Search results “Real-time data stream mining”
What is DATA STREAM MINING? What does V mean? DATA STREAM MINING meaning - DATA STREAM MINING definition - DATA STREAM MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Data Stream Mining is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities. In many data stream mining applications, the goal is to predict the class or value of new instances in the data stream given some knowledge about the class membership or values of previous instances in the data stream. Machine learning techniques can be used to learn this prediction task from labeled examples in an automated fashion. Often, concepts from the field of incremental learning are applied to cope with structural changes, on-line learning and real-time demands. In many applications, especially operating within non-stationary environments, the distribution underlying the instances or the rules underlying their labeling may change over time, i.e. the goal of the prediction, the class to be predicted or the target value to be predicted, may change over time. This problem is referred to as concept drift. Examples of data streams include computer network traffic, phone conversations, ATM transactions, web searches, and sensor data. Data stream mining can be considered a subfield of data mining, machine learning, and knowledge discovery.
Views: 595 The Audiopedia
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: 1535 KDD2016 video
#bbuzz: Mikio Braun "Beyond scaling: real-time event analysis with stream mining"
Mikio Braun http://berlinbuzzwords.de/sessions/beyond-scaling-real-time-event-analysis-stream-mining High volume event streams are an important case of big data applications. Dealing with millions of events per day is a huge challenge, in particular for batch-oriented scalability approaches like map-reduce. In this talk, I will discuss an alternative approach based on stream mining algorithms, which have been developed in the mid 2000s in the data mining community, but have to yet make it into the mainstream. Instead of relying on scalability and parallelization alone, stream mining allows you to trade accuracy for resource usage, resulting in robust algorithms with performance guarantees. I will focus on two classes of algorithms, counter based algorithms for identifying so-called heavy hitters, and sketch based algorithms to estimate activities of different event types. While these algorithms seem pretty basic at first, in the last part of the talk, I'll discuss how these algorithms can be used for more advanced analytics, for example, trending, probabilistic modelling and outlier detection, clustering, TF-IDF and related relevancy reweighting measures, and classification. About the speaker: Mikio L. Braun is co-founder and chief data scientist of TWIMPACT, and PostDoc for machine learning at the TU Berlin. His interests are real-time data analysis, in particular for social media data.
Lecture 36 — Mining Data Streams | Mining of Massive Datasets | Stanford University
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Live Data Streaming in Power BI
The speed of creating an Analysis or an Overview of the KPIs in a company is growing in significance. To make quality decisions, one needs both quality data and up-to-date data. Without accurate live data, it’s difficult to steer the ship. This session will look at today’s methods of live data streaming and in doing that we’re going to look at the famous IoT world. This session will incorporate samples using live key-strokes input, followed by more real-life samples. Excel, Visual Studio, Azure Data Streaming, PowerShell and Power BI will be shown. The session will require a camera to show Phone and Tablet Dashboard. Follow us on Twitter - https://twitter.com/mspowerbi More questions? Try asking the Power BI Community @ https://community.powerbi.com/
Views: 39853 Microsoft Power BI
Continuous Queries over Data Streams
Continuous queries are a common interface for monitoring dynamically changing data, including data streams. Applications include tracking financial trends, network health monitoring, and sensor deployments. In the STREAM project at Stanford, we have built a comprehensive prototype system that supports rich, declarative continuous queries over data streams. In this talk I will focus on continuous aggregation queries and address the following three problem settings. (1) A large number of queries: Here a primary challenge is to share resources (e.g., space, computation) across different queries. (2) Limited memory: Here the challenge is to design algorithms for maintaining approximate statistics making the best use of available memory. (3) Distributed systems: Here a primary challenge is to minimize communication while correlating events on distributed streams. I will conclude with a brief summary of my other work, including continuous query language design and semantics, and characterizing memory requirements for continuous queries.
Views: 607 Microsoft Research
Create a Streaming Application Solution to Manage Real-time Traffic
This video shows you how to create a streaming application solution to monitor and manage Real-time Traffic by using Oracle Stream Analytics. ================================= To improve the video quality, click the gear icon and set the Quality to 1080p/720p HD. For more information, see http://www.oracle.com/goto/oll and http://docs.oracle.com Copyright © 2017 Oracle and/or its affiliates. Oracle is a registered trademark of Oracle and/or its affiliates. All rights reserved. Other names may be registered trademarks of their respective owners. Oracle disclaims any warranties or representations as to the accuracy or completeness of this recording, demonstration, and/or written materials (the “Materials”). The Materials are provided “as is” without any warranty of any kind, either express or implied, including without limitation warranties or merchantability, fitness for a particular purpose, and non-infringement.
