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Lecture 36 — Mining Data Streams | Mining of Massive Datasets | Stanford University
 
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What is DATA STREAM MINING? What does DATA STREAM MINING mean? DATA STREAM MINING meaning
 
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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: 1027 The Audiopedia
Data -  Batch processing vs Stream processing
 
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Data - Batch processing vs Stream processing Video in Tamil https://goo.gl/DgUdQp Video in English https://goo.gl/5U2d1b YouTube channel link www.youtube.com/atozknowledgevideos Website http://atozknowledge.com/ Technology in Tamil & English I created this video with the YouTube Video Editor (http://www.youtube.com/editor)
Views: 3516 atoz knowledge
IoT Big Data Stream Mining (Part 1)
 
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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: 1683 KDD2016 video
Introduction to Data Streaming (C. Escoffier, G. Zamarreño)
 
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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: 539 Devoxx FR
Streaming Data: How to Move from State to Flow - Whiteboard Walkthrough
 
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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: 6776 MapR Technologies
How to do real-time Twitter Sentiment Analysis (or any analysis)
 
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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: 71169 sentdex
Concept Drift Detector in Data Stream Mining
 
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Jorge Casillas, Shuo Wang, Xin Yao, Concept Drift Detection in Histogram-Based Straightforward Data Stream Classification, 6th International Workshop on Data Science and Big Data Analytics, IEEE International Conference on Data Mining, November 17-20, 2018 - Singapore http://decsai.ugr.es/~casillas/downloads/papers/casillas-ci44-icdm18.pdf This presentation shows a novel algorithm to accurately detect changes in non-stationary data streams in a very efficiently way. If you want to know how the yacare caiman, the cheetah and the racer snake are related to this research, do not stop watching the video! More videos here: http://decsai.ugr.es/~casillas/videos.html
Views: 143 Jorge Casillas
SAMOA: A Platform for Mining Big Data Streams by Gianmarco De Francisci Morales
 
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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
Mining Big Data Streams with Apache SAMOA - Albert Bifet - JOTB16
 
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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: 893 J On The Beach
#bbuzz: Mikio Braun "Beyond scaling: real-time event analysis with stream mining"
 
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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.
Twitter API with Python: Part 1 -- Streaming Live Tweets
 
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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: 36472 LucidProgramming
Cloud Data Streaming
 
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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: 744 Intricity101
Data Stream Processing   Concepts and Implementations by Matthias Niehoff
 
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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: 1814 Devoxx
How to Apply Machine Learning (R,  Apache Spark, H2O.ai) To Real Time Streaming Analytics
 
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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: 4210 Kai Wähner
Record and Plot Real time Data in Python
 
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This sample exercise records, analyzes, and plots real-time data in Python. It is an introductory exercise for the project listed at http://apmonitor.com/che263/index.php/Main/CourseProjects
Views: 28133 APMonitor.com
Scalable real time processing techniques
 
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Big data today revolves primarily around batch processing with Hadoop and Spark. In many cases, however, it is desirable to quickly react to incoming data, and an approximate result within seconds may be preferable to an accurate result after minutes or hours. This presentation is a technical introduction into a few practical and scalable stream processing techniques for common data stream aggregation and mining scenarios. The techniques are also suitable as basis for dynamic data-based personalisation and recommendations. Lars Albertsson, Data Architect, Schibsted Media Group
Views: 101 Javaforum
IoT Big Data Stream Mining (Part 2)
 
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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: 337 KDD2016 video
Data Stream Algorithms
 
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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
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: 8434 @Scale
Setup For Performing Real Time Analysis of Twitter Data
 
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This video list out all the settings to be done in order to perform real time analysis. The step wise solution is listed in order to fetch Twitter streaming data. How to setup an architecture to fetch the data from twitter streaming API in HDFS.
Views: 277 Viveak Sharma
Spark Streaming:  Large Scale near real-time Stream Processing
 
