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Search results “Data mining tasks and methods of execution”
KNIME: A Tool for Data Mining
 
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KNIME is very helpful tool for Data Mining tasks like Clustering, Classification, Standard Deviation and Mean
Views: 30952 Sania Habib
Applying Data Mining Techniques to Computer Systems
 
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Modern computer systems desire several properties, such as high performance, reliability and manageability. To deliver these properties requires a lot of human effort which is costly and error-prone. In order to automate the procedure in development and management, the first step is to analyze and characterize systems by computers. The system data such as source code, development documents, execution traces, access traces, etc. provide a valuable asset for us to target the solution. In the meantime, the huge amount of data, however, renders a tedious and difficult task on managers and developers, and hence the hidden information would be difficult to extract. During this talk, I will present a novel approach to analyze various system data by applying data mining techniques. This approach can effectively obtain useful information hidden in huge amount of system data, and then such information can be exploited for improving system performance, reliability and manageability. Specifically, I have applied different data mining algorithms on different types of system data such as source code and access traces to achieve different goals including automated debugging and system behavior characterization. The results demonstrate that data mining is an effective and promising method to help us solve problems in computer systems.
Views: 73 Microsoft Research
Final Year Projects | An Ontology-Based Text-Mining Method to Cluster Proposals for Research
 
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Final Year Projects | An Ontology-Based Text-Mining Method to Cluster Proposals for Research Project Selection More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 3533 Clickmyproject
MatFast: In Memory Distributed Matrix Computation Processing and Optimization  - Yanbo Liang
 
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"The use of large-scale machine learning and data mining methods is becoming ubiquitous in many application domains ranging from business intelligence and bioinformatics to self-driving cars. These methods heavily rely on matrix computations, and it is hence critical to make these computations scalable and efficient. These matrix computations are often complex and involve multiple steps that need to be optimized and sequenced properly for efficient execution. This work presents new efficient and scalable matrix processing and optimization techniques based on Spark. The proposed techniques estimate the sparsity of intermediate matrix-computation results and optimize communication costs. An evaluation plan generator for complex matrix computations is introduced as well as a distributed plan optimizer that exploits dynamic cost-based analysis and rule-based heuristics The result of a matrix operation will often serve as an input to another matrix operation, thus defining the matrix data dependencies within a matrix program. The matrix query plan generator produces query execution plans that minimize memory usage and communication overhead by partitioning the matrix based on the data dependencies in the execution plan. We implemented the proposed matrix techniques inside the Spark SQL, and optimize the matrix execution plan based on Spark SQL Catalyst. We conduct case studies on a series of ML models and matrix computations with special features on different datasets. These are PageRank, GNMF, BFGS, sparse matrix chain multiplications, and a biological data analysis. The open-source library ScaLAPACK and the array-based database SciDB are used for performance evaluation. Our experiments are performed on six real-world datasets are: social network data ( e.g., soc-pokec, cit-Patents, LiveJournal), Twitter2010, Netflix recommendation data, and 1000 Genomes Project sample. Experiments demonstrate that our proposed techniques achieve up to an order-of-magnitude performance. Session hashtag: #EUai1"
Views: 522 Databricks
Application of data mining methods in diabetes prediction
 
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Application of data mining methods in diabetes prediction IEEE PROJECTS 2018-2019 TITLE LIST Call Us: +91-7806844441,9994232214 Mail Us: [email protected] Website: : http://www.nextchennai.com : http://www.ieeeproject.net : http://www.projectsieee.com : http://www.ieee-projects-chennai.com : http://www.24chennai.com WhatsApp : +91-7806844441 Chat Online: https://goo.gl/p42cQt Support Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Video Tutorials * Supporting Softwares Support Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * Remote Connectivity * Document Customization * Live Chat Support
Final Year Projects 2015 | Automated web usage data mining and recommendation system
 
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Including Packages ===================== * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 3419 Clickmyproject
parallel computing and types of architecture in hindi
 
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#Pds #pdc #parallelcomputing #distributedsystem #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)23 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice (sample Notes : https://goo.gl/fkHZZ1) To buy the course click here: https://goo.gl/E9NxXR if you have any query email us at [email protected] Index 1.Introduction to Parallel Computing and Types of Architecture 2.flynn’s classification or taxonomy in parallel computing 3.feng’s classification in parallel computing 4.Amdahl’s law in parallel computing 5.Pipelining Concept in Distributed System 6.Fixed point and Floating Point addition in Pipelining 7.Digit Product and Fixed Point Multiplication 8.Synchronization in process distribution system 9.Cristian algorithm 10.berkeley algorithm in process distribution system 11.Network time protocol in process distribution system 12.Logical clock in distributed system 13.Lamport’s logical clock algorithm in distributed system 14.Vector logical clock algorithm in distributed system 15.Lamports non token based algorithm in mutual execution 16.Ricart agarwala algorithm 17.Suzuki kasami algorithm with example 18.Raymonds algorithms 19.Bully and Ring Election algorithm in Distributed System 20.RMI remote method invocation 21.RPC(remote procedure call) in distributed system 22.Resources management in Distributed System 23.Load Balancing Algorithm and Design issues
Views: 197813 Last moment tuitions
Postman Tutorial for Automation | How to use Postman
 
