Search results “Streaming data mining”
Twitter Streaming API - Data Mining #6
Data Mining twitter streaming API using tweepy. Jupyter Notebook: http://nbviewer.ipython.org/github/twistedhardware/mltutorial/blob/master/notebooks/data-mining/6.%20Twitter%20Streaming%20API.ipynb
Views: 10510 Roshan
Twitter API with Python: Part 1 -- Streaming Live Tweets
In this video, we make use of the Tweepy Python module to stream live tweets directly from Twitter in real-time. In order to follow along, you will require: 1. A Twitter account, 2. Python. Assuming you have both of these, go ahead and install the "tweepy" module by running the following command inside a terminal shell. pip install tweepy Once we have this, we make a Twitter application that will be used to interface with Python code we will write, and allow us to stream and process live tweets. After creating the Twitter application, we will leverage the "tweepy" module to stream the tweets. Relevant Links: Part 1: https://www.youtube.com/watch?v=wlnx-7cm4Gg Part 2: https://www.youtube.com/watch?v=rhBZqEWsZU4 Part 3: https://www.youtube.com/watch?v=WX0MDddgpA4 Part 4: https://www.youtube.com/watch?v=w9tAoscq3C4 Part 5: https://www.youtube.com/watch?v=pdnTPUFF4gA Tweepy Website: http://www.tweepy.org/ Tweepy Docs: https://tweepy.readthedocs.io/en/v3.5.0/ Create Twitter Application: https://apps.twitter.com/ GitHub Code for this Video: https://github.com/vprusso/youtube_tutorials/tree/master/twitter_python/part_1_streaming_tweets This video is brought to you by DevMountain, a coding boot camp that offers in-person and online courses in a variety of subjects including web development, iOS development, user experience design, software quality assurance, and salesforce development. DevMountain also includes housing for full-time students. For more information: https://devmountain.com/?utm_source=Lucid%20Programming Do you like the development environment I'm using in this video? It's a customized version of vim that's enhanced for Python development. If you want to see how I set up my vim, I have a series on this here: http://bit.ly/lp_vim If you've found this video helpful and want to stay up-to-date with the latest videos posted on this channel, please subscribe: http://bit.ly/lp_subscribe
Views: 17951 LucidProgramming
Advanced Data Mining with Weka (2.4: MOA classifiers and streams)
Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 4: MOA classifiers and streams http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 2765 WekaMOOC
IoT Big Data Stream Mining (Part 1)
Authors: Latifur Khan, Department of Computer Science, Erik Jonsson School of Engineering & Computer Science, The University of Texas at Dallas João Gama, Laboratory of Artificial Intelligence and Decision Support, University of Porto Albert Bifet, Telecom ParisTech Abstract: The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. Advanced analysis of big data streams from sensors and devices is bound to become a key area of data mining research as the number of applications requiring such processing increases. Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in IoT stream mining. This tutorial is a gentle introduction to mining IoT big data streams. The first part introduces data stream learners for classification, regression, clustering, and frequent pattern mining. The second part deals with scalability issues inherent in IoT applications, and discusses how to mine data streams on distributed engines such as Spark, Flink, Storm, and Samza. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 1542 KDD2016 video
Data Stream Algorithms
The age of Big Data has propelled innovations in streaming algorithms and synopses data structures. In this talk we will cover a few novel methods which have been developed to extract maximum information in minimal space and time. - Sandeep Joshi
Streaming Data: How to Move from State to Flow - Whiteboard Walkthrough
In this week’s Whiteboard Walkthrough Part II, Ted Dunning, Chief Application Architect at MapR, talks about the design freedom gained by adopting a micro-services architecture based on streaming data. When you move – one step at a time - from an old style architecture that suffers from too much dependence on a shared global state database to a stream-based flow architecture, the isolation between micro-services results in reduced strain on the original database, improved flexibility and often speed. If you would like to know more about building a stream-based architecture, read about MapR Streams as part of the MapR Converged Platform (https://www.mapr.com/products/mapr-streams) or see the book 'Streaming Architecture' (https://www.mapr.com/ebooks/streaming-architecture/preface.html). Watch Part I: https://youtu.be/4lUxf5pzAHs
