them all
•Remove constants
•Keep largest term only
9. O(1)
void print(int index,int values[]){
cout<<“the value at index”<<index<< “ is
”<<value[index];
}
You can directly access the index element of the
array, so no matter how big the array gets, the time it
takes to access the element won’t change.
CONSTANT COMPLEXITY (VERY GOOD)
10. O(n)
void print(int index, int values[]){
for(int x=0;x<index;x++) cout<<index<<“
.”<<value[index]<<endl;
}
As you increase the size of the array, the time for this
algorithm will increase proportionally. An array of 50 will take
twice the time as an array of 25 and half the time of an array
of 100.
LINEAR COMPLEXITY (GOOD)
11. O(n2)
int CompareToAllNumbers (int array1[ ] ,int
index1,int array2[ ],int index2){
for (int x = 0; x < index1; x++)
{bool isMin; for (int y = 0; y <
index2; y++)
{ if( array[x] > array[y])
isMin = false; }
if(isMin) break; }
return array[x];}
If you add one element to array, you need to go through the entire
rest of the array one additional time. As the input grows, the time
required to run the algorithm will grow quite quickly.
POLYNOMIAL COMPLEXITY (BAD THOUGH TOLERABLE)
12. O(n!)
•Travelling salesman problem
The number of cities increases the runtime also
increases accordingly by a factor of cities factorial.
•Enumerating all possible chess moves
All legal positions can be stored on 4.45*10^46 bits
More than the number of molecules of earth.
EXPONENTIAL COMPLEXITY
– REALLY BAD “INTRACTABLE” PROBLEMS
13.
14. Summary of Complexity
Constant complexity O(1)
Logarithmic complexity O(log(n))
Linear Complexity O(n)
Pseudolinear Complexity O(nlog(n))
Polynomial Complexity O(n
k
) k>=2
Exponential Complexity O(k
n
)
15. int CompareSmallestNumber (int array[ ])
{ int x, curMin;
curMin = array[0];
for (x = 1; x < 10; x++)
{ if( array[x] < curMin)
curMin = array[x];}
return curMin;}
int CompareToAllNumbers (int array[ ])
{bool is Min;
int x, y;
for (int x = 0; x < 10; x++)
{ isMin = true;
for (int y = 0; y < 10; y++)
{ if( array[x] > array[y])
isMin = false;
}if(isMin)
break; }
return array[x];}
We are having two different algorithms
to find the smallest element in the array
16. •Uses array to store elements
•Compares a temporary variable in the array
•The Big O notation for it is O(n)
This algorithm is efficient and effective as its
O is small as compare to the 2nd algorithm
And hence it gives the best result in less time.
•Uses array to store elements
•Compares the array with array itself
•The big O notation for it is O(n2)
This algorithm is less effective than the other
Beacause this algo has a high Big O and hence
It is more time taking and less effective.
Worst --Best ++
18. Best case:
When the algorithm takes less
run time w.r.t the given inputs/data.
Best case also defines as Big Ω.
Average case:
When the algorithm takes
average run time w.r.t to the input /data.
This is also known as BigΘ.
Worst case:
When the algorithm takes higher runtime
as compared to the given inputs/data.
Worst case is also called as Big O.
19. Best case:
1
Big Ω =Ω(k)≈1.
Average case:
n/2
BigΘ=Θ(n)≈n.
Worst case:
n
Big O = O(n)≈n .
Linear search (n elem) Binary search (n elem)
Best case:
1
Big Ω =Ω(k)≈1.
Average case:
log2(n)-1.
BigΘ=Θ(n)≈log(n).
Worst case:
log2(n)
Big O = O(n)≈log(n) .
20. Best case (time):
Ω(n)≈n.
Worst case (time):
O(n
2
)≈n
2
.
Worst case (space):
Big O = O(n)≈
2
.
Insertion sort (n elem) Selection sort (n elem)
Best case (time):
Ω(n)≈n..
Worst case (time):
O(n
2
)≈n
2
.
Worst case (space):
O(n)≈
2
.
21. Best case (time):
Ω(n)≈n.
Worst case (time):
O(n
2
)≈n
2
.
Worst case (space):
Big O = O(n)≈n
2
.
Bubble sort (n elem) Stupid sort (n elem)
Best case (time):
Ω(n)≈n..
Worst case (time):
O(n!)≈n
!
.
Worst case (space):
O(∞)≈∞ .