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Tathagata Das of UC Berkeley AmpLab presents Spark Streaming which has been released as alpha in release 0.7 of Spark. This presentation was given at the Spark meetup on Feb 21st 2013 at Conviva in San Mateo, Ca. Download: http://spark-project.org/downloads/ Summary: 00:09 Motivation 01:07 Case study: Conviva, Inc. 03:26 Goals 04:04 Existing Streaming Systems, 05:07 Storm and Trident 06:40 Discretized Stream Processing Series of very small, deterministic batch jobs 07:52 State between batches in memory, immutable, fault tolerant 08:11 Minimum batch time period from 1/2 second to aproximately 1 second 08:46 Visual representation of Discretized Stream Processing 16:32 Fault Recovery 17:02 Fault Recovery is computed in parallel 17:12 Programming Model and DStreams 17:53 DStream Data Sources, {HDFS, Kafka, Flume, Twitter, TCP sockets, Akka actor, ZeroMQ} 18:34 Transformations of DStreams RDD like operations, New window and stateful operations 19:18 Output: HDFS, console, foreach arbitrary operation on every RDD 19:53 Example: 20 most popular hashtags in the last 10 minutes of tweet stream 23:15 Smart window-based reduce 25:24 Sort transform by key on hashtags 27:09 Demo using AWS 29:39 Other Operations, Maintaining state, tracking sessions 30:45 Performance, Can process 6 GB/sec (60M records/sec) on 100 nodes at sub-second latency, Grep, WordCount 31:32 Comparison Spark Streaming: 670k records/sec/node Storm: 115k records/sec/node Apache S4: 7.5k records/sec/node 32:30 Fast Fault Recovery, recovers from faults/stragglers within 1 sec 32:53 Real Applications: Conviva real-time monitoring of video metadata 34:05 Real Applications: Mobile Millennium Project, traffic estimation Markov chain Monte Carlo simulations on GPS observations 35:39 Failure semantics 35:53 Java API for Streaming 36:06 Contributors, 5 from UC Berkeley, 3 external contributors 36:12 Vison, one stop shop, stream processing + Ad-hoc queries + batch processing 37:24 Questions 38:00 Strata Conference presentations on Berkeley Data Analytics Stack (BDAS) 38:37 Conclusion New Streaming guide Spark Streaming system in paper http://tinyurl.com/dstreams
Views: 10416 Stoney Vintson
Advanced Data Mining with Weka (2.4: MOA classifiers and streams)
 
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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: 3002 WekaMOOC
2018 IEEE International Conference on Data Stream Mining & Processing
 
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2018 IEEE International Conference on Data Stream Mining & Processing, August 21-25, 2018, Lviv
Views: 278 Dsmp Conference
Extremely Fast Decision Tree Mining for Evolving Data Streams
 
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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: 574 KDD2017 video
Adaptive Machine Learning for Real-Time Streaming
 
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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: 5032 Microsoft Research
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: 317 KDD2018 video
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: 776 codecentric AG
Real Time Big Data - InfoSphere Streams in Action - Part 1
 
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http://www.ibm.com/software/data/bigdata/ Chris Howard, a Big Data Solution Architect from the Office of the CTO, IBM Software Group Europe, discusses Big Data projects that require real time processing of large volumes of data, including Smart Bay, a project to instrument Galway bay in Ireland. This is part 1 of a two part series. Video produced, directed and edited by Gary Robinson, contact robinsg at us.ibm.com Music Track title: Clouds, composer: Dmitriy Lukyanov, publisher:Shockwave-Sound.Com Royalty Free
Views: 9457 IBM Analytics
Flink Forward 2015: Albert Bifet – SAMOA Mining Big Data Streams with Apache Flink
 
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Flink Forward Conference on Apache Flink, October 12 & 13 at Kulturbrauerei Berlin
Views: 506 Flink Forward
Streaming Data
 
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A brief introduction to Streaming Data.
Views: 162 Frank Blau
Data Stream Basics
 
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Fundamental issues relating to the transmission of digital (data) streams such as coding, signal element identification, synchronizing, and framing structures.
Views: 9058 noessllc
Real-Time Analytics On Data Streams - A New Era For Big Data And IoT - Jeremy Hillier
 