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: 2750 WekaMOOC
Data Stream Processing   Concepts and Implementations by Matthias Niehoff
In this talk I will give 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. In the end I will give a brief outline on the implementation status of those strategies in the popular streaming frameworks Apache Spark Streaming, Apache Flink and Kafka Streams. Matthias Niehoff is an IT consultant at codecentric AG in Germany, where he focuses on big data and streaming applications with Apache Cassandra and Apache Spark as well as other tools in the area of big data. Matthias shares his experience at conferences, meetups, and user groups.
Views: 1184 Devoxx
Data Stream Basics
Fundamental issues relating to the transmission of digital (data) streams such as coding, signal element identification, synchronizing, and framing structures.
Views: 8392 noessllc
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: 5577 MapR Technologies
The real-time journey from raw streaming data to AI-based analytics
Roy Ben-Alta, solution architect and principal business development manager at Amazon Web Services, and Anodot’s Chief Data Scientist Dr. Ira Cohen present various design patterns and share a solution implemented using Amazon Kinesis as a real-time event data processing pipeline that feeds Anodot’s AI-based analytics service, discovering and alerting on the anomalies in the data in real time and helping you avoid costly business incidents.
Views: 151 Anodot
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
Adaptive Machine Learning for Real-Time Streaming
Direct processing of real-time data can provide a crucial edge in the software-and-services industry. Combining such processing with machine learning can provide a reasoning flow and enable runtime updates of the machine-learning model. Customer scenarios in manufacturing and IT services will benefit.
Views: 4694 Microsoft Research
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: 783 J On The Beach 2018
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: 17391 LucidProgramming
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: 69173 sentdex
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
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: 546 Intricity101
How to Apply Machine Learning (R,  Apache Spark, H2O.ai) To Real Time Streaming Analytics
This video shows how business analysts, data scientists and developers work together to bring an analytic machine learning model into a (real time) production deployment. The beginning explains in two minutes the methodology before a 10min live demo discusses use cases such as customer churn and predictive analytics to demonstrate how different tooling for visual analytics / data discovery (TIBCO Spotfire), advanced analytics / machine learning (TIBCO Spotfire in conjunction with R, H2O.ai, Apache Spark) and stream processing / streaming analytics (TIBCO StreamBase, TIBCO Live Datamart) are combined by leveraging the same analytic model (e.g. clustering, random forest) without redevelopment. You are just beginning your journey with deploying analytic models to real time processing? Feel free to contact me to discuss your architecture, challenges and questions… If you want to discover some components by yourself, please check out our new and growing TIBCO Community Wiki (https://community.tibco.com/wiki). It already contains a lot of information about the discussed components, e.g. the page “Machine Learning in TIBCO Spotfire and TIBCO Streambase” (https://community.tibco.com/wiki/machine-learning-tibco-spotfirer-and-tibco-streambaser). You can also ask questions in the Answers section to get a response by a TIBCO expert or other community members (https://community.tibco.com/answers).
Views: 3929 Kai Wähner
2018 IEEE International Conference on Data Stream Mining & Processing
2018 IEEE International Conference on Data Stream Mining & Processing, August 21-25, 2018, Lviv
Views: 194 Dsmp Conference
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: 8108 @Scale
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: 10490 Roshan
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: 251 KDD2016 video
Extremely Fast Decision Tree Mining for Evolving Data Streams
Extremely Fast Decision Tree Mining for Evolving Data Streams Albert Bifet (Telecom ParisTech) Jiajin Zhang (Noah's Ark Lab, Huawei) Wei Fan (Huawei Noah’s Ark Lab) Cheng He (Noah's Ark Lab, Huawei) Jianfeng Zhang (Noah's Ark Lab, Huawei) Jianfeng Qian (Huawei Noah's Ark Lab) Geoffrey Holmes (University of Waikato) Bernhard Pfahringer (University of Waikato) Nowadays real-time industrial applications are generating a huge amount of data continuously every day. To process these large data streams, we need fast and efficient methodologies and systems. A useful feature desired for data scientists and analysts is to have easy to visualize and understand machine learning models. Decision trees are preferred in many real-time applications for this reason, and also, because combined in an ensemble, they are one of the most powerful methods in machine learning. In this paper, we present a new system called streamDM-C++, that implements decision trees for data streams in C++, and that has been used extensively at Huawei. Streaming decision trees adapt to changes on streams, a huge advantage since standard decision trees are built using a snapshot of data, and can not evolve over time. streamDM-C++ is easy to extend, and contains more powerful ensemble methods, and a more efficient and easy to use adaptive decision tree. We compare our new implementation with VFML, the current state of the art implementation in C, and show how our new system outperforms VFML in speed using less resources. More on http://www.kdd.org/kdd2017/