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This postman tutorial will explain how to use postman for automation. The tool has become increasingly popular for testing and can execute complex tasks with ease. In this video we will give you codes, scripts and everything you need to setup postman effectively for your project. At Cuelogic we use it to deliver better quality code and in combination with newman automate manual tasks. The results have been great and in this video we walkthrough from setup to execution. ********************************************************************* Hire world class QAs - https://www.cuelogic.com/contact-us Get this video in a blog - https://www.cuelogic.com/blog/postman-tutorial-for-automation ********************************************************************* Follow us on Social Networks - Facebook - https://www.facebook.com/cuelogictechnologies/ Twitter - https://twitter.com/Cuelogic LinkedIn - https://in.linkedin.com/company/cuelogic-technologies Instagram - https://www.instagram.com/cuelogictechnologies/ Medium - https://medium.com/cuelogic-technologies Youtube - https://www.youtube.com/c/CuelogicTechnologies ********************************************************************* Links : http://blog.getpostman.com/2015/04/09/installing-newman-on-windows/ http://blog.getpostman.com/2014/10/28/using-csv-and-json-files-in-the-postman-collection-runner/ https://www.getpostman.com/docs/postman/environments_and_globals/variables ********************************************************************* Share this video - https://youtu.be/0cRGh-AxE2s
Spark basic tutorial for log mining
 
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Hello and welcome to this video tutorial on how to use apache spark for log mining. In this demo we are going to see apache spark application for processing large collection of data. This is kind of big data operation, where you have Tera bytes of Data collected over time and want to look for a particular error message. Something similar to what used for Metrix dashboard in normal BI operations. In this demo I’m going to show you how to execute this log mining exercise across spark cluster. It’s a kind of theory of operation.
Views: 2026 Navneet Kumar
A Survey on Trajectory Data Mining: Techniques and Applications | Final Year Projects 2016 - 2017
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 271 myproject bazaar
Machine Learning - Supervised VS Unsupervised Learning
 
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Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/ Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning. This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each. Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed! Explore many algorithms and models: Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction. Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine! Connect with Big Data University: https://www.facebook.com/bigdatauniversity https://twitter.com/bigdatau https://www.linkedin.com/groups/4060416/profile ABOUT THIS COURSE •This course is free. •It is self-paced. •It can be taken at any time. •It can be audited as many times as you wish. https://bigdatauniversity.com/courses/machine-learning-with-python/
Views: 94194 Cognitive Class
#HITB2018AMS CommSec D2 - Hiding Tasks via Hardware Task Switching - Kyeong Joo Jung
 
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Recently, malicious mining using CPUs has become a trend – mining where the task is not detected by the user is even more of a threat. We have worked to discover IA-32 vulnerabilities over the last couple of months and have found that by using hardware task switching method, we can execute another task that is undetectable by the OS from the normal user perspective. Currently hardware task switching method is not used but exists on modern computers as current task switching methods are managed by the underlying operating system. The important point of this research was that you can conceal these attacks from the user. Proof of the concealment will be shown with video demos during the presentation. We will also show that it is difficult to defend against hardware switching attacks because there are currently no tools that detect when the Global Descriptor Table has been modified. We have only studied IA-32 CPUs for now and have been able to create other schedulers that is undetectable in 32-bit OSes but there is a way to pull off this attack on 64-bit operating systems as well and we are actively exploring this area. === Kyeong Joo Jung is currently enrolled in the Masters program for computer science in Stonybrook University, SUNY Korea and is a member of B.o.B (Best of the Best) – Korea’s next generation security leader education program. He has interests in malware and rootkits and the team he is affiliated in is ‘Ajae.dll’ – a team built for researching rootkits and malware.
Data Mining IT 5th Session4 'MDX' (MultiDeminsional Expressions)
 
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Multidimensional Expressions (MDX) is a query language for OLAP databases. We will execute MDX to fetch data from the cube Which was already created in the previous session
Views: 273 Mounir Alyousef
Characteristics Of Task (High Performance Computing /Parallel Computing) (HINDI)
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 3644 5 Minutes Engineering
Clustering In Data Science | Data Science Tutorial | Simplilearn
 
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Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics. Data Science Certification Training - R Programming: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Clustering-Data-Science-a3It88zzbiA&utm_medium=SC&utm_source=youtube #datascience #datasciencetutorial #datascienceforbeginners #datasciencewithr #datasciencetutorialforbeginners #datasciencecourse What are the course objectives? This course will enable you to: 1. Gain a foundational understanding of business analytics 2. Install R, R-studio, and workspace setup. You will also learn about the various R packages 3. Master the R programming and understand how various statements are executed in R 4. Gain an in-depth understanding of data structure used in R and learn to import/export data in R 5. Define, understand and use the various apply functions and DPLYP functions 6. Understand and use the various graphics in R for data visualization 7. Gain a basic understanding of the various statistical concepts 8. Understand and use hypothesis testing method to drive business decisions 9. Understand and use linear, non-linear regression models, and classification techniques for data analysis 10. Learn and use the various association rules and Apriori algorithm 11. Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: IT professionals looking for a career switch into data science and analytics Software developers looking for a career switch into data science and analytics Professionals working in data and business analytics Graduates looking to build a career in analytics and data science Anyone with a genuine interest in the data science field Experienced professionals who would like to harness data science in their fields Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 4891 Simplilearn
Task Modeling using CTT's
 
