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Lecture 36 — Mining Data Streams | Mining of Massive Datasets | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
How to Integrate Natural Language Processing and Elasticsearch for Better Analytics - Tech Talks
 
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To learn more, visit: https://aws.amazon.com/comprehend/ There’s a proliferation of unstructured data. Companies collect massive amounts of newsfeed, emails, social media, and other text-based information to get to know their customers better or to comply with regulations. However, most of this data is unused and untouched. Natural language processing (NLP) holds the key to unlocking business value within these huge datasets, by turning free text into data that can be analyzed and acted upon. Join this tech talk and learn how you can get started mining text data effectively and extracting the rich insights it can bring. In this tech talk, you’ll learn how to process, analyze and visualize data by pairing Amazon Comprehend with Amazon Elasticsearch. Learn how you can boost search results, create rich filtering, and develop social media analytics dashboard. Learning Objectives: - Get an introduction to Amazon Comprehend, a natural language processing service from AWS - Understand how to use a natural language processing with Elasticsearch - Learn how to build customer feedback and social media analytics dashboards and how to boost rankings of the search results and build rich filtering
Lecture 43 — Collaborative Filtering | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Analyzing Big Data in less time with Google BigQuery
 
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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: 59336 Google Cloud Platform
Data Science at Netflix with Amazon EMR (BDT306) | AWS re:Invent 2013
 
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A few years ago, Netflix had a fairly classic business intelligence tech stack. Now, things have changed. Netflix is a heavy user of AWS for much of its ongoing operations, and Data Science & Engineering (DSE) is no exception. In this talk, we dive into the Netflix DSE architecture: what and why. Key topics include their use of Big Data technologies (Cassandra, Hadoop, Pig + Python, and Hive); their Amazon S3 central data hub; their multiple persistent Amazon EMR clusters; how they benefit from AWS elasticity; their data science-as-a-service approach, how they made a hybrid AWS/data center setup work well, their open-source Hadoop-related software, and more.
Views: 3077 Amazon Web Services
Data Science - AntenaDev #02
 
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Data Science - AntenaDev #02 Publicado em 04/12/2017 Antenados e Antenadas! Bem vindos ao segundo episódio da trilha principal do Canal AntenaDev!!!! Apresentamos neste episódio uma lendária entrevista com três renomados Cientistas de Dados. Conheça todas os detalhes dessa área que vem revolucionando a tecnologia e a vida dos seres humanos. Prepare-se!!! Sua vida já está sendo transformada pela Ciência de Dados (ou Data Science, em inglês). Descubra o que é Data Science, seus principais conceitos, suas aplicações, as oportunidades que estão surgindo, como o mundo está sendo transformado por ela e como se tornar um Cientistas dos Dados. Participantes: Hosts: - Prof. José Maria Monteiro, um host que tenta ser politicamente correto. Mas, só tenta mesmo. - Prof. Marcelo Gonçalves, o DevMan que ficou sem energia elétrica... Entrevistados: - Aderson Olivera - http://aderson.com - Igo Brilhante - https://www.wanderpaths.com/ - Tales Matos - www.arida.ufc.b - Nauber Gois - innovanti.io/workshopdatascience Links do episódio: - Workshop de Data Science: innovanti.io/workshopdatascience - Kaggle: www kaggle.com - Coursera: www.coursera.com - Certificação de DataScience (CAP): https://www.certifiedanalytics.org/ - Wanderpaths: https://www.wanderpaths.com - Wanderpaths no GooglePlay: https://play.google.com/store/apps/details?id=com.wanderpaths.app - Wanderpaths no Apple Store: https://itunes.apple.com/br/app/wanderpaths/id1147166365?mt=8 - Wanderpaths no Instagram: @wanderpathsapp - Wanderpaths no Facebook: @wanderpaths - Canal do Nauber: https://www.youtube.com/channel/UCZctB98Gn7af-3OpGC-7-Sg - Como encontrar o Igo Brilhante: @igobrilhante - Visualização de Dados: https://uber.github.io/deck.gl/#/ https://d3js.org/ - Livros sobre Data Science: https://www.amazon.com/Data-Mining-Concepts-Techniques-Management/dp/0123814790 https://www.amazon.com/Mining-Social-Web-Facebook-LinkedIn/dp/1449367615/ref=sr_1_1?s=home-garden&ie=UTF8&qid=1512041753&sr=8-1&keywords=mining+web+python https://www.amazon.com/Mining-Massive-Datasets-Jure-Leskovec/dp/1107077230/ref=sr_1_1?ie=UTF8&qid=1512041778&sr=8-1&keywords=mining+of+massive+datasets - Datasets Interessantes: https://elitedatascience.com/datasets - Plataformas https://databricks.com/ https://databricks.com/blog/2016/01/25/deep-learning-with-apache-spark-and-tensorflow.html https://hortonworks.com/ https://www.cloudera.com/ - Diary of a data scientist at Booking.com (Sou fã da Booking) https://towardsdatascience.com/diary-of-a-data-scientist-at-booking-com-924734c71417 Produção e conteúdo: • AntenaDev Inovação Edição e sonorização:  • AntenaDev Inovação Redes Sociais: • Blog: www.antenadev.com.br • Facebook: www.facebook.com/AntenaDev • Instagram: @AntenaDev – http://www.instagram.com/AntenaDev • Twitter: @AntenaDev – www.twitter.com/AntenaDev Agradecimentos: • https://www.freesound.org/ • https://www.pond5.com • http://br.freepik.com/ • https://pixabay.com/ • https://www.pexels.com/ • http://audiomicro.com/royalty-free-music Categoria • Educação Licença • Licença padrão do YouTube
Views: 196 AntenaDev
AWS re:Invent 2016: Data Polygamy: Relationships among Urban Spatio-Temporal Datasets (WWPS401)
 