Views: 479 KDD2017 video
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: 8774 Microsoft Research
Streaming Analytics presented by Big Data Developers at Streams Developer Day
Big Data Developers presents streaming analytics, a hands on tutorial broadcast live during Streams Developer Day. To find your local Big Data Developers meetup group and learn more about the applications of big data visit www.ibm.meetup.com. With smart phones and fully instrumented cars, the amount of data we can collect from moving objects is growing at staggering rates. In a few years, the automotive industry will be the largest data producer of data after utilities --bigger than health care. And with this Big Data volume comes Big Data challenges. The opportunity for applying all this data in real-time to problems in transportation, congestion management, emergency response, microweather prediction, supply chain management, and so on is tremendous. But this requires a real-time streaming analytics platform that can integrate GPS locations, telematics messages and sensor readings, video, and other kinds of information--and scale up to any level. Beyond automotive applications, the need for a real-time big-data streaming analytics platform is becoming critical in other industries, including Government, Telecommunications, Healthcare, Energy and Utilities, Finance, and Manufacturing. Join the Big Data Developers meetup and hear about future live streams: http://www.meetup.com/bigdatadevelopers Subscribe to the IBM Analytics Channel: https://www.youtube.com/subscription_center?add_user=ibmbigdata The world is becoming smarter everyday, join the conversation on the IBM Big Data & Analytics Hub: www.ibmbigdatahub.com www.youtube.com/user/ibmbigdata www.facebook.com/IBManalytics www.twitter.com/IBMAnalytics www.linkedin.com/company/ibm-big-data-&-analytics www.slideshare.net/IBMBDA
Views: 1348 IBM Analytics
Real-Time Data Streaming - automatio.co
Real-Time Data Streaming of worldometers.info For the early access of Automatio, go to https://automatio.co
Views: 127 Stefan Smiljkovic
Talend Big Data Streaming Sports Analysis
See what's new in our latest version - http://www.talend.com/products This video demonstrating how to achieve clear, easy-to-read data, running through Talend Real time Big Data Platform and coming to you LIVE from an outside source. We’ve all seen some form of professional sport—soccer, football, hockey and even baseball... where professional athletes test their physical strength and endurance against one another in a competitive arena. But did you know that before these displays of physicality, a different team of mental analyst are crunching datasets to find that physical edge over an opponent. How is this possible? Through the use of live-action cameras and sensors hovering over the field or placed along the sidelines, these cameras and sensors are able to capture the movement and position of individual players during the course of competition. These data points can be set up to run through Talend and Spark to calculate any number of advanced metrics and then pushed out into a format for coaches, team members, managers, and even those in the online betting industry to use.
Views: 1845 Talend
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: 2160 Microsoft Developer
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: 441 Devoxx FR
Streaming Data
A brief introduction to Streaming Data.
Views: 162 Frank Blau
Make Streaming IoT Analytics Work For You: The Devil is in the Details
Real-time streaming analytics drives value for every business - from advertising to IoT enabled smart cities. Streaming technology must be able to continuously capture and process terabytes of data from a growing cacophony of sources - clickstreams, financial transactions, social media feeds, IT logs, location-tracking events etc. To make all this work for you there is a need for high throughput, low-latency fault-tolerant distributed systems with specialized algorithms, complex event processing, and fine control over data in transit. And you need a standards based approach to make everything work together. We will share how to leverage streaming technologies to build standards based cloud services that are able to securely discover, register, authenticate and communicate with IoT devices, and thus allow delivery of large scale analytics that enable the delivery of DNS, identity, marketing, data information, real time analytics, security and communications services to the IoT
Views: 2034 Hortonworks
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: 4316 MapR Technologies
How to use the Twitter API v1.1 with Python to stream tweets
Part 1: http://youtu.be/pUUxmvvl2FE Part 2: http://youtu.be/d-Et9uD463A Part 3: http://youtu.be/AtqqVXZ365g In this video, you are shown how to use Twitter's API v1.1 to stream tweets using Python. Twitter's on-site documentation for their API is massive, but I found it to be a bit overboard for the simple task I wanted to achieve. If you have been having trouble figuring out how to stream twitter in python, this should help you. Sentdex.com Facebook.com/sentdex Twitter.com/sentdex Example code: http://sentdex.com/sentiment-analysisbig-data-and-python-tutorials-algorithmic-trading/how-to-use-the-twitter-api-1-1-to-stream-tweets-in-python/