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This video explains how you can do task modeling using a graphical tool called concurrent task trees (CTT's). Using an online pizza ordering platform it quickly describes the different tasks and relationships that can be used. Timestamps: 1. Different tasks: 0:48 2. Disabling relation/tasks: 1:11 3. Enabling relation/tasks: 2:13 4. Repeating tasks: 2:43 5. Enabling with information passing relation/tasks: 2:56 6. Choice relationship: 4:38 7. Optional tasks: 4:56 8. Concurrent relation/tasks: 5:30 9. Concurrent communicating relation/tasks: 5:49 10. Parallel relation/tasks: 6:22 11. Suspend-resume relation: 7:38 12. Task independence: 8:23 Links: W3 Concur Task Trees: https://www.w3.org/2012/02/ctt/ ConcurTaskTrees Environment (modelling software): http://hiis.isti.cnr.it/lab/research/CTTE/home
Convolutional Neural Network (CNN) | Convolutional Neural Networks With TensorFlow | Edureka
 
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( TensorFlow Training - https://www.edureka.co/ai-deep-learning-with-tensorflow ) This Edureka "Convolutional Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you in understanding what is Convolutional Neural Network and how it works. It also includes a use-case, in which we will be creating a classifier using TensorFlow. Below are the topics covered in this tutorial: 1. How a Computer Reads an Image? 2. Why can't we use Fully Connected Networks for Image Recognition? 3. What is Convolutional Neural Network? 4. How Convolutional Neural Networks Work? 5. Use-Case (dog and cat classifier) Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course. - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. Business Analysts who want to understand Deep Learning (ML) Techniques 4. Information Architects who want to gain expertise in Predictive Analytics 5. Professionals who want to captivate and analyze Big Data 6. Analysts wanting to understand Data Science methodologies However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio. - - - - - - - - - - - - - - Why Learn Deep Learning With TensorFlow? TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 97577 edureka!
“Big Data Mining Services and (...)” Dr. Domenico Talia (IC3K 2014)
 
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Keynote Title: Big Data Mining Services and Distributed Knowledge Discovery Applications on Clouds Keynote Lecturer: Domenico Talia Keynote Chair: Wil Van Der Aalst Presented on: 21-10-2014, Rome, Italy Abstract: Digital data repositories are more and more massive and distributed, therefore we need smart data analysis techniques and scalable architectures to extract useful information from them in reduced time. Cloud computing infrastructures offer an effective support for addressing both the computational and data storage needs of big data mining and parallel knowledge discovery applications. In fact, complex data mining tasks involve data- and compute-intensive algorithms that require large and efficient storage facilities together with high performance processors to get results in acceptable times. In this talk we introduce the topic and the main research issues, then we present a Data Mining Cloud Framework designed for developing and executing distributed data analytics applications as workflows of services. In this environment we use data sets, analysis tools, data mining algorithms and knowledge models that are implemented as single services that can be combined through a visual programming interface in distributed workflows to be executed on Clouds. The first implementation of the Data Mining Cloud Framework on Azure is presented and the main features of the graphical programming interface are described. Presented at the following Conference: IC3K, International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management Conference Website: ic3k.org/
Views: 15 YoutubeINSTICC
Web Service Task
 
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This tutorial video of SSIS 2012 Tutorial Videos illustrates how to use Web Service Task to send parameters and receive results from a web service into xml file. Samples of work with World Cup 2010 Football championship to send country name as parameter with variable and get results related to that country illustrated in this video.
Views: 7941 RADACAD
Data Mining with R and SAS enterprise miner - Part 1
 
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Introduction to Data structures in R UCF University
Views: 189 taha mokfi
MSBI - SSIS - Maintenance Plan Tasks In SSIS - Part-45
 
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MSBI - SSIS - Maintenance Plan Tasks In SSIS - Part-45
Script Task in SSIS
 
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SSIS INTERVIEW QUESTION talks about WHY .NET KNOWLEDGE REQUIRED FOR SSIS PACKAGE DEVELOPERS? .NET REQUIRED FOR SSIS DEVELOPERS? What is Script Task in SSIS C#.Net code in SSIS tasks Hello world example in SSIS Part of MSBI Interview Questions
Views: 527 Training2SQL MSBI
DATA PROFILING TASK
 
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SSIS te kullanılan Data Profiling Task bileşenin anlatımı.
Python Web Scraping Real estate website in Lithuania - Aruodas.lt (DEMO)
 