Views: 5619 MapR Technologies
Mining Twitter with Python : 4 - Using the Twitter Streaming API
The Streaming API is one of the favorite ways of getting a massive amount of data without exceeding the rate limits. Lets see how this differs from REST API and the Search API. ----- ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE Link to playlist: https://goo.gl/WIHiEy ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 1380 Sukhvinder Singh
Sketching Streaming Data: Efficient Collection & Processing | Lectures On-Demand
Professor Anna Gilbert, Department of Mathematics - University of Michigan Data Mining- The 4th University of Michigan Data Mining Workshop Sponsored by Computer Science and Engineering, Yahoo!, and Office of Research Cyberinfrastructure (ORCI) Faculty, staff, and graduate students working in the fields of data mining, broadly construed. This workshop will present techniques: models and technologies for statistical data analysis, Web search technology, analysis of user behavior, data visualization, etc. We speak about data-centric applications to problems in all fields, whether it is in the natural sciences, the social sciences, or something else.
Twitter Streaming API in Python. Data mining Demonstration
I have made some web scrappers before. You can check it out here: - https://www.youtube.com/watch?v=-vYiAfLDEVw - https://www.youtube.com/watch?v=oQkIp1Bk_vg - https://www.youtube.com/watch?v=pqAdxZWFkTM and more. It was so easy and fun work by using Beautiful Soup and Selenium. This time I going to build a powerful Twitter scanner. Twitter provide three main APIs: the REST API, Streaming API, and the Ads API. I will use Twitter Streaming API for gathering all related by a keyword tweets in real time. Yes - in real time! First of all before all the work you must to create an application in your Twitter account. Then you will generate your Consumer ID, Secret keys, Token key and Token secret keys. You have to obtain credentials to be able to collect data from Twitter. Before coding I recommend install requests-oauthlib 0.8.0 module from here: https://pypi.python.org/pypi/requests-oauthlib to your default Python directory. Good is that my code will write the real time tweets to output csv file after each tweet is come to the pocket. Next step: implement sentimental analysis for tweets. Still searching for solutions. Vytautas Bielinskas LinkedIn: https://www.linkedin.com/in/bielinskas
Views: 391 Vytautas Bielinskas
GOTO 2017 • Fast Data Architectures for Streaming Applications • Dean Wampler
This presentation was recorded at GOTO Chicago 2017 http://gotochgo.com Dean Wampler - Big Data Architect at Lightbend & O'Reilly Author ABSTRACT The Big Data world is evolving from batch-oriented to stream-oriented. Instead of capturing data and then running batch jobs to process it, processing is done as the data arrives to extract [...] Download slides and read the full abstract here: https://gotochgo.com/2017/sessions/37 https://twitter.com/gotochgo https://www.facebook.com/GOTOConference http://gotocon.com
Views: 5391 GOTO Conferences
Streaming Data
A brief introduction to Streaming Data.
Views: 162 Frank Blau
Cloud Data Streaming
Ever wonder how Cloud Data Streaming works? See our new video on the topic. Here's a link to the Strategic Roadmap engagement I mention at the end: https://intricity.attach.io/r1x~TiWdz Also, here's how to get connected to talk with an Intricity Specialist: https://www.intricity.com/intricity101/
Views: 553 Intricity101
Why and how to transform a REST API into a Data Streaming API Audrey Neveu
We know interactivity is the key to keep our user’s interest alive but we can’t reduce animation to UI anymore. Twitter, Waze, Slack… users are used to have real-time data in applications they love. But how can you turn your static API into a stream of data? By pulling? Pushing? Webhook-ing? When talking about data streaming, we often think about WebSockets. But have you ever heard of Server-Sent Events? In this tools-in-action we will compare those technologies to understand which one you should opt for depending on your usecase, and I’ll show you how we have been reducing the amount of data to transfer even further with JSON-Patch. And because real-time data is not only needed by web (and because it’s much more fun), I’ll show you how we can make a drone dance on streamed APIs. Audrey is Developer Relations at Streamdata.io. She’s a passion driven developer with experience in both web frontend and backend development, specialised in APIs and BigData. Heavily involved in the European wide Java Community, she’s part of Devoxx4Kids, a not-for-profit global initiative to get children coding.
