in this how the split() function work with string in python is discussed
TO DOWNLOAD MORE INFORMATION:
https://computerassignmentsforu.blogspot.com/p/stringinpythonsplit.html
VIDEO TUTORIAL LINK:
https://youtu.be/6BvslDmk1Z8
It covers- Introduction to R language, Creating, Exploring data with Various Data Structures e.g. Vector, Array, Matrices, and Factors. Using Methods with examples.
A graph search (or traversal) technique visits every node exactly one in a systematic fashion. Two standard graph search techniques have been widely used: Depth-First Search (DFS) Breadth-First Search (BFS)
in this how the split() function work with string in python is discussed
TO DOWNLOAD MORE INFORMATION:
https://computerassignmentsforu.blogspot.com/p/stringinpythonsplit.html
VIDEO TUTORIAL LINK:
https://youtu.be/6BvslDmk1Z8
It covers- Introduction to R language, Creating, Exploring data with Various Data Structures e.g. Vector, Array, Matrices, and Factors. Using Methods with examples.
A graph search (or traversal) technique visits every node exactly one in a systematic fashion. Two standard graph search techniques have been widely used: Depth-First Search (DFS) Breadth-First Search (BFS)
What are Data structures in Python? | List, Dictionary, Tuple Explained | Edu...Edureka!
YouTube Link: https://youtu.be/m9n2f9lhtrw
** Python Certification Training: https://www.edureka.co/data-science-python-certification-course **
This Edureka video on 'Data Structures in Python' will help you understand the various data structures that Python has built into itself such as the list, dictionary, tuple and more. Further, we will also understand stacks, queues, trees and how they are implemented in Python using classes and functions. The video is divided into the following parts:
What are Data Structures?
Why are Data Structures needed?
Types of Data Structures in Python
Built-In Data Structures
Lists
Dictionary
Tuple
Sets
User-Defined Data Structure
Array
Stack
Queue
Linked List
Tree
Graph
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Bridging research and practice in the evaluation of English Language Learners.
MinnesotaDept. of Education
Minneapolis, MN
January 27, 2011
Samuel O. Ortiz, Ph.D.
St. John’s University
Scheduling jobs on identical parallel machinessadasidha08
This presentation was made as an assignment in my University. Here I have tried to explain one of the oldest approximation algorithm The List Scheduling Algorithm. The idea is to schedule some jobs in some identical parallel machaine.
Best corporate-r-programming-training-in-mumbaiUnmesh Baile
Vibrant Technologies is headquarted in Mumbai,India.We are the best Teradata training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best Teradata Database classes in Mumbai according to our students and corporates
Multinomial Logistic Regression with Apache SparkDB Tsai
Logistic Regression can not only be used for modeling binary outcomes but also multinomial outcome with some extension. In this talk, DB will talk about basic idea of binary logistic regression step by step, and then extend to multinomial one. He will show how easy it's with Spark to parallelize this iterative algorithm by utilizing the in-memory RDD cache to scale horizontally (the numbers of training data.) However, there is mathematical limitation on scaling vertically (the numbers of training features) while many recent applications from document classification and computational linguistics are of this type. He will talk about how to address this problem by L-BFGS optimizer instead of Newton optimizer.
Bio:
DB Tsai is a machine learning engineer working at Alpine Data Labs. He is recently working with Spark MLlib team to add support of L-BFGS optimizer and multinomial logistic regression in the upstream. He also led the Apache Spark development at Alpine Data Labs. Before joining Alpine Data labs, he was working on large-scale optimization of optical quantum circuits at Stanford as a PhD student.
What are Data structures in Python? | List, Dictionary, Tuple Explained | Edu...Edureka!
YouTube Link: https://youtu.be/m9n2f9lhtrw
** Python Certification Training: https://www.edureka.co/data-science-python-certification-course **
This Edureka video on 'Data Structures in Python' will help you understand the various data structures that Python has built into itself such as the list, dictionary, tuple and more. Further, we will also understand stacks, queues, trees and how they are implemented in Python using classes and functions. The video is divided into the following parts:
What are Data Structures?