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This is just Data scraping demonstration that was executed by Python. In this Python video I demonstrate the web scraper that I programmed from zero by Python. I scraped one of the most popular real estate website in Lithuania - aruodas.lt. For the first stage of my Web Scraping project I just scraped District and Price of real estate objects (flats for sell) data. Final results are saved to CSV file. BeautifulSoup library was used to execute scraping task in this Python application. The goal of this Python project is to create the biggest database of real estate ads for Data mining and Data visualization purposes. If you need explaining how I executed and prepared it, you can contact me in LinkedIn: https://www.linkedin.com/in/bielinskas/ Vytautas.
Informatica Tutorial For Beginners | Informatica PowerCenter | Informatica Training | Edureka
 
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( Informatica Tutorial - https://www.edureka.co/informatica ) This Edureka Informatica Tutorial For Beginners will help you in understanding the various components of Informatica PowerCenter in detail with examples. This video helps you to learn following topics: 1. What do Businesses need today? 2. Business Intelligence 3. Extract, Transform, Load 4. Data Warehousing 5. Why Informatica PowerCenter? 6. Informatica PowerCenter 7. Informatica PowerCenter Client Tools 8. Informatica Architecture 9. Data Visualization Check our Informatica playlist here https://goo.gl/TmX6Fv. What is Informatica Blog: https://goo.gl/hKXhV8 Other Related Blog Post: https://goo.gl/tq8qBu https://goo.gl/ey7YMC https://goo.gl/bUFckp https://goo.gl/c6ttKu Subscribe to our channel to get video updates. Hit the subscribe button above. #Informatica #Informaticatutorial #Informaticapowercenter #Informaticaonlinetraining How it Works? 1. This is a 6 Week Instructor led Online Course, 25 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Informatica PowerCenter Certification training is designed to help you become a top Informatica Developer and Administrator. During this course, our expert Informatica instructors will help you: 1. Understand and identify different Informatica Products 2. Describe Informatica PowerCenter architecture & its different components 3. Use PowerCenter 9.x components to build Mappings, Tasks, Workflows 4. Describe the basic and advanced features functionalities of PowerCenter 9.X transformations 5. Understand Workflow Task and job handling 6. Describe Mapping Parameter and Variables 7. Perform debugging, troubleshooting, error handling and recovery 8. Learn to calculate cache requirement and implement session cache 9. Execute performance tuning and Optimisation 10. Recognise and explain the functionalities of the Repository Manager tool. 11. Identify how to handle services in the Administration Console 12. Understand techniques of SCD, XML Processing, Partitioning, Constraint based loading and Incremental Aggregation 13. Gain insight on ETL best practices using Informatica - - - - - - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course : 1. Software Developers 2. Analytics Professionals 3. BI/ETL/DW Professionals 4. Mainframe developers and Architects 5. Individual Contributors in the field of Enterprise Business Intelligence - - - - - - - - - - - - - - - - Why learn Informatica? Informatica provides the market's leading data integration platform. Tested on nearly 500,000 combinations of platforms and applications, the data integration platform interoperates with the broadest possible range of disparate standards, systems, and applications. This unbiased and universal view makes Informatica unique in today's market as a leader in the data integration platform. It also makes Informatica the ideal strategic platform for companies looking to solve data integration issues of any size. The topics related to Informatica have extensively been covered in our course 'Informatica PowerCenter 9.X Developer & Admin’. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 171257 edureka!
An Empirical Performance Evaluation of Relational Keyword Search Systems
 
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Title: An Empirical Performance Evaluation of Relational Keyword Search Systems Domain: Data Mining Abstract: In the past decade, extending the keyword search paradigm to relational data has been an active area of research within the database and information retrieval (IR) community. A large number of approaches have been proposed and implemented, but despite numerous publications, there remains a severe lack of standardization for system evaluations. This lack of standardization has resulted in contradictory results from different evaluations, and the numerous discrepancies muddle what advantages are proffered by different approaches. In this paper, we present a thorough empirical performance evaluation of relational keyword search systems. Our results indicate that many existing search techniques do not provide acceptable performance for realistic retrieval tasks. In particular, memory consumption precludes many search techniques from scaling beyond small datasets with tens of thousands of vertices. We also explore the relationship between execution time and factors varied in previous evaluations; our analysis indicates that these factors have relatively little impact on performance. In summary, our work confirms previous claims regarding the unacceptable performance of these systems and underscores the need for standardization—as exemplified by the IR community—when evaluating these retrieval systems. Key Features: 1. The success of keyword search stems from what it does not require—namely, a specialized query language or knowledge of the underlying structure of the data. Internet users increasingly demand keyword search interfaces for accessing information, and it is natural to extend this paradigm to relational data. This extension has been an active area of research throughout the past decade. However, we are not aware of any research projects that have transitioned from proof-of-concept implementations to deployed systems. 2. We conduct an independent, empirical performance evaluation of 7 relational keyword search techniques, which doubles the number of comparisons as previous work. 3. Our results do not substantiate previous claims regarding the scalability and performance of relational keyword search techniques. Existing search techniques perform poorly for datasets exceeding tens of thousands of vertices. 4. We show that the parameters varied in existing evaluations are at best loosely related to performance, which is likely due to experiments not using representative datasets or query workloads. 5. Our work is the first to combine performance and search effectiveness in the evaluation of such a large number of systems. Considering these two issues in conjunction provides better understanding of these two critical tradeoffs among competing system designs. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 – 2018 data mining projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 ieee titles 8. 2017 – 2018 base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e projects 14. 2017 – 2018 m.e projects 15. 2017 – 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique
Views: 1646 InnovationAdsOfIndia
Performance Metrics for Parallel System ll Execution Time & Total Overhead Explained in Hindi
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5-MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5-MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 6228 5 Minutes Engineering
SSIS Tutorial Part 68- Percentage Sampling Transformation in SSIS Package
 