Views: 2608 Devoxx
Anomaly Detection in Telecommunications Using Complex Streaming Data | Whiteboard Walkthrough
In this Whiteboard Walkthrough Ted Dunning, Chief Application Architect at MapR, explains in detail how to use streaming IoT sensor data from handsets and devices as well as cell tower data to detect strange anomalies. He takes us from best practices for data architecture, including the advantages of multi-master writes with MapR Streams, through analysis of the telecom data using clustering methods to discover normal and anomalous behaviors. For additional resources on anomaly detection and on streaming data: Download free pdf for the book Practical Machine Learning: A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman https://www.mapr.com/practical-machine-learning-new-look-anomaly-detection Watch another of Ted’s Whiteboard Walkthrough videos “Key Requirements for Streaming Platforms: A Microservices Advantage” https://www.mapr.com/blog/key-requirements-streaming-platforms-micro-services-advantage-whiteboard-walkthrough-part-1 Read technical blog/tutorial “Getting Started with MapR Streams” sample programs by Tugdual Grall https://www.mapr.com/blog/getting-started-sample-programs-mapr-streams Download free pdf for the book Introduction to Apache Flink by Ellen Friedman and Ted Dunning https://www.mapr.com/introduction-to-apache-flink
Views: 4331 MapR Technologies
Introduction to Data Streaming (C. Escoffier, G. Zamarreño)
Dealing with real-time, in-memory, streaming data is a unique challenge and with the advent of the smartphone and IoT (trillions of internet connected devices), we are witnessing an exponential growth in data at scale. Learning how to implement architectures that handle real-time streaming data, where data is flowing constantly, and combine it with analysis and instant search capabilities is key for developing robust and scalable services and applications. In this university session, we will look at how to implement an architecture like this, using reactive open source frameworks. An architecture based on the Swiss rail transport system will be used throughout the university. Technologies: Java (attendees must be comfortable with Java 8), Infinispan, Eclipse Vert.x, Apache Kafka, OpenShift.
Views: 446 Devoxx FR
Web Scraping - Data Mining #1
Using LXML for web scraping to get data about Nobel prize winners from wikipedia. This is done using IPython Notebook and pandas for data analysis. Github/NBViewer Link: http://nbviewer.ipython.org/github/twistedhardware/mltutorial/blob/master/notebooks/data-mining/1.%20Web%20Scraping.ipynb
Views: 18260 Roshan
Data Stream Basics
Fundamental issues relating to the transmission of digital (data) streams such as coding, signal element identification, synchronizing, and framing structures.