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In this session, learn how Data Polygamy, a scalable topology-based framework, can enable users to query for statistically significant relationships between spatio-temporal datasets. With the increasing ability to collect data from urban environments and a push toward openness by governments, we can analyze numerous spatio-temporal datasets covering diverse aspects of a city. Urban data captures the behavior of the city’s citizens, existing infrastructure (physical and policies), and environment over space and time. Discovering relationships between these datasets can produce new insights by enabling domain experts to not only test but also generate hypotheses. However, discovery is difficult. A relationship between two datasets can occur only at locations or time periods that behave differently compared to the regions’ neighborhood. The size and number of datasets and diverse spatial and temporal scales at which the data is available presents computational challenges. Finally, of several thousand possible relationships, only a small fraction is actually informative. We have implemented the framework on Amazon EMR and show through an experimental evaluation using over 300 spatial-temporal urban datasets how our approach is scalable and effective at identifying significant relationships. Find details about the work at http://dl.acm.org/citation.cfm?id=2915245. The code and experiments are available at https://github.com/ViDA-NYU/data-polygamy.
Views: 412 Amazon Web Services
frequent itemset mining using map reduce framework
 
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This project is for identifying the Frequent Itemset mining of amazon datasets using mapreduce framework
High Performance and Scalable Geospatial Analytics on Cloud with Open Source
 
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g the rise and innovation of “big data,” the geospatial analytics landscape has grown and evolved. We are beyond just analyzing static maps. Geospatial data is streaming from devices, sensors, infrastructure systems, or social media, and our applications and use cases must dynamically scale to meet the increased demands. Cloud can provide cost-effective storage and that ephemeral resource-burst needed for fast processing and low latency, all to monetize the immediate value of fresh geospatial data. Geospatial analytics require optimized spatial data types and algorithms to distill data to knowledge. Such processing, especially with strict latency requirements, has always been a challenge. We propose an open source big data stack for geospatial analytics on Cloud based on Apache NiFi, Apache Spark and LocationTech GeoMesa. GeoMesa is a geospatial framework deployed in a modern big data platform that provides a scalable and low latency solution for indexing volumes of historical data and generating live views and streaming geospatial analytics.
Views: 125 DataWorks Summit
Apache Spark Tutorial for Beginners Part 3 - Resilient Distributed Dataset - Frank Kane
 
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Explore the full course on Udemy (special discount included in the link): https://www.udemy.com/the-ultimate-hands-on-hadoop-tame-your-big-data/?couponCode=HADOOPUYT Apache Spark is arguably the hottest technology in the field of big data right now. It allows you to process and extract meaning from massive data sets on a cluster, whether it is a Hadoop cluster you administer or a cloud-based deployment. In this series of 8 videos, we will walk through installing Spark on a Hortonworks sandbox running right on your own PC, and we will talk about how Spark works and its architecture. We will then dive hands-on into the origins of Spark by working directly with RDDs Resilient Distributed Datasets and then move on to the modern Spark 2.0 way of programming with Datasets. You will get hands-on practice writing a few simple Spark applications using the Python programming language, and then we will actually build a movie recommendation engine using real movie ratings data, and Sparks machine learning library MLLib. We will end with an exercise you can try yourself for practice, along with my solution to it. In this video, we will focus on getting VirtualBox, a Hortonworks Data Platform (HDP) sandbox, and the MovieLens data set installed for use in the rest of the series. Your instructor is Frank Kane, who spent nine years at Amazon.com and IMDb.com as a senior engineer and senior manager, wrangling their massive data sets.
Views: 1821 Udemy Tech
Unboxing Canada's BIGGEST Supercomputer!
 