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In this SQL Server Integration Services (SSIS) Tutorial video you will learn What is Percentage Sampling Transformation in SSIS Package How to use Percentage Sampling Transformation in SSIS Package How to take sample of input records in Data Flow Task in SSIS Package What are blocking Transformations in SSIS Package To follow Step by Step SQL Server Integration Services(SSIS) Video Tutorial By TechBrothers, please open below link. http://www.techbrothersit.com/2014/12/ssis-videos.html Twitter https://twitter.com/AamirSh48904922 Facebook https://www.facebook.com/TechBrothersIt
Views: 4647 TechBrothersIT
TN TRB Computer Science Syllabus - Business Computing #2 System Development Life Cycle SDLC
 
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Tamil Nadu TRB Computer Instructors GRADE1 Exam Syllabus Tamil Nadu TRB Computer Science Syllabus - Business Computing - SDLC -------------- TRB COMPUTER SCIENCE - BUSINESS COMPUTING System Development Life Cycle Application Development Life Cycle Software Development Life Cycle (SDLC) * Framework to define tasks performed at each step / phase * Structure followed by development team * Defines a methodology for improving the quality of s/w and process. * Also called Software Development Process. Phases of SDLC 1. Planning 2. Requirement Analysis 3. Design 4. Implementation / Coding 5. Testing 6. Deployment 7. Maintenance Customer / Client has business idea and money to get start it. 1. Planning planning the requirements what / when / where / how Eg: registration login dashboard products list logout ... 2. Requirement Analysis The team gather collect detail requiremnt and document everyting Product owner Operations Developers Testers Registration username input field password field checkbox to accept tnc submit button Save data to DB Login Username field Password field Submit button Read user information from DB Log user into the system session Dashboard After login redirect to here after logout redirect from here other data to display Logout Logout button Clear the user session Prevernt other hack - Software specification document 3. Design Business rules Layout Color scheme for the application Supporting device Supporting browsers for online applications Programing languages / DB tool / Frameworks Server design DB design Applicaiton architecture - Design specification document 4. Implementation Operations team setup the development servers Developers wirtting code for the application Design team planing the UI Testers analyze requirement and write test cases with testing plans sometimes find some usablity issue while wirting test cases, that leads to redesign the UI 5. Testing Execute all test cases validate all the requirements all functionalities are working as expected 6. Deployment Operations team moves the application to production can have staging real users will use the application 7. Maintenance Enhancement in application / servers / DB Add new feature Production bug fix SDLC Models: 1. Waterfall Model 2. Iterative Model 3. Sprial Model 4. V-Model 5. Big Bang Model * Agile
MSBI - SSIS - WMI Event Watcher Task SSIS - Part-46
 
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MSBI - SSIS - WMI Event Watcher Task SSIS - Part-46
Sources Of Overhead in Parallel Program (High Performance Computing)  Explained in Hindi
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5-MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5-MINUTES ENGINEERING 📚📚📚📚📚📚📚📚 #Parallelprogram #Parallelcomputation #HighperformanceComputation
Views: 5739 5 Minutes Engineering
MapReduce : Simplified Data Processing on Large Cluster
 
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Big Data Analytics CS 7070 Presentation by Adit Chawdhary Slide 1: Hello, In this presentation I am going to talk about the Hadoop framework and its different components. Also, I will answer the questions posted in the paper on MapReduce. The image here depicts the input chunks, which are fed to the workers which execute the mapper function parallelly, shown by M and gives intermediate key-value pairs which are then sorted and grouped. Followed by the execution of the reducer function, shown by R to give the output. Slide 2: The main idea of Hadoop and MapReduce paradigm is to use commodity hardware and make the software resilient to hardware failures. And is an example of scaling out architecture. A MapReduce job splits the input data-set into M chunks of 16 to 64 Mega-bytes and replicates it, which are processed by the map tasks in a parallel manner across different workers in clusters, which run the user's map program. The framework sorts the intermediate key/value pairs. These intermediate key/value pairs are buffered in local disks. Which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system. The master is responsible for scheduling the jobs' component tasks on the slaves, monitoring them and re-executing the failed tasks. Typically the compute nodes and the storage nodes are the same, that is, the MapReduce framework and the Hadoop Distributed File System are running on the same set of nodes. This is the idea of bringing the computation to the data storage. Also helps converse the network bandwidth. Slide 5: Map Reduce has a file system which replicates the data to provide availability and readability on top of unreliable hardware. Slide 6: We subdivide the map phase into M pieces and reduce phase into R pieces as described previously. Such that M and R are much larger than the number of machines. Having each worker perform many different tasks improves the dynamic load balancing and also spends up recovery: when a worker fail, the map tasks completed can be spread out to other worker machines. There are atomic commits of a map and reduce task outputs to achieve sequential execution. Each in-progress task writes its output to a temporary file. A reduce task produces one such file whereas the map task produces R such files. One per reduce task. When a map task is completed, the worker sends a message to the master and includes the names of the R temporary files in the message.
Views: 71 Adit Chawdhary
WACV18: A Simple yet Effective Model for Zero-Shot Learning
 