Views: 8413 noessllc
How to do real-time Twitter Sentiment Analysis (or any analysis)
This tutorial video covers how to do real-time analysis alongside your streaming Twitter API v1.1 feed. In this case, for example, we use the Sentdex Sentiment Analysis API, http://sentdex.com/sentiment-analysis-api/, though you can use ANY API like this, or just your own custom function too. If you don't already have a twitter stream set up, here is some sample code and tutorial video for it: http://sentdex.com/sentiment-analysisbig-data-and-python-tutorials-algorithmic-trading/how-to-use-the-twitter-api-1-1-to-stream-tweets-in-python/ Sentdex.com Facebook.com/sentdex Twitter.com/sentdex
Views: 69255 sentdex
Data analytics with Microsoft Azure
Explore the comprehensive set of services Microsoft Azure has for ingesting, storing and analyzing data of almost all types of scales, spanning table, file, streaming and other data types. The Azure platform provides tools across the data analytics' life-cycle. www.azure.com/essentials (Azure Essentials)
Views: 14930 Microsoft Mechanics
Keynote: How Netflix Leverages Big Data - Brian Sullivan, Director of Streaming Analytics, Netflix
Netflix is the world's leading internet television network. That didn't happen by accident or simple fortune - we are data-driven as part of our culture, and have built the tools needed to navigate the unchartered waters of delivering internet video at scale and becoming the first truly global storyteller in movies and television. About Brian Sullivan Brian Sullivan is the Director of the Streaming Data Science and Engineering team at Netflix, the world’s leading Internet television network. His team builds analytic systems and delivers insight into the streaming activity across hundreds of client devices, world-class server systems and content delivery networks to serve up a third of peak internet traffic in North America. Brian’s prior experience spans the analytic stack from software engineering, data architecture, reporting/visualization and analysis. He has also worked across a number of domains including virtual worlds, banking, retail, automobile traffic measurement and fingerprint recognition systems.
SAMOA: A Platform for Mining Big Data Streams by Gianmarco De Francisci Morales
NoSQL matters Conference in Barcelona, Spain 2013 - SAMOA: APlatform for Mining Data Systems by Gianmarco De Francisci Morales. http://2013.nosql-matters.org/bcn/ Streaming data analysis in real time is becoming the fastest and most efficient way to obtain useful knowledge from what is happening now, allowing organizations to react quickly when problems appear or to detect new trends helping to improve their performance. In this talk, we present SAMOA, an upcoming platform for mining big data streams. SAMOA is a platform for online mining in a cluster/cloud environment. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as S4 and Storm. SAMOA includes algorithms for the most common machine learning tasks such as classification and clustering. Slides are available: http://2013.nosql-matters.org/bcn/wp-content/uploads/2013/12/SAMOA-NoSQLMatters2013.pdf
DataMining12-L12: Streaming + HeavyHitters (1 of 3)
Video Lectures by Prof. Jeff M. Phillips given as courses in the School of Computing at the University of Utah. Topics include Data Mining, Computational Geometry, and Big Data Algorithmics.
Views: 153 Jeff Phillips
Getting Tweets, Trends, and User Timeline from Twitter using R
Includes working with r for, - getting tweets from twitter - saving data in a csv file - getting worldwide and local twitter trends - getting user timeline Machine Learning videos: https://goo.gl/WHHqWP R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 19307 Bharatendra Rai
Heron: Real-time Stream Data Processing at Twitter
Storm has long served as the main platform for real-time analytics at Twitter. However, as the scale of data being processed in real- time at Twitter has increased, along with an increase in the diversity and the number of use cases, many limitations of Storm have become apparent. We need a system that scales better, has better debug-ability, has better performance, andis easier to manage – all while working in a shared cluster infrastructure. We considered various alternatives to meet these needs, and in the end concluded that we needed to build a new real-time stream data processing system. This talk will present the design and implementation of the new system, called Heron. Heron is now the de facto stream data processing engine inside Twitter, and we will share our experiences from running Heron in production.