Why are Data Structures needed?
Types of Data Structures in Python
Built-In Data Structures
Lists
Dictionary
Tuple
Sets
User-Defined Data Structure
Array
Stack
Queue
Linked List
Tree
Graph
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Bridging research and practice in the evaluation of English Language Learners.
MinnesotaDept. of Education
Minneapolis, MN
January 27, 2011
Samuel O. Ortiz, Ph.D.
St. John’s University
Scheduling jobs on identical parallel machinessadasidha08
This presentation was made as an assignment in my University. Here I have tried to explain one of the oldest approximation algorithm The List Scheduling Algorithm. The idea is to schedule some jobs in some identical parallel machaine.
Best corporate-r-programming-training-in-mumbaiUnmesh Baile
Vibrant Technologies is headquarted in Mumbai,India.We are the best Teradata training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best Teradata Database classes in Mumbai according to our students and corporates
Multinomial Logistic Regression with Apache SparkDB Tsai
Logistic Regression can not only be used for modeling binary outcomes but also multinomial outcome with some extension. In this talk, DB will talk about basic idea of binary logistic regression step by step, and then extend to multinomial one. He will show how easy it's with Spark to parallelize this iterative algorithm by utilizing the in-memory RDD cache to scale horizontally (the numbers of training data.) However, there is mathematical limitation on scaling vertically (the numbers of training features) while many recent applications from document classification and computational linguistics are of this type. He will talk about how to address this problem by L-BFGS optimizer instead of Newton optimizer.
Bio:
DB Tsai is a machine learning engineer working at Alpine Data Labs. He is recently working with Spark MLlib team to add support of L-BFGS optimizer and multinomial logistic regression in the upstream. He also led the Apache Spark development at Alpine Data Labs. Before joining Alpine Data labs, he was working on large-scale optimization of optical quantum circuits at Stanford as a PhD student.
Intelligent Process Management & Visualization TechnologiesDafna Levy
The presentation was prepared for a keynote which I was honored to give at the TAProViz Process Visualization workshop at BPM 2014 conference.
TAPROVIZ'14 was the 3rd International Workshop on Theory and Applications of Process Visualization , organized by Ross Brown, Simone Kriglstein and Stefanie Rinderle-Ma. The workshop took place at the 12th Business Process Management (BPM) in Eindhoven University of Technology (TU/e) during the second week of September (7-9-2014 until 12-9-2014).
BPM Goes to School: Case study - Birkbeck, University of London Bizagi
How can Higher Education providers deliver cost-effective IT services and prove better value for money? What are the key factors to implementing successful workflow solutions within the highly regulated academic sector? And what are the best ways to achieve employee buy-in as you strive towards a Center of Excellence?
This Case study: Achieve Operational Excellence through BPM - explains how Birkbeck, University of London, utilized BPMS to significantly streamline administration processes and improve student services.
With help from Bizagi, Birkbeck improved the timely application of student loans and sped up its Student Status Amendment Program by 90%.
Hear the story from BPM advocate James Smith, Director of Process Improvement & Corporate Information Systems, at Birkbeck, University of London.
First presented at London's Ovum BPM Forum 2014.
Have you ever been involved in developing a strategy for loading, extracting, and managing large amounts of data in salesforce.com? Join us to learn multiple solutions you can put in place to help alleviate large data volume concerns. Our architects will walk you through scenarios, solutions, and patterns you can implement to address large data volume issues.
Rule Based Asset Management Workflow Automation at NetflixHostedbyConfluent
"At Netflix, we deal with millions of digital assets every day. Hours of video clips, along with audio, text and image assets are ingested for various purposes. Several workflows are then executed on them; such as inspection, transcoding, editing, logging, etc. These assets can also be used in machine learning workflows, either to train these models, or to get content insights. Not all workflows are applicable to all assets, and some workflows depend on other workflows to run. Additionally, new workflows are introduced regularly, and they need to be executed on existing assets, as well.