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What incentive do companies have to invest in large and complex Big Data architectures where installation, management and infrastructure costs detract from the ROI? Many sectors like energy, manufacturing, automotive, financial, etc. demand immediate responses to optimise their businesses and deliver on services promised. Analysing data streams in real-time becomes a requirement for companies to become successful with IoT projects, as data generated moves at high velocity, often at scale. Already, real-time is no longer fast enough, as companies are looking to achieve predictive insights. Questions: • How you can combine your streaming analytics, big data storage and computations to create value for your clients. • How the combination of the previous ones can help you achieve ROI #HyperightDataTalks is a video podcast of best presentations, discussions and interviews with some of the most innovative minds, enterprise practitioners, technology and service providers, start-ups and academics, working with Data Science, Data Management, Big Data, Analytics, AI, IOT and much more. All presentations are taken from Hyperight´s Data summits and now available for you. For more interviews, audio podcast and videos from some of the best presentations from our Data Summits, please visit http://www.hyperight.com Presentation recorded during: Data Innovation Summit 2017 - http://www.datainnovationsummit.com/ Follow us on twitter: https://Twitter.com/datasweden More information about Hyperight: http://www.hyperight.com/ Subscribe to our channel: https://www.youtube.com/channel/UCCLYBm1MHI3jIvZo9YKPq-g
Views: 203 Hyperight AB
Streams Mining Toolkit - Base case
 
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InfoSphere Streams Mining Toolkit
Views: 811 IBMStreams
AWS San Francisco Big Data Meetup | Stream Processing at Answers
 
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Learn More: http://amzn.to/1sT8Pi8 Learn about how Answers, the analytics component of the mobile development platform Fabric, processes billions of events in realtime using Twitter's new stream processing engine, Heron. Cory Dolphin, Software Engineer at Twitter, explains some of the challenges the team faced while scaling Storm, and how Heron has helped them fly faster. Specifically, Cory describes how Heron's separation of message processing guarantees and backpressure propagation have allowed them to build, scale and tune high volume topologies.
Views: 597 Amazon Web Services
Modeling Data Streams Using Sparse Distributed Representations
 
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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: 20450 Numenta
Getting Ready for Change: Handling Concept Drift in Predictive Analytics
 
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In the real world data often arrives in streams and evolves over time. Concept drift in supervised learning means that the relation between the input data and the target variable changes. Therefore, in many real-world applications the learning models need to adapt to the anticipated changes. In this talk I will overview the state of the art in concept drift research in data mining and related areas. First, I will introduce the problem of concept drift with illustrative real-world examples, characterize adaptive learning process, categorize existing strategies for (reactive) handling concept drift in the most assumed setting � unpredictable changes happen in hidden contexts that are not observable to the adaptive learning system. Then, I will show why from the application perspective it is interesting to look into several other operational settings that commonly occur in practice, but have been underexplored in academia. In particular, I will show that there is a room for proactive approaches for handling. I will conclude the talk with an overview of the recent trends and next challenges in concept drift research.
Views: 1275 Microsoft Research
Real-time stream data mining based on CanTree and Gtree | Final Year Projects 2016 - 2017
 
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Views: 4 myproject bazaar
Streaming Analytics (Part 1)
 
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Author: Ashish Gupta, LinkedIn Corporation Abstract: Recently we have seen emergence and huge adoption of social media, internet of things for home, industrial internet of things,mobile applications and online transactions. These systems generate streaming data at very large scale. Building technologies and distributed systems that can capture, process and analyze this streaming data in real time is very important for gaining real time insights. Real-time analysis of streaming data can be used for applications as diverse as fraud detection, in-session targeting and recommendations, control systems for transportation systems and smarter cities, earthquake prediction and control of autonomous vehicles. This programming tutorial provides overview of streaming data systems and hands on tutorial on building streaming systems using open source technologies. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 189 KDD2016 video
Massive Online Analytics for the Internet of Things (IoT)
 
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Author: Albert Bifet, Telecom ParisTech Abstract: Big Data and the Internet of Things (IoT) have the potential to fundamentally shift the way we interact with our surroundings. 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 stream mining. In this talk, I will present an overview of data stream mining, and I will introduce some popular open source tools for data stream mining. More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 77 KDD2017 video
What is stream processing?
 