Views: 8120 @Scale
Apache Flume Tutorial | Twitter Data Streaming Using Flume | Hadoop Training | Edureka
( ** Hadoop Training: https://www.edureka.co/hadoop ** ) This Edureka Flume tutorial will explain you the fundamentals of Flume. It will also give you a brief on apache flume's architecture along with a demo on Twitter Data Streaming using Apache Flume. Below topics are covered in this tutorial: 1. Need for apache flume 2. Introduction to Flume 3. Advantages of Flume 4. Apache Flume Architecture 5. Twitter Data Streaming Check our complete Hadoop playlist here: https://goo.gl/hzUO0m Subscribe to our channel to get video updates. Hit the subscribe button above. ------------------------------------------------------------------------------------------------------- #BigDataAnalytics #BigDataApplications #UsecasesofBigData #BigDataHadoopCertificationTraining #BigDataMastersProgram #HadoopCertification Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ ------------------------------------------------------------------------------------------------------ How does it work? 1. This is a 5 Week Instructor-led Online Course, 40 hours of assignment and 30 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 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 Big Data and Hadoop online training is designed to help you become a top Hadoop developer. During this course, our expert Hadoop instructors will help you: 1. Master the concepts of HDFS and MapReduce framework 2. Understand Hadoop 2.x Architecture 3. Setup Hadoop Cluster and write Complex MapReduce programs 4. Learn data loading techniques using Sqoop and Flume 5. Perform data analytics using Pig, Hive and YARN 6. Implement HBase and MapReduce integration 7. Implement Advanced Usage and Indexing 8. Schedule jobs using Oozie 9. Implement best practices for Hadoop development 10. Work on a real life Project on Big Data Analytics 11. Understand Spark and its Ecosystem 12. Learn how to work in RDD in Spark ---------------------------------------------------------------------- Who should go for this course? If you belong to any of the following groups, knowledge of Big Data and Hadoop is crucial for you if you want to progress in your career: 1. Analytics professionals 2. BI /ETL/DW professionals 3. Project managers 4. Testing professionals 5. Mainframe professionals 6. Software developers and architects 7. Recent graduates passionate about building a successful career in Big Data --------------------------------------------------------------------- Why Learn Hadoop? Big Data! A Worldwide Problem? According to Wikipedia, "Big data is collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications." In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, it is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success! The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. The good news is - Hadoop has become an integral part for storing, handling, evaluating and retrieving hundreds of terabytes, and even petabytes of data. --------------------------------------------------------------------- Opportunities for Hadoopers! Opportunities for Hadoopers are infinite - from a Hadoop Developer, to a Hadoop Tester or a Hadoop Architect, and so on. If cracking and managing BIG Data is your passion in life, then think no more and Join Edureka's Hadoop Online course and carve a niche for yourself! Please write back to us at [email protected] or call us at +91 88808 62004 for more information. --------------------------------------------------------------------- Please write back to us at [email protected] or call us at +91 8880862004 for more information.
Views: 1502 edureka!
R Shiny: Email and Text Message Alerts Based on Streaming Sensor Data
Github repo: https://github.com/fissehab/Email_text_alerting_with_Shiny
Views: 3006 Fisseha Berhane
ASSISTments Data Mining Competition 2017: Can you predict student careers from click stream data
For more information, please visit our website here https://sites.google.com/view/assistmentsdatamining/
Views: 603 Neil Heffernan
Data ingestion, stream processing and sentiment analysis pipeline using Twitter data example
Follow the conversation between Lena and Suz and learn about setting up a data ingestion and processing system consisting of event producer, reliable event aggregation and consumer using Twitter client, Event Hubs and Spark on Azure Databricks as an example. Lena and Suz are also discussing alternative options for stream processing, and how it can be used for various scenarios, including IoT, and how to apply machine learning to streaming data by showing an example of sentiment analysis on tweets coming in real-time. Useful links: https://lenadroid.github.io/posts/connecting-spark-and-eventhubs.html https://lenadroid.github.io/posts/offset-enqueuetime-spark-eventhubs.html Create a Free Account (Azure): https://aka.ms/azft-oss
Views: 2163 Microsoft Developer
Mining Big Data Streams with Apache SAMOA - Albert Bifet - JOTB16
In this talk, we present Apache SAMOA, an open-source platform for mining big data streams with Apache Flink, Storm and Samza. Real time analytics is becoming the fastest and most efficient way to obtain useful knowledge from what is happening now, allowing organizations to react quickly when problems appear or to detect new trends helping to improve their performance. Apache SAMOA includes algorithms for the most common machine learning tasks such as classification and clustering. It provides a pluggable architecture that allows it to run on Apache Flink, but also with other several distributed stream processing engines such as Storm and Samza.