We implemented a workflow rule engine that allows users to define rules and conditions to specify the applicable workflows for assets, based on their types, metadata and states. In order to make this system scalable and fault tolerant, we utilize Kafka to send out events on asset state changes (on create, update, workflow completion, etc.) with minimal information in the payload (asset id and version). The rule engine then enriches this payload by fetching additional metadata, evaluates it against the workflow rules, triggers applicable workflows based on the outcome, and monitors their results by listening to the workflow events.
By using a highly available Kafka setup, we can easily scale, handle ETL cases such as migrations, replay messages if needed without impacting asset ingestions."
In this Meetup Arik Lerner – Liveperson Team lead of Java Automation, Performance & Resilience , will talk about How we measure our services, By End2End testing which become one of the most critical Monitor tool in LP .
Over 200K tests runs per day providing statistics and insights into the problem as they happen.
Arik will go through different topics and stages of the journey and share details that led to current results .
Part of the menu topics are : The Awakens of the End2End Insights
• How we measure our services using synthetic user experience
• Measuring through analytics & insights
• How we collect our data
• How we debug our services? Hint: video recording, HAR (Http archive), KIbana , Dashboard analytics & insights
• Future logs App correlation with End2End data
• Our tools: Selenium, Jenkins and cutting edge technologies such as Kafka & ELK (Elastic search, Logstash and Kibana)
In this Meetup, Arik will host Ali AbuAli- NOC Team Leader , who will talk about the e2e usage on his day 2 day work.
In this Meetup Arik Lerner – Liveperson Team lead of Java Automation, Performance & Resilience , will talk about How we measure our services, By End2End testing which become one of the most critical Monitor tool in LP .
Over 200K tests runs per day providing statistics and insights into the problem as they happen.
Arik will go through different topics and stages of the journey and share details that led to current results .
Part of the menu topics are : The Awakens of the End2End Insights
• How we measure our services using synthetic user experience
• Measuring through analytics & insights
• How we collect our data
• How we debug our services? Hint: video recording, HAR (Http archive), KIbana , Dashboard analytics & insights
• Future logs App correlation with End2End data
• Our tools: Selenium, Jenkins and cutting edge technologies such as Kafka & ELK (Elastic search, Logstash and Kibana)
In this Meetup, Arik will host Ali AbuAli- NOC Team Leader , who will talk about the e2e usage on his day 2 day work.
While many organizations have already implemented business intelligence (BI) solutions, these solutions do not provide insight into underlying business processes.
Process Intelligence solutions provide an integrated view of your company’s performance from process perspectives, and would alert you just in time, to correct deviations that might occur in your on-going processes.
Learn more about how it's implemented and the technologies that were used.
One of the things I enjoy most in process analysis is combining technologies. The idea is that deliverables generated by one technology, can be associated nicely with deliverables generated by other technologies. Such combinations reveal new magnificent insights about our processes, and opportunities for improving them.
The three technologies that I find extremely friendly and "opened minded" for such a challenge are: the BPM manager of Priority ERP, Disco - an Automatic Process Discovery tool, and QlikView -a business discovery tool.
The attached presentation includes practical examples to get you inspired. So, go ahead and give it a try!
Discovery of Production Processes - TutorialDafna Levy
The attached demonstration includes some of the treasures which can be revealed by those who record their production data, and instructions for taking a self test-drive with Disco, a process mining tool of Fluxicon.
Becoming a Process Minding Organization - a Solution OverviewDafna Levy
While process mining is perceived by many as a very cool technology, yet companies are not willing to embrace it as warmly and quickly as expected. Managers still expect more convincing and significant added value. Another issue might be with offering process mining technology as somewhat detached solution and not taking into account BI solutions, which might already exist in a company.
The attached presentation proposes a solution which is based on an integration of ERP, BI and process mining technologies.
The technologies used enable a quick, easy and low cost “jumpstart” in order to increase ‘process minding’ in a company. Hope you find it useful!