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27,000 Intel Xeon Cores, 190 TERABYTES of RAM, and 64 PETABYTES of storage lie within this crazy datacenter called Cedar. Let's check it out!! Thanks to SFU and Compute Canada for allowing us to visit this INCREDIBLE facility. Learn more about Cedar at http://geni.us/5N3l Squarespace sponsor link: Visit https://www.squarespace.com/LTT and use offer code LTT for 10% off Savage Jerky sponsor link: Use offer code LTT to save 10% on Savage Jerky at http://geni.us/savagejerky Buy Nvidia GPUs Amazon: http://geni.us/lMYwXi Newegg: http://geni.us/xx2bsA Discuss on the forum: https://linustechtips.com/main/topic/801815-unboxing-canadas-biggest-supercomputer/ Our Affiliates, Referral Programs, and Sponsors: https://linustechtips.com/main/topic/... Linus Tech Tips merchandise at http://www.designbyhumans.com/shop/LinusTechTips Linus Tech Tips posters at http://crowdmade.com/linustechtips Production gear: http://geni.us/cvOS https://twitter.com/linustech http://www.facebook.com/LinusTech Intro Screen Music Credit: Title: Laszlo - Supernova Video Link: https://www.youtube.com/watch?v=PKfxm... iTunes Download Link: https://itunes.apple.com/us/album/sup... Artist Link: https://soundcloud.com/laszlomusic Outro Screen Music Credit: Approaching Nirvana - Sugar High http://www.youtube.com/approachingnir... Sound effects provided by http://www.freesfx.co.uk/sfx/
Views: 1829017 Linus Tech Tips
Making Movie Recommendations with Item-Based Collaborative Filtering
 
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Complete course: https://www.udemy.com/building-recommender-systems-with-machine-learning-and-ai/?couponCode=RECSYS15 Learn how to design, build, and scale recommender systems from Frank Kane, who led teams building them at Amazon.com for 9 years. In this excerpt from "Building Recommender Systems with Machine Learning and AI," Frank covers the item-based collaborative filtering technique used by Amazon to produce product recommendations at massive scale. Well, at least the parts he can legally tell you about! The idea is simple, but it works remarkably well: find the items a given individual has expressed interest in through their purchases, ratings, or other activity - and recommend the items most similar to those items. Measuring item similarity for every possible pair of items is the key issue. Metrics such as cosine similarity, pearson correlation, or Jaccard similarity may be used.
Views: 604 Sundog Education
Advanced BigQuery features: keys to the cloud datawarehouse of the future (Google Cloud Next '17)
 
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Google BigQuery has a lot of features you may not know about: undelete, partitions, table searches and even time-travel. In this video, Dan McClary and Jordan Tigani introduce these advanced concepts and show you how to supercharge your work with them. Missed the conference? Watch all the talks here: https://goo.gl/c1Vs3h Watch more talks about Big Data & Machine Learning here: https://goo.gl/OcqI9k
Views: 12737 Google Cloud Platform
AWS re:Invent 2016: Fanatics: Deploying Scalable, Self-Service Business Intelligence on AWS (BDA207)
 
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Data is growing at a quantum scale and one of challenges you face is to enable your users to analyze all this data, extract timely insights from it, and visualize it. In this session, you learn about business intelligence solutions available on AWS. We discuss best practices for deploying a scalable and self-serve BI platform capable of churning through large datasets. Fanatics, the nation’s largest online seller of licensed sports apparel, talks about their experience building a globally distributed BI platform on AWS, that delivers massive volumes of reports, dashboards, and charts on a daily basis to an ever growing user base. Fanatics shares the architecture of their data platform, built using Amazon Redshift, Amazon S3, and open source frameworks like Presto and Spark. They talk in detail about their BI platform including Tableau, Microstrategy, and other tools on AWS to make it easy for their analysts to perform ad-hoc analysis and get real-time updates, alerts, and visualizations. You also learn about the experimentation-based approach that Fanatics adopted to fully engage their business intelligence community and make optimal use of their BI platform resources on AWS.
Views: 1035 Amazon Web Services
Introduction to recommendation systems
 