Views: 791 J On The Beach 2018
IoT Big Data Stream Mining (Part 3)
Authors: Latifur Khan, Department of Computer Science, Erik Jonsson School of Engineering & Computer Science, The University of Texas at Dallas João Gama, Laboratory of Artificial Intelligence and Decision Support, University of Porto Albert Bifet, Telecom ParisTech Abstract: The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. Advanced analysis of big data streams from sensors and devices is bound to become a key area of data mining research as the number of applications requiring such processing increases. Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in IoT stream mining. This tutorial is a gentle introduction to mining IoT big data streams. The first part introduces data stream learners for classification, regression, clustering, and frequent pattern mining. The second part deals with scalability issues inherent in IoT applications, and discusses how to mine data streams on distributed engines such as Spark, Flink, Storm, and Samza. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 252 KDD2016 video
Bit heroes stream #48 (¯―¯٥) Datamining fail
PG-13 Warning - 36:30-37:00 Donate to receive praise from a weird guy over the internet: https://streamlabs.com/link2012philanthropist Random reminder that you are awesome ^_^ Please be respectful in chat and in the comments. Jerks don't make this world better. Ignoring the bad and promoting the good is what makes a difference ❤️ My policy is like if this video/stream was worth your time, and dislike it if it was not. Subscribe if you want, I need 1000 subs for monetization. Share if others might find my videos entertaining or insightful :)
Views: 34 link 2012
Anomaly Detection: Algorithms, Explanations, Applications
Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 8920 Microsoft Research
The Bull Rush #1 | Clash Course, Hero Balance, Data Mining, and Streaming
First ever Bull Rush covering Clash Course, Tripp and Imani hero balance, Data mining, and the streamers initiative. -- Watch live at http://www.twitch.tv/ggunleashed Discussions: 0:20 - Sven Clash Course 4:21 - The Future of Clash Course 14:18 - Hero Balance (Tripp) 26:02 - Hero Balance (Imani) 32:13 - Datamining New Maps 41:42 - Thoughts on Datamining 47:38 - Streamer's Initiative 1:06:40 - Signoff
Views: 31 GG Unleashed
Unstructured Text Event Streaming Analysis Demo with SAS
http://www.sas.com/en_us/software/data-management/event-stream-processing.html Analyze both unstructured and structured streaming data and make instant, accurate decisions. Understand high-velocity big data while it's in motion to know what requires action, and what can be ignored. SAS EVENT STREAM PROCESSING As large amounts of data flow into your business at lightning speed, you have to act fast before that data is stored or becomes obsolete. Slow response times lead to lost opportunities – or missed red flags. With SAS Event Stream Processing, you can analyze high-velocity big data while it’s in motion, helping you know what requires action, and what can be ignored. LEARN MORE ABOUT SAS EVENT STREAM PROCESSING http://www.sas.com/en_us/software/data-management/event-stream-processing.html SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 22157 SAS Software
Video Streaming Tip: Using Unlimited Smartphone Data on the Big Screen
When it comes to movie night - bigger is better. But a lot of the best options for streaming via cellular unlimited data plans are limited to "on device data" - blocking the connection from easily being shared with your big screen in your living room. The way around this is to use your smartphone or tablet to drive the big screen directly via a hard-wired HDMI video cable. In this quick video, we show how this works so you can legitimately use your unlimited data plan for streaming video to the BIG SCREEN! ----------------------------- For more video streaming & entertainment on the road tips: http://www.rvmobileinternet.com/tv Products shown in this video: Apple Lightning to HDMI Adapter: http://amzn.to/2d79WWz Android MHL Options: http://amzn.to/2czqcvw http://amzn.to/2dpexpf --------------------------------------- Chris Dunphy & Cherie Ve Ard of http://www.RVMobileInternet.com and authors of 'The Mobile Internet Handbook' and hosts of RVMobileInternet.com. We've been on the road full time since 2006, working remotely and sucking up mobile bandwidth. We also personally blog & share about our travels at http://www.technomadia.com