Process mining with Disco (Hebrew) עבריתDafna Levy
Introducing Process Mining concepts, advantages and a solution based on a case study with Priority ERP
הכרות עם Process Mining
כלי עזר לניתוח תהליכים
מיועד ליועצים, מיישמים ומפתחים
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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!
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.
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/
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
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.
3. BPM Flow Chart for Service Calls in Priority
Statuses, paths and rules are defined for the process in Priority ERP.
4. The Process in Reality
Can we visualize how our BPM flow chart is actually executed?
Are there any bottlenecks in the process?
Are there problematic paths taken between statuses?
What about fulfilling SLA conditions?
Which and how many employees have received business rules
notifications?
Why are there performance differences among our branches?
6. Data Requirements for Process Mining
1 2 3
Attribute
Cases
Events
Process Instance or
1
Case identifier
Status or Activity
2
attribute (process step)
One timestamp for
3
status change, start or
completion of activity
7. Generating the Required Data in Priority
Create a query in the To Do List History form, or
Generate SQL query (with WINDBI or ODBC)
Export the data and save as Excel sheet or in CSV file format.
10. Import Configuration Screen in Disco
1
2
1. Mapping the log columns to Disco predefined columns
2. Starting to import the log.
11. Map View of the Discovered Process Flow
Statuses & paths are displayed with their frequencies.
Colors, paths’ thickness and numbers are used for indication.
12. Details of a Specific Status
Checking SLA conformance
We can filter the log to display only calls with the current status.
13. Process Animation
Status changes are shown at their relative, actual speed.
Process bottlenecks are immediately spotted.
14. Process Statistics
Events represents the total number of status changes.
Cases represents the total number of Service Calls documents.
15. Activity Statistics
Viewing performance metrics of the statuses in the process.
We can export the calculated table data as a CSV file for further
analysis.
16. Resource Statistics
Viewing performance metrics of the employees in the process.
We can export graphs as images for our reports.
17. Variants and Individual Cases
Each variant represents a unique sequence of statuses.
We can see that multiple service calls can follow a specific variant.
18. Performance Comparison of Employees
After configuring both Status and Assigned to columns as Activity in
the Import screen.
19. The Process Flow Among Employees
After swapping the Resource and the Activity columns in the Import screen.
22. Performance Filter Optional Settings
After filtering by Case Duration, we realized that 4% of our cases
run longer than 10 days.
23. Performance Display – Mean Duration
Process bottlenecks and repetitions are quickly discovered.
24. Frequency Display – the Maximal Status Repetitions
Exceptional repetitions of statuses in the process are immediately
discovered.
25. Comparing Slow and Fast Processes
Toggling easily between different logs.
26. Locating Problematic Paths with the Follower Filter
A serial should be sent to the manufacturer’s lab only after
inspection.
Locate Service calls which do not obey this instruction
Optionally use the “4-eyes principle” definition.
27. Viewing the Service Calls with the Filtered Path
After locating such calls, we advise to add a business rule in order to
prevent the free path between the statuses Received and Manuf.
Lab.
29. Analyzing Business Rules defined in Priority
The rule: If the Service Call remains in the Technician status longer
than 12 hours, send an E-mail to the assigned technician.
31. 2nd Step: Define the Condition with the Performance Filter
We already see that 30% of the cases remained in the Technician status
longer than 12 hours.
32. Analyzing the results
Locating the employees that were assigned the Technician status which
received E-mail notifications.
35. Project Management View
Maintaining multiple Data sets
Documenting our work
Exporting projects for backup.
36. Next Steps
Learn more about Disco here:
http://fluxicon.com/disco/
Install a demo and play with the Sandbox project
Perform a pilot project with us!
Contact
Dafna Levy Email: dafnal@nool.co.il
Phone: +972 (0)54-6881739
Web: http://bpmintro.wordpress.com
Anne Rozinat Email: anne@fluxicon.com
Phone: +31(0)62-4364201
Web: http://fluxicon.com