Views: 148100 sentdex
Collect Twitter Data into MongoDB
Collect Twitter data into MongoDB using Twitter REST API and Streaming API, and query data in MongoDB. REST API Code: https://github.com/xbwei/Data-Mining-on-Social-Media/blob/master/MongoDB/Collect_Tweets_MongoDB_REST.py Streaming API Code: https://github.com/xbwei/Data-Mining-on-Social-Media/blob/master/MongoDB/Collect_TWeets_MongoDB_Stream.py Query Code: https://github.com/xbwei/Data-Mining-on-Social-Media/blob/master/MongoDB/Demo_MongoDB.py
Views: 4543 Xuebin Wei
Modeling Data Streams Using Sparse Distributed Representations
In this screencast, Jeff Hawkins narrates the presentation he gave at a workshop called "From Data to Knowledge: Machine-Learning with Real-time and Streaming Applications." The workshop was held May 7-11, 2012 at the University of California, Berkeley. Slides: http://www.numenta.com/htm-overview/05-08-2012-Berkeley.pdf Abstract: Sparse distributed representations appear to be the means by which brains encode information. They have several advantageous properties including the ability to encode semantic meaning. We have created a distributed memory system for learning sequences of sparse distribute representations. In addition we have created a means of encoding structured and unstructured data into sparse distributed representations. The resulting memory system learns in an on-line fashion making it suitable for high velocity data streams. We are currently applying it to commercially valuable data streams for prediction, classification, and anomaly detection In this talk I will describe this distributed memory system and illustrate how it can be used to build models and make predictions from data streams. Live video recording of this presentation: http://www.youtube.com/watch?v=nfUT3UbYhjM General information can be found at https://www.numenta.com, and technical details can be found in the CLA white paper at https://www.numenta.com/faq.html#cla_paper.
Views: 19966 Numenta
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 data mining and analysis in real time - IQLECT's Ampere and Python
This video demonstrates how we can mine data using python script and tweepy API and later use that same data to analyze trends in twitter using IQLECT's Ampere. Ampere is a real time big data analytics platform that can receive data from any source and provide actionable insights for business. The step by step guide shows how the guide can be easily used to mine twitter for specific keywords. For more videos and documentation please visit www.iqlect.com
Views: 201 IQLECT
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: 55792 Google Cloud Platform
Live Streaming Architecture
Episode 6 - Live Streaming Architecture 3 Live Streaming Sections 1. Publisher 2. Server 3. Viewer
Views: 11014 livestreamninja
SAMOA: A Platform for Mining Big Data Streams - Gianmarco De Francisci Morales
Talk about Apache SAMOA at Strata 2014 in Barcelona.
Dynamic Clustering of Streaming Short Documents
Author: Weinan Zhang, Department of Computer Science and Engineering, Shanghai Jiao Tong University Abstract: Clustering technology has found numerous applications in mining textual data. It was shown to enhance the performance of retrieval systems in various different ways, such as identifying different query aspects in search result diversification, improving smoothing in the context of language modeling, matching queries with documents in a latent topic space in ad-hoc retrieval, summarizing documents etc. The vast majority of clustering methods have been developed under the assumption of a static corpus of long (and hence textually rich) documents. Little attention has been given to streaming corpora of short text, which is the predominant type of data in Web 2.0 applications, such as social media, forums, and blogs. In this paper, we consider the problem of dynamically clustering a streaming corpus of short documents. The short length of documents makes the inference of the latent topic distribution challenging, while the temporal dynamics of streams allow topic distributions to change over time. To tackle these two challenges we propose a new dynamic clustering topic model - DCT - that enables tracking the time-varying distributions of topics over documents and words over topics. DCT models temporal dynamics by a short-term or long-term dependency model over sequential data, and overcomes the difficulty of handling short text by assigning a single topic to each short document and using the distributions inferred at a certain point in time as priors for the next inference, allowing the aggregation of information. At the same time, taking a Bayesian approach allows evidence obtained from new streaming documents to change the topic distribution. Our experimental results demonstrate that the proposed clustering algorithm outperforms state-of-the-art dynamic and non-dynamic clustering topic models in terms of perplexity and when integrated in a cluster-based query likelihood model it also outperforms state-of-the-art models in terms of retrieval quality. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
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