Views: 9808 Technomadia
In the old days of yore, even before computers were invented, "reactive streaming fast data" was already known as on-line algorithms. This "elegant weapon for a more civilized age" can now make a come-back due to the rare alignment of planets of Scala and Akka. In this talk we want to give a couple examples of how practical problems from the worlds of genome sequencing, machine learning and data mining can be solved using the tools from Akka toolbox. In particular, our focal point will be akka-streams package but we will also use akka-http and (just) Actors. We will mix obscure algorithms found in dust-covered textbooks with hottest newest features from the streams ecosystem to solve real problems from domains of bio-technology and computer security. Thanks to the reactive streaming approach and back-pressure, we won't care whether our data is big or small. And if mentioned planets bend the light in favourable way, we may even do some live-coding together. About speaker: Technical Architect at GFT Poland and Lecturer at the University of Łódź by day, sporadic akka project contributor by night. Interested in how software works on low level, he does not find big frameworks appealing. This is the main reason why he loves Scala, a very expressive language that allows one to build right abstractions quickly without losing control over details. Jan is an active member of JUG Łódź and occasional conference speaker. Currently he is busy with a Big Data project for one of major investment banks. In his spare time he loves to dust-off some old issue of computer science journal only to find out that everything has already been invented before he even was born. See also: @gosubpl
Fast Database and Data Streaming Operations using Graphics Processors
We present novel techniques to utilize the high computational power of graphics processing units (GPUs) to significantly accelerate many of the traditional general purpose algorithms on CPUs. As graphics processors are primarily designed to perform fast display of geometric primitives, we abstract many of the essential database and data mining algorithms using basic graphics operations. Our algorithms use efficient data representations and utilize the inherent parallelism in the single instruction multiple data (SIMD) units and the vector processing functionalities of the GPUs to efficiently evaluate the boolean combinations of predicates, aggregates, and join queries. Graphics processors are optimized for processing data streams. We present deterministic algorithms to efficiently estimate quantiles and frequencies in large data streams. We utilize the high computational power and the memory bandwidth on a GPU to perform sorting on a GPU. The sorting algorithm is used as a main computational component for the construction of epsilon-approximate quantile and frequency summaries. We have applied our algorithm to data streams consisting of more than 100 million elements on a 3.4GHz PC with a NVIDIA 6800 Ultra GPU and achieved 2-4 times performance improvement over optimized CPU-based algorithms. Our recent research focuses on using GPUs for sorting very large databases composed of hundreds of gigabytes of data using low-end commodity PCs. Experimental studies on the SortBenchmark indicate that external sorting is highly memory-intensive. As the GPUs internally have a dedicated memory interface, we present an efficient hybrid sorting algorithm to perform the computation on both the GPU and CPU, in parallel. Experimental results on a low-end PC with a NVIDIA 7800 GTX graphics co-processor indicate higher performance than optimized CPU-based algorithms on a high-end PC with 3.6 GHz Dual Xeon processors.