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Xi Hang Cao, Zoran Obradovic, Kyungnam Kim Zero-shot learning has tremendous application value in complex computer vision tasks, e.g. image classification, localization, image captioning, etc., for its capability of transferring knowledge from seen data to unseen data. Many recent proposed methods have shown that the formulation of a compatibility function and its generalization are crucial for the success of a zero-shot learning model. In this paper, we formulate a softmax-based compatibility function formulation, and more importantly, propose a regularized empirical risk minimization objective to optimize the function parameter which leads to a better model generalization. In the comparisons to eight baseline models on four benchmark datasets, our model achieved the highest average ranking. Our model was effective even when the training set size was small and significantly outperforming an alternative state-of-the-art model in generalized zero-shot recognition tasks.
Lifelong Machine Learning and Computer Reading the Web (Part 1)
 
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Authors: Bing Liu, University of Illinois at Urbana-Champaign Estevam R. Hruschka, Federal University of Săo Carlos Zhiyuan (Brett) Chen, Department of Computer Science, University of Illinois at Chicago Abstract: This tutorial introduces Lifelong Machine Learning (LML) and Machine Reading. The core idea of LML is to learn continuously and accumulate the learned knowledge, and to use the knowledge to help future learning, which is perhaps the hallmark of human learning and human intelligence. By us- ing prior knowledge seamlessly and effortlessly, we humans can learn without a lot of training data, but current machine learning algorithms tend to need a huge amount of training data. LML aims to mimic this human capability. Machine Reading is a research area with the goal of building systems to read natural language text. Among different approaches employed in Machine Reading, this tutorial focuses on projects and approaches that use the idea of LML. Most current machine learning (ML) algorithms learn in isolation. They are designed to address a specific problem using a single dataset. That is, given a dataset, an ML algorithm is executed on the dataset to build a model. Although this type of isolated learning is very useful, it does not have the ability to accumulate past knowledge and to make use of the knowledge for future learning, which we believe are critical for the future of machine learning and data mining. LML aims to design and develop computational systems and algorithms with this capability, i.e., to learn as humans do in a lifelong manner. In this tutorial, we introduce this important problem and the existing LML techniques and discuss opportunities and challenges of big data for lifelong machine learning. We also want to motivate researchers and practitioners to actively explore LML as the big data provides us a golden opportunity to learn a large volume of diverse knowledge, to connect different pieces of it, and to use it to raise data mining and machine learning to a new level. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 633 KDD2016 video
High performance computing /Parallel Computing 
:One To All Broadcast and All To One Reduction(HIND)
 
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Hello dosto mera naam hai shridhar mankar aur mein aap Sabka Swagat karta hu 5-minutes engineering channel pe. This channel is launched with a aim to enhance the quality of knowledge of engineering,here I am going to introduce you to every subject of computer engineering like artificial intelligence database management system software modeling and designing Software engineering and project planning data mining and warehouse data analytics Mobile communication Mobile computing Computer networks high performance computing parallel computing Operating system Software programming SPOS web technology internet of things design and analysis of algorithm
Views: 8333 5 Minutes Engineering
Improving Speed Of Communication Operation(One To All Broadcast,All To One Reduction And All Reduce)
 
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All To All Reduction And All To All Broadcast https://youtu.be/_HQPf7MoYDg All To One Reduction And One To All Broadcast https://youtu.be/fsllCdhWQYc All Reduce https://youtu.be/nLipVoZTDCc Scatter And Gather https://youtu.be/a2hTxkp8mcY 📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 2623 5 Minutes Engineering
Create an SSIS Data Profiling Task In SQL Server
 
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I Use SQL Server Management Studio and SQL Server Data Tools to Create an SSIS Data Profiling Task. Data profiling should be established as a best practice for every data warehouse, BI, and data migration project! AnthonySmoak.com @AnthonySmoak
Views: 2210 Anthony B. Smoak
MSBI - SSIS - Groups And How To Group Tasks - Part-36
 
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MSBI - SSIS - Groups And How To Group Tasks - Part-36
MSBI - SSIS - WMI Data Reader Task - Part-48
 
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MSBI - SSIS - WMI Data Reader Task SSIS - Part-48
Cost Optimal Way ll Effect Of Granularity on Performance of Parallel System Explained with Example
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 6299 5 Minutes Engineering
Using Data Flow Task of SSIS Part 2
 