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The video is based on the online resources and the book "Mining of Massive Datasets"
Views: 40 Yayati Gupta
Lecture 17.1 — Large Scale Machine Learning | Learning With Large Datasets — [ Andrew Ng ]
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Visual Analysis of Data
 
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CE502 Fall 2017
Views: 85 Dragos Andrei
Neuromation - the knowledge mining era
 
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Cryptonomos will hold a token sale for Neuromation that is creating a distributed platform of synthetic data ecosystem. The enormous computing capacity that will become available on the platform will be game-changing for wide AI adoption by the Enterprise. Neuromation is creating a platform to allow users to create dataset generators, generate massive datasets, train deep learning models. Users will also be able to trade datasets and models in the platform marketplace. Neuromation engages crypto-currency miners in computationally intensive tasks of data generation and model training. By performing these tasks they will be mining Neuromation Tokens. Tokens for the Neuromation platform are already available for purchase on pre-sale via Cryptonomos. Buy tokens: https://neuromation.cryptonomos.com Telegram RU: https://t.me/icocryptonomosrus Telegram EN: https://t.me/Cryptonomos_ICOs
Views: 3180 Cryptonomos Platform
Heron: Real-time Stream Data Processing at Twitter
 
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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: 8184 @Scale
Taming Big Data with Apache Spark and Python - Hands On!
 
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get this course from here: http://cuon.io/R1x6p New! Updated for Spark 2.0.0 “Big data" analysis is a hot and highly valuable skill – and this course will teach you the hottest technology in big data: Apache Spark. Employers including Amazon, EBay, NASA JPL, and Yahoo all use Spark to quickly extract meaning from massive data sets across a fault-tolerant Hadoop cluster. You'll learn those same techniques, using your own Windows system right at home. It's easier than you might think. Learn and master the art of framing data analysis problems as Spark problems through over 15 hands-on examples, and then scale them up to run on cloud computing services in this course. You'll be learning from an ex-engineer and senior manager from Amazon and IMDb. Learn the concepts of Spark's Resilient Distributed Datastores Develop and run Spark jobs quickly using Python Translate complex analysis problems into iterative or multi-stage Spark scripts Scale up to larger data sets using Amazon's Elastic MapReduce service Understand how Hadoop YARN distributes Spark across computing clusters Learn about other Spark technologies, like Spark SQL, Spark Streaming, and GraphX By the end of this course, you'll be running code that analyzes gigabytes worth of information – in the cloud – in a matter of minutes. This course uses the familiar Python programming language; if you'd rather use Scala to get the best performance out of Spark, see my "Apache Spark with Scala - Hands On with Big Data" course instead. We'll have some fun along the way. You'll get warmed up with some simple examples of using Spark to analyze movie ratings data and text in a book. Once you've got the basics under your belt, we'll move to some more complex and interesting tasks. We'll use a million movie ratings to find movies that are similar to each other, and you might even discover some new movies you might like in the process! We'll analyze a social graph of superheroes, and learn who the most “popular" superhero is – and develop a system to find “degrees of separation" between superheroes. Are all Marvel superheroes within a few degrees of being connected to The Incredible Hulk? You'll find the answer. This course is very hands-on; you'll spend most of your time following along with the instructor as we write, analyze, and run real code together – both on your own system, and in the cloud using Amazon's Elastic MapReduce service. 5 hours of video content is included, with over 15 real examples of increasing complexity you can build, run and study yourself. Move through them at your own pace, on your own schedule. The course wraps up with an overview of other Spark-based technologies, including Spark SQL, Spark Streaming, and GraphX. Enjoy the course! Who is the target audience? People with some software development background who want to learn the hottest technology in big data analysis will want to check this out. This course focuses on Spark from a software development standpoint; we introduce some machine learning and data mining concepts along the way, but that's not the focus. If you want to learn how to use Spark to carve up huge datasets and extract meaning from them, then this course is for you. If you've never written a computer program or a script before, this course isn't for you - yet. I suggest starting with a Python course first, if programming is new to you. If your software development job involves, or will involve, processing large amounts of data, you need to know about Spark. If you're training for a new career in data science or big data, Spark is an important part of it.
Mining Competitors from Large Unstructured Datasets
 