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CEO Damian Black explains how stream processing works and reveals what streaming analytics is all about.
Views: 5977 SQLstream
From Data to Knowledge - 206 - Joao Gama
 
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Slides: http://lyra.berkeley.edu/CDIConf/pdfs/JGama-2012.pdf Joao Gama: "Challenges on Mining Evolving Data Streams". A video from the UC Berkeley Conference: From Data to Knowledge: Machine-Learning with Real-time and Streaming Applications (May 7-11, 2012). Abstract Joao Gama (Lab. of A.I. and Decision Support, Economics at Univ. of Porto, Portugal) The computational model of data streams imposes new challenges and open new research opportunities on the design of data mining algorithms. Data is abundant, being continuously generated from time-changing processes with unknown dynamics. Evolving time-changing data requires that learning algorithms must be able to monitor the evolution of the learning process. Monitoring the learning process opens the ability of predictive self-diagnosis; not only after a failure has occurred, but also predictive, before the failure. These aspects require monitoring the evolution of the learning process, taking into account the available resources. Diagnosis is a significant and useful characteristic, and requires the ability of reasoning and learning about the learning process itself. In this talk we present a one-pass classification algorithm able for self-diagnosis. It is able to detect and react to changes in the process generating data, identifies contexts using drift detection, characterize contexts using meta-learning, and select the most appropriate base model for the incoming data using unlabeled examples.
Views: 318 ckleinastro
How to use the Twitter API v1.1 with Python to stream tweets
 
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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: 153972 sentdex
Prototype-based learning on concept-drifting data streams (KDD 2014 Presentation)
 
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Prototype-based learning on concept-drifting data streams KDD 2014 Presentation Junming Shao Zahra Ahmadi Stefan Kramer Data stream mining has gained growing attentions due to its wide emerging applications such as target marketing, email filtering and network intrusion detection. In this paper, we propose a prototype-based classification model for evolving data streams, called SyncStream, which dynamically models time-changing concepts and makes predictions in a local fashion. Instead of learning a single model on a sliding window or ensemble learning, SyncStream captures evolving concepts by dynamically maintaining a set of prototypes in a new data structure called the P-tree. The prototypes are obtained by error-driven representativeness learning and synchronization-inspired constrained clustering. To identify abrupt concept drift in data streams, PCA and statistics based heuristic approaches are employed. SyncStream has several attractive benefits: (a) It is capable of dynamically modeling evolving concepts from even a small set of prototypes and is robust against noisy examples. (b) Owing to synchronization-based constrained clustering and the P-Tree, it supports an efficient and effective data representation and maintenance. (c) Gradual and abrupt concept drift can be effectively detected. Empirical results shows that our method achieves good predictive performance compared to state-of-the-art algorithms and that it requires much less time than another instance-based stream mining algorithm.
Time Series for Monitoring, Metrics, Real-Time Analytics and IoT/Sensor Data
 
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In this webinar, Director of Products Shubhra Kar will walk you through what time-series is (and isn't), what makes it different than stream processing, full-text search and other NoSQL solutions. He'll also work through why time-series databases engines are the superior choice for the monitoring, metrics, real-time analytics and Internet of Things/sensor data use cases. Ready to give InfluxDB a spin? Click below to get started for free: https://www.influxdata.com/influxcloud-trial/
Views: 2661 InfluxData
Anomaly detection using machine learning in Azure Stream Analytics | Azure Friday
 
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Azure Stream Analytics is a fully managed serverless offering on Azure. With the new Anomaly Detection functions in Stream Analytics, the whole complexity associated with building and training custom machine learning (ML) models is reduced to a simple function call resulting in lower costs, faster time to value, and lower latencies. #azure #azurestreamanalytics #machinelearning Anomaly Detection in Azure Stream Analytics (docs) https://aka.ms/azfr/525/01 Anomaly detection using built-in machine learning models in Azure Stream Analytics (blog post) https://aka.ms/azfr/525/02 Azure Stream Analytics docs https://aka.ms/azfr/525/03 Azure Stream Analytics - Real-time data analytics overview https://aka.ms/azfr/525/04 Azure Stream Analytics pricing https://aka.ms/azfr/525/05 Create a free account (Azure) https://aka.ms/azfr/525/free
Views: 603 Microsoft Developer