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In this video you learn about how to use the SQL task to execute the SQL command
Views: 951 Madhukar Singh
Supporting Investigative Analysis through Visual Analytics
 
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Carsten Goerg, Professor - University of Colorado Medical School Presents... Supporting Investigative Analysis through Visual Analytics Today's analysts and researchers are faced with the daunting task of analyzing and understanding large amounts of data, often including textual documents and unstructured data. Sensemaking tasks, such as finding relevant pieces of information, formulating hypotheses, and combining facts to establish supporting or contradicting evidence, become more and more challenging as the data grow in size and complexity. Visual analytics aims at developing methods and tools that integrate computational approaches with interactive visualizations to support analysts in performing these types of sensemaking tasks. In this talk, I first briefly introduce the fields of investigative analysis and visual analytics and then discuss methods for the design, development, and evaluation of visual analytics systems in the context of the Jigsaw project. Jigsaw is a visual analytics system for exploring and understanding document collections. It represents documents and their entities visually in order to help analysts examine them more efficiently and develop theories more quickly. Jigsaw integrates computational text analyses, including document summarization, similarity, clustering, and sentiment analysis, with multiple coordinated views of documents and their entities. It has a special emphasis on visually illustrating connections between entities across the different documents. Brief biography: Carsten Grg is a faculty member in the Computational Bioscience Program and in the Pharmacology Department in the University of Colorado Medical School. He received a Ph.D. in computer science from Saarland University, Germany in 2005 and worked as a Postdoctoral Fellow in the Graphics, Visualization & Usability Center at the Georgia Institute of Technology before joining the University of Colorado. Dr. Grg's research interests include visual analytics and information visualization with a focus on designing, developing, and evaluating visual analytics tools to support the analysis of biological and biomedical datasets.
Views: 1574 SCIInstitute
Effect Of Granularity On Performance Of Parallel System Explained with Solved Example in Hindi
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 9817 5 Minutes Engineering
Launchpad Online: Automating YouTube stats with Google Apps Script
 
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Have you ever been asked by your boss to do something simple but long and tedious, such as counting up the view counts for your corporate videos and your competitors'? What about outside of work where you and your gamer friends are competing to see whose replay clips get the most views? This is a boring task that's easily processed from a Google Sheets spreadsheet with the help of Google Apps Script and the YouTube Data API. In this Launchpad Online episode, Google engineer Wesley Chun (http://google.com/+WesleyChun) and special guests propose a scenario that may not be so different from real life including a line-by-line code walkthough that can get you started building a solution in JavaScript that will make your boss' (and you) happy! LINKS * New to Google Apps Script video (http://goo.gl/1sXeuD) * Spreadsheet with Apps Script code (http://goo.gl/SVxoCt) * Apps Script Execution API documentation (http://goo.gl/ryD6Q3) - Subscribe to the brand new Firebase Channel: https://goo.gl/9giPHG
Views: 11644 Google Developers
BDA - Big Data with Stratosphere
 
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The talk will present a programming model for big data analytics, with a particular focus on our research in a massively parallel data processor in the Stratosphere project. We will present a new flavor of data processor that goes beyond the popular map/reduce paradigm. We propose a programming model based on second order functions that describe what we call parallelization contracts (PACTs). PACTs are a generalization of the map/reduce programming model, extending it with additional higher order functions and output contracts that give guarantees about the behavior of a function. A PACT program is transformed into a data flow for a massively parallel execution engine, which executes its sequential building blocks in parallel and provides communication, synchronization and fault tolerance. The concept of PACTs allows the system to abstract parallelization from the specification of the data flow and thus enables several types of optimizations on the data flow. The system as a whole is as generic as map/reduce systems, but can provide higher performance through optimization and adaptation of the system to changes in the execution environment. Moreover, it enables the execution of tasks that traditional map/reduce systems cannot execute without mixing data flow program specification and parallelization, like joins, time-series analysis or data mining operations. We will present our research vision and research results that we have achieved during the last year. We will also highlight our research agenda for the upcoming year.
Views: 40 Microsoft Research
Shmoocon 2012: Malware Visualization in 3D
 
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This video is part of the Infosec Video Collection at SecurityTube.net: http://www.securitytube.net Shmoocon 2012: Malware Visualization in 3D PDF :- http://www.shmoocon.org/2012/presentations/Danny_Quist-3dmalware-shmoocon2012.pdf Malware reverse engineering is greatly helped by visualization techniques. In this talk I will show you my 3D visualization enhancements to VERA for creating compelling, and useful displays of malware. This new tool provides a new method to visualize running code, show concurrent running threads of execution, visualize the temporal relationships of the code, and illustrate complicated packer original entry point detection. Real! Live! Reverse Engineering! of the past year of malware will show the utility of the program on in-the-wild samples. Danny Quist is a research scientist at Los Alamos National Laboratory and the founder of Offensive Computing, LLC. His research is in automated analysis methods for malware with software and hardware assisted techniques. He consults with both private and public sectors on system and network security. His interests include malware defense, reverse engineering, exploitation methods, virtual machines, and automatic classification systems. Danny holds a Ph.D. from the New Mexico Institute of Mining and Technology. He is the master of the Five Point Exploding Packer Technique. Danny has presented at several industry conferences including Blackhat, RSA, ShmooCon, Vizsec, and Defcon.
Views: 1727 SecurityTubeCons
SSIS Tutorial Part 80- Import Column Transformation in SSIS Package
 