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Mining Competitors from Large Unstructured Datasets To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org In any competitive business, success is based on the ability to make an item more appealing to customers than the competition. A number of questions arise in the context of this task: how do we formalize and quantify the competitiveness between two items? Who are the main competitors of a given item? What are the features of an item that most affect its competitiveness? Despite the impact and relevance of this problem to many domains, only a limited amount of work has been devoted toward an effective solution. In this paper, we present a formal definition of the competitiveness between two items, based on the market segments that they can both cover. Our evaluation of competitiveness utilizes customer reviews, an abundant source of information that is available in a wide range of domains. We present efficient methods for evaluating competitiveness in large review datasets and address the natural problem of finding the top-k competitors of a given item. Finally, we evaluate the quality of our results and the scalability of our approach using multiple datasets from different domains.
Views: 1328 jpinfotechprojects
Case Study: How a Large Brewery Uses Machine Learning for Preventive Maintenance (Cloud Next '18)
 
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Learn how machine learning is used to optimize the beer manufacturing process. This use case has a direct impact on the production line and identifying downtime of equipment, and huge impact on cost, time, and quality of beer being produced. The role of machine learning is to improve the manufacturing process and quality while driving higher ROI through an undisruptive production process. Listen to experts on how Deep Learning was used with classifications of good parts vs. bad parts using TensorFlow. The model will be deployed to Google Cloud Machine Learning Engine where it will make predictions of the new data that is fed every day with an interactive dashboard using Data Studio. Event schedule → http://g.co/next18 Watch more Machine Learning & AI sessions here → http://bit.ly/2zGKfcg Next ‘18 All Sessions playlist → http://bit.ly/Allsessions Subscribe to the Google Cloud channel! → http://bit.ly/NextSub
Views: 1214 Google Cloud Platform
Building Your Data Lake on AWS
 
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Learn more about Big Data on AWS at - https://amzn.to/2MOMLPA. A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it's readily available to be categorized, processed, analyzed and consumed by diverse groups within an organization.
Views: 2186 Amazon Web Services
Apache Spark Tutorial for Beginners Part 5 - Spark SQL - Frank Kane
 
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Explore the full course on Udemy (special discount included in the link): https://www.udemy.com/the-ultimate-hands-on-hadoop-tame-your-big-data/?couponCode=HADOOPUYT Apache Spark is arguably the hottest technology in the field of big data right now. It allows you to process and extract meaning from massive data sets on a cluster, whether it is a Hadoop cluster you administer or a cloud-based deployment. In this series of 8 videos, we will walk through installing Spark on a Hortonworks sandbox running right on your own PC, and we will talk about how Spark works and its architecture. We will then dive hands-on into the origins of Spark by working directly with RDDs Resilient Distributed Datasets and then move on to the modern Spark 2.0 way of programming with Datasets. You will get hands-on practice writing a few simple Spark applications using the Python programming language, and then we will actually build a movie recommendation engine using real movie ratings data, and Sparks machine learning library MLLib. We will end with an exercise you can try yourself for practice, along with my solution to it. In this video, we will focus on getting VirtualBox, a Hortonworks Data Platform (HDP) sandbox, and the MovieLens data set installed for use in the rest of the series. Your instructor is Frank Kane, who spent nine years at Amazon.com and IMDb.com as a senior engineer and senior manager, wrangling their massive data sets.
Views: 862 Udemy Tech
Big Data Clustering: A MapReduce Implementation of Hierarchical Affinity Propagation
 
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This project allows users to effectively perform a hierarchical clustering algorithm over extremely large datasets. The research team developed a distributed software system which reads in data from multiple input sources using a common interface, clusters the data according to a user-defined similarity metric, and represents the extracted clusters to the user in an interactive, web-based visualization. In order to deal with large "Big Data" datasets, the team derived and implemented a distributed version of the Hierarchical Affinity Propagation (HAP) clustering algorithm using the MapReduce framework. This parallelization allows the algorithm to run in best-case linear time on any cardinality dataset. It also allows execution of the software within a scalable cloud-computing framework such as Amazon's Elastic Compute Cloud (EC2).
Views: 4447 FIT ICELAB
Datamining Algorithm with Hadoop Cluster  on AWS  Part2
 
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Running data mining algorithm for finding frequent items from large data on AWS Cloud
Views: 62 Sanket Thakare
GOTO 2017 • Fast Data Architectures for Streaming Applications • Dean Wampler
 
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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: 5482 GOTO Conferences
RecSys 2014 Industry Session I: Mainstream
 
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Unfortunately, Youtube live streaming seems to have run into some glitches between 2:00 pm and 3:30 pm on Wednesday. This affected the end of the keynote stream and the first three speakers of Industry Session I: Mainstream.
Views: 323 ACM RecSys
Applying Geospatial Analytics at a Massive Scale using Kafka, Spark and Elasticsearch on DC/OS
 