Views: 670 Microsoft Research
Apache Spark Streaming - Listen to a local streaming data using PySpark -- Explained
In this video I have explained how to read a streaming log file using PySpark with an interval of 10 seconds(batch process value). In linux and mac machine netcat (https://en.wikipedia.org/wiki/Netcat) is available for creating local network connections. | Developer Bytes - Like and Share this Video, Subscribe and Support us . ****************************************************************** JOIN Developer Bytes & Support us : YouTube : https://www.youtube.com/channel/UCX4nwbrO1_wG76n2pXgfeNw/?sub_confirmation=1 Facebook: https://www.facebook.com/developer.bytes.tech
Views: 3518 Developer Bytes
Data mining solution by integrating Spark and Cassandra Big Data Spain 2013
http://www.bigdataspain.org Abstract: http://www.bigdataspain.org/2013/conference/data-mining-solution-by-integrating-spark-and-cassandra Integrating C* and Spark gives us a system that combines the best of both worlds. The goal of this integration is to obtain a better result than using Spark over HDFS because Cassandra´s philosophy is much closer to RDD's philosophy than what HDFS is. Session presented at Big Data Spain 2013 Conference 7th Nov 2013 Kinépolis Madrid Event promoted by: http://www.paradigmatecnologico.com Slides: https://speakerdeck.com/bigdataspain/efficient-solution-integrating-spark-and-cassandra-by-alvaro-agea-and-luca-rosellini-big-data-spain-2013
Views: 2732 Big Data Spain
Data Science Experience: Sentiment Analysis of Twitter Hashtags Using Spark Streaming
Find more videos in the IBM Watson and Cloud Platform Learning Center at http://ibm.biz/learning-centers
Views: 2212 IBM Developer
Flink Forward 2015: Albert Bifet – SAMOA Mining Big Data Streams with Apache Flink
Flink Forward Conference on Apache Flink, October 12 & 13 at Kulturbrauerei Berlin
Views: 498 Flink Forward
Analyzing Big Data in less time with Google BigQuery
Most experienced data analysts and programmers already have the skills to get started. BigQuery is fully managed and lets you search through terabytes of data in seconds. It’s also cost effective: you can store gigabytes, terabytes, or even petabytes of data with no upfront payment, no administrative costs, and no licensing fees. In this webinar, we will: - Build several highly-effective analytics solutions with Google BigQuery - Provide a clear road map of BigQuery capabilities - Explain how to quickly find answers and examples online - Share how to best evaluate BigQuery for your use cases - Answer your questions about BigQuery
Views: 56286 Google Cloud Platform
High Dimensional Data
Match the applications to the theorems: (i) Find the variance of traffic volumes in a large network presented as streaming data. (ii) Estimate failure probabilities in a complex systems with many parts. (iii) Group customers into clusters based on what they bought. (a) Projecting high dimensional space to a random low dimensional space scales each vector's length by (roughly) the same factor. (b) A random walk in a high dimensional convex set converges rather fast. (c) Given data points, we can find their best-fit subspace fast. While the theorems are precise, the talk will deal with applications at a high level. Other theorems/applications may be discussed.
Views: 2098 Microsoft Research
Cryptocurrency: Virtual money, real power and the fight for a small town's future
In the world of cryptocurrency, money is power, sparking a gold rush wherever energy is cheap and abundant. A booming cryptocurrency mining industry is disrupting a small town in Washington state. Subscribe to the CBS News Channel HERE: http://youtube.com/cbsnews Watch CBSN live HERE: http://cbsn.ws/1PlLpZ7 Follow CBS News on Instagram HERE: https://www.instagram.com/cbsnews/ Like CBS News on Facebook HERE: http://facebook.com/cbsnews Follow CBS News on Twitter HERE: http://twitter.com/cbsnews Get the latest news and best in original reporting from CBS News delivered to your inbox. Subscribe to newsletters HERE: http://cbsn.ws/1RqHw7T Get your news on the go! Download CBS News mobile apps HERE: http://cbsn.ws/1Xb1WC8 Get new episodes of shows you love across devices the next day, stream CBSN and local news live, and watch full seasons of CBS fan favorites like Star Trek Discovery anytime, anywhere with CBS All Access. Try it free! http://bit.ly/1OQA29B --- CBSN is the first digital streaming news network that will allow Internet-connected consumers to watch live, anchored news coverage on their connected TV and other devices. At launch, the network is available 24/7 and makes all of the resources of CBS News available directly on digital platforms with live, anchored coverage 15 hours each weekday. CBSN. Always On.
Views: 343466 CBS News

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