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In this video you will learn how to use Import Column Transformation in SSIS to load image files, text file, pdf file and all other formats to SQL Server Table. This video is also used for SQL Server Integration Services (SSIS ) Interview Question " Which transformation can I use in SSIS to importimage files to a Table?" To follow Step by Step SQL Server Integration Services(SSIS) Video Tutorial By TechBrothers, please open below link. http://www.techbrothersit.com/2014/12/ssis-videos.html
Views: 10039 TechBrothersIT
Free Inference and Instant Training: Breakthroughs and Implications
 
01:42:29
The fact that many commonly used networks take hours to days for training has motivated recent research towards reducing training time. On the other hand networks, once trained, are heavyweight dense linear algebra computations, usually requiring expensive acceleration to execute in real time. However, recent advances in algorithms, hardware, and systems have broken through these barriers dramatically. Models that took days to train are now reported to be trainable in under an hour. Further, with model optimization techniques and emerging commodity silicon, these models can be executed on the edge or in the cloud at surprisingly low energy and dollar cost. This session will present the ideas and techniques underlying these breakthroughs and discuss the implications of this new regime of “free inference and instant training.” See more at https://www.microsoft.com/en-us/research/video/free-inference-and-instant-training-breakthroughs-and-implications/
Views: 1862 Microsoft Research
Informatica Power Center Live Training Orientation For Beginners Apr 2016
 
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www.itelearn.com Informatica Power Center Live Training Informatica provides the market’s leading data integration platform. Tested on nearly 500,000 combinations of platforms and applications, the data integration platform inter operates with the broadest possible range of disparate standards, systems and applications. This unbiased and universal view makes Informatica unique in today’s market as a leader in the data integration platform. It also makes Informatica the ideal strategic platform for companies looking to solve data integration issues of any size. Informatica Power Center 9.X Developer will introduce the participants to work with the Power Center. Developers can use Informatica to create, execute, as well as administer, monitor and schedule ETL processes and understand how these are used in data mining operations. This course covers Informatica development techniques, error handling, data migration, performance tuning and more. Date: The Orientation Session on 11th April 2016. Duration: 1 hour Timing: 6:30 PM Pacific. Trainer: Seshasayana Reddy Day 01 & Day 02 Demo sessions will begin from 13th & 14th April 2016 Paid sessions will begin from 18th April to 27th May 2016 @6:30 PM PST Informatica Power Center Live Training Informatica provides the market’s leading data integration platform. Tested on nearly 500,000 combinations of platforms and applications, the data integration platform inter operates with the broadest possible range of disparate standards, systems and applications. This unbiased and universal view makes Informatica unique in today’s market as a leader in the data integration platform. It also makes Informatica the ideal strategic platform for companies looking to solve data integration issues of any size. Informatica Power Center 9.X Developer will introduce the participants to work with the Power Center. Developers can use Informatica to create, execute, as well as administer, monitor and schedule ETL processes and understand how these are used in data mining operations. This course covers Informatica development techniques, error handling, data migration, performance tuning and more. Date: The Orientation Session on 11th April 2016. Duration: 1 hour Timing: 6:30 PM Pacific. Trainer: Seshasayana Reddy Day 01 & Day 02 Demo sessions will begin from 13th & 14th April 2016 Paid sessions will begin from 18th April to 27th May 2016 @6:30 PM PST Register Here Course Objectives: 1. Understand and identify different Informatica Products 2. Describe Informatica PowerCenter architecture & its different components. 3. Use PowerCenter 9.x components to build Mappings, Tasks, Workflows 4. Describe the basic and advanced features functionalities of PowerCenter 9.X transformations. 5. Understand Workflow Task and job handling. 6. Describe Mapping Parameter and Variables 7. Perform debugging, troubleshooting, error handling and recovery. 8. Learn to calculate cache requirement and implement session cache. 9. Execute performance tuning and Optimization. 10. Recognize and explain the functionalities of the Repository Manager tool. 11. Identify how to handle services in the Administration Console. 12. Understand techniques of SCD, XML Processing, Partitioning, Constraint based loading and Incremental Aggregation. 13. Gain insight on ETL best practices using Informatica. ITeLearn provides the Best Selenium Online Training with real time experts with more than 15 years of real time experience.ITeLearn provide integrated IT training services and the complete range of IT training to provide all the requirements of both individual learners and corporate clients also. Please contact us for more details. http://www.itelearn.com/ USA: +1-314-827-5272 India: 91-837-432-3742 Email: [email protected] Social links: FaceBook - http://www.facebook.com/Itelearn Twitter - https://twitter.com/ITeLearn Linkedin - Add Connection OR Join our Professional Network on [email protected] Google+ - Add to Circles on [email protected]
Views: 748 ITeLearn