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Applying Geospatial Analytics at a Massive Scale using Kafka, Spark and Elasticsearch on DC/OS - Adam Mollenkopf, Esri This session will explore how DC/OS and Mesos are being used at Esri to establish a foundational operating environment to enable the consumption of high velocity IoT data using Apache Kafka, streaming analytics using Apache Spark, high-volume storage and querying of spatiotemporal data using Elasticsearch, and recurring batch analytics using Apache Spark & Metronome. Additionally, Esri will share their experience in making their application for DC/OS portable so that it can easily be deployed amongst public cloud providers (Microsoft Azure, Amazon EC2), private cloud providers and on-premise environments. Demonstrations will be performed throughout the presentation to cement these concepts for the attendees. About Adam Mollenkopf Esri Real-Time & Big Data GIS Capability Lead Redlands, CA Twitter Tweet Websiteesri.com Adam Mollenkopf is responsible for the strategic direction Esri takes towards enabling real-time and big data capabilities in the ArcGIS platform. This includes having the ability to ingest real-time data streams from a wide variety of sources, performing continuous and recurring spatiotemporal analytics on data as it is received & disseminating analytic results to communities of interest. He leads a team of experienced individuals in the area of stream processing and big data analytics.
Views: 1307 The Linux Foundation
Mining Competitors from Large Unstructured Datasets  IEEE 2017 JAVA Project
 
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Project Developed by igeeks technologies,bangalore,www.makefinalyearproject.com,Cal Mr.Nandu Project Director-9590544567,Email :[email protected]
Data Stream Processing: Concepts and Implementations by Matthias Niehoff
 
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With data stream processing there are plenty of options. Matthias gives an overview on various concepts used in data stream processing. Most of them are used for solving problems in the field of time, focussing on processing time compared to event time. The techniques shown include the Dataflow API as it was introduced by Google and the concepts of stream and table duality. But I will also come up with other problems like data lookup and deployment of streaming applications and various strategies on solving these problems. The summary contains a brief outline on the implementation status of those strategies in the popular streaming frameworks Apache Spark Streaming, Apache Flink and Kafka Streams. Meet The Experts: Data-driven Day provides an overview of the challenges, possible solutions and technologies for data-driven applications and use cases. This talk is one of the series at codecentric's Data Driven Day. • Complete Playlist: http://bit.ly/mte-datadrivenday
Views: 459 codecentric AG
The Stanford Data Stream Management System
 
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This talk will describe our development of the Stanford Stream Data Manager (STREAM), a system for executing complex continuous queries over multiple continuous data streams. The STREAM system supports a declarative query language, it copes with high data rates and query workloads by providing approximate answers when resources are limited, and it adapts its execution strategies automatically as conditions change. We will provide an overview of the system and our research results, and show a live demo assuming time and logistics permit. Joint work with the entire STREAM group at Stanford: http://www-db.stanford.edu/stream
Views: 738 Microsoft Research
Streaming Data
 
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Here is Brightlight Sr. Principal Consultant Frank Blau discussing the new frontier of Streaming Data and how it relates to Business Intelligence and Analytics. This video provides a basic introduction to the concepts and evolution of streaming data as the enabling technology for the future of BI.
Views: 165 BrightlightConsult
Lecture 60 — The k Means Algorithm | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Venkata Pingali – Increasing Trust and Efficiency of Data Science using dataset versioning
 
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As data science grows and matures as a domain, harder questions are being asked by decision makers about trust and efficiency of data science process. Some of them include: Lineage/Auditability: Where did the numbers come from? Reproducibility/Replicability: Is this an accident? Does it hold now? Efficiency/Automation: Can you do it faster, cheaper, better? Significant amount of data scientists’ time goes towards generating, shaping, and using datasets. It is laborious and error prone. In this talk, we introduce an open source tool, dgit - git wrapper to manage dataset versions, and discuss why dgit was developed, and how we can redo the data science process using dgit.
Views: 228 HasGeek TV
Machine Learning #73 - Support Vector Machines #4 - Soft Margin
 
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In diesem Tutorial behandeln wir Soft Margins. ❤❤❤ Früherer Zugang zu Tutorials, Abstimmungen, Live-Events und Downloads ❤❤❤ ❤❤❤ https://www.patreon.com/user?u=5322110 ❤❤❤ ❤❤❤ Keinen Bock auf Patreon? ❤❤❤ ❤❤❤ https://www.paypal.me/TheMorpheus ❤❤❤ 🌍 Website 🌍 https://the-morpheus.de ¯\_(ツ)_/¯ Tritt der Community bei ¯\_(ツ)_/¯ ** https://discord.gg/BnYZ8XS ** ** https://www.reddit.com/r/TheMorpheusTuts/ ** ( ͡° ͜ʖ ͡°) Mehr News? Mehr Code? ℱ https://www.facebook.com/themorpheustutorials 🐦 https://twitter.com/TheMorpheusTuts 🐙 https://github.com/TheMorpheus407/Tutorials Du bestellst bei Amazon? Bestell über mich, kostet dich null und du hilfst mir »-(¯`·.·´¯)-» http://amzn.to/2slBSgH Videowünsche? 🎁 https://docs.google.com/spreadsheets/d/1YPv8fFJOMRyyhUggK8phrx01OoYXZEovwDLdU4D4nkk/edit#gid=0 Fragen? Feedback? Schreib mir! ✉ https://www.patreon.com/user?u=5322110 ✉ https://www.facebook.com/themorpheustutorials ✉ https://discord.gg/BnYZ8XS ✉ [email protected] oder schreib einfach ein Kommentar :)
Fast Data: Selecting The Right Streaming Technologies For Data Sets That Never End
 
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With Dr. Dean Wampler, Office of the CTO and Fast Data Architect at Lightbend, Inc. Why have stream-oriented data systems become so popular, when batch-oriented systems have served big data needs for many years? Batch-mode processing isn’t going away, but exclusive use of these systems is now a competitive disadvantage. You’ll learn that, while fast data architectures are much harder to build, they represent the state of the art for dealing with mountains of data that require immediate attention. In this webinar, Lightbend’s Big Data Architect, Dr. Dean Wampler, examines the rise of streaming systems for handling time-sensitive problems. We’ll explore the characteristics of fast data architectures, and the open source tools for implementing them. We’ll also take a brief look at Lightbend’s upcoming Fast Data Platform (FDP), a comprehensive solution of OSS and commercial technologies. FDP includes installation, integration, and monitoring tools tuned for various deployment scenarios, plus sample applications to help you sort out which tools to use for which purposes. We’ll cover: Learn step-by-step how a basic fast data architecture works Understand why event logs are the core abstraction for streaming architectures, while message queues are the core integration tool Use methods for analyzing infinite data sets, where you don’t have all the data and never will Take a tour of open source streaming engines, and discover which ones work best for different use cases Get recommendations for making real-world streaming system responsive, resilient, elastic, and message driven Explore an example streaming application for the IoT: telemetry ingestion and anomaly detection for home automation systems
Views: 1024 Lightbend
Correlation with BigQuery
 
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Michael Manoochehri and Felipe Hoffa give us a look at the new and powerful correlation functions now available in Big Query. Find the full code for this demo at http://nbviewer.ipython.org/6459195.
Views: 7161 Google Developers
Apache Spark with Scala :  Learn Spark from a Big Data Guru | BEST SELLER
 
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This course covers all the fundamentals about Apache Spark with Scala and teaches you everything you need to know about developing Apache Spark applications with Scala Spark. At the end of this course, you will gain in-depth knowledge about Apache Spark Scala and general big data analysis and manipulations skills to help your company to adapt Apache Scala Spark for building big data processing pipeline and data analytics applications. This course covers 10+ hands-on big data examples involving Apache Spark. You will learn valuable knowledge about how to frame data analysis problems as Scala Spark problems. Together we will learn examples such as aggregating NASA Apache web logs from different sources; we will explore the price trend by looking at the real estate data in California; we will write Scala Spark applications to find out the median salary of developers in different countries through the Stack Overflow survey data; we will develop a system to analyze how maker spaces are distributed across different regions in the United Kingdom. And much much more. What will you learn from this lecture: In particularly, you will learn: An overview of the architecture of Apache Spark. Develop Apache Spark 2.0 applications with Scala using RDD transformations and actions and Spark SQL. Work with Apache Spark's primary abstraction, resilient distributed datasets(RDDs) to process and analyze large data sets. Deep dive into advanced techniques to optimize and tune Apache Spark jobs by partitioning, caching and persisting RDDs. Scale up Apache Spark applications on a Hadoop YARN cluster through Amazon's Elastic MapReduce service. Analyze structured and semi-structured data using Datasets and DataFrames, and develop a thorough understanding of Apache Spark SQL. Share information across different nodes on an Apache Spark cluster by broadcast variables and accumulators. Best practices of working with Apache Spark Scala in the field. Big data ecosystem overview. Why shall we learn Apache Spark: Apache Spark gives us unlimited ability to build cutting-edge applications. It is also one of the most compelling technologies of the last decade in terms of its disruption to the big data world. Apache Scala Spark provides in-memory cluster computing which greatly boosts the speed of iterative algorithms and interactive data mining tasks. Apache Spark is the next-generation processing engine for big data. Tons of companies are adapting Apache Spark to extract meaning from massive data sets, today you have access to that same big data technology right on your desktop. Apache Spark is becoming a must tool for big data engineers and data scientists. What programing language is this course taught in? This course is taught in Scala. Scala is the next generation programming language for functional programing that is growing in popularity and it is one of the most widely used languages in the industry to write Apache Spark programs. Let's learn how to write Apache Spark programs with Scala to model big data problem today! 30-day Money-back Guarantee! You will get 30-day money-back guarantee from Udemy for this course. If not satisfied with Apache Spark course, simply ask for a refund within 30 days. You will get a full refund. No questions whatsoever asked. Are you ready to take your big data analysis skills and career to the next level, take this course now! You will go from zero to Apache Spark hero in 4 hours. Course Link : http://bit.ly/2DKjsZD Google Searching Text: spark with scala tutorial,scala for spark pdf,apache spark tutorial,spark with scala book,spark scala example,spark scala wiki,spark tutorials with scala the beginner's guide pdf,what is spark,
Architectures for Big Data Analytics and Data Mining Platforms-  Panel Discussion, 20120827
 
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http://www.sfbayacm.org/event/architectures-big-data-analytics-and-data-mining-platforms How are cutting edge, stellar companies advancing the state of the art in big data mining? We will take a look at some outstanding platforms for analytics and data mining on "Big Data." Our panelists helped... Netflix to analyze massive data on customer's movie viewing in order to make recommendations, gauge engagement, and to improve retention Quantcast to measure audiences for digital media across the web and help their customers reach desired audiences and Think Big Analytics to provide leading clients in technology, advertising, financial services and retail with professional services, engineering, and advanced analytics on big data. Our panelists will draw on their experiences to give overviews of multiple architectures. They will tell us about what they do and how they use machine learning on massive data sets. Speaker Bio Panalists Ron Bodkin, Founder & CEO, ThinkBig Analytics Jim Kelly, VP, R&D, Quantcast Mohammad Sabah, Data Science & Analytics Manager, Facebook, ex Netflix Moderator Paul O'Rorke, Sony Mobile
Views: 1159 San Francisco Bay ACM
Live Streaming Architecture
 
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Episode 6 - Live Streaming Architecture 3 Live Streaming Sections 1. Publisher 2. Server 3. Viewer
Views: 12126 livestreamninja
[OREILLY] Social Web Mining - Github - Welcome To The Course
 
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The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining
Views: 89 Freemium Courses
xStream: Outlier Detection in Feature-Evolving Data Streams
 
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Authors: Emaad Manzoor (CMU), Hemank Lamba (CMU), Leman Akoglu (CMU) Abstract: This work addresses the outlier detection problem for feature-evolving streams, which has not been studied before. In this setting both (1) data points may evolve, with feature values changing, as well as (2) feature space may evolve, with newly-emerging features over time. This is notably different from row-streams, where points with fixed features arrive one at a time. We propose a density-based ensemble outlier detector, called xStream, for this more extreme streaming setting which has the following key properties: (1) it is a constant-space and constant-time (per incoming update) algorithm, (2) it measures outlierness at multiple scales or granularities, it can handle (3i) high-dimensionality through distance-preserving projections, and (3ii) non-stationarity via O(1)-time model updates as the stream progresses. In addition, xStream can address the outlier detection problem for the (less general) disk-resident static as well as row-streaming settings. We evaluate xStream rigorously on numerous real-life datasets in all three settings: static, row-stream, and feature-evolving stream. Experiments under static and row-streaming scenarios show that xStream is as competitive as state-of-the-art detectors and particularly effective in high-dimensions with noise. We also demonstrate that our solution is fast and accurate with modest space overhead for evolving streams, on which there exists no competition. More on http://www.kdd.org/kdd2018/
Views: 235 KDD2018 video
BigDataX: Bloom filtering
 
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Big Data Fundamentals is part of the Big Data MicroMasters program offered by The University of Adelaide and edX. Learn how big data is driving organisational change and essential analytical tools and techniques including data mining and PageRank algorithms. Enrol now! http://bit.ly/2rg1TuF

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