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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. .
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: 218 Jorge Casillas
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: 1290 The Audiopedia
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: 1723 KDD2016 video
#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.
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
Data Streams I
 
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Michael Kapralov, EPFL https://simons.berkeley.edu/talks/clone-clone-clone-clone-sketching-linear-algebra-i-basics-dim-reduction Foundations of Data Science Boot Camp
Views: 686 Simons Institute
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: 397 KDD2018 video
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: 951 J On The Beach
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: 4081 atoz knowledge
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: 2164 Devoxx
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: 9366 noessllc
Continuous Queries over Data Streams
 
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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: 778 Microsoft Research
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
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: 303 Dsmp Conference
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: 342 KDD2016 video
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: 209 Hyperight AB
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: 7356 MapR Technologies
Stream Processing Design Patterns | Data Council NYC '18
 
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WANT TO EXPERIENCE A TALK LIKE THIS LIVE? Barcelona: https://www.datacouncil.ai/barcelona New York City: https://www.datacouncil.ai/new-york-city San Francisco: https://www.datacouncil.ai/san-francisco Singapore: https://www.datacouncil.ai/singapore ABOUT THE TALK: Streaming applications can be designed to balance or favor one or more of latency, throughput, memory consumption, or CPU load. In order to scale growing real-time applications well, properties like replayability, at-least-once and exactly-once processing, and out-of-order processing drive decisions that need to be made inside the streaming application and by data producers and consumers. This presentation discusses some useful design patterns for streaming applications that help deliver great value and an exceptional digital personalization experience to our customers, with personalized responses for Capital One's Eno chatbot as an example. ABOUT THE SPEAKER: Andreas Markmann is Manager of Data Engineering at Capital One and tech lead for the Potomac Clickstream project, where he works with engineers and partner teams to democratize strongly scaling data efficiently for the benefit of customer experience. Before joining Capital One, he created efficient parallel classical and quantum dynamics algorithms for the simulation of laser-molecule and nuclear fusion reactions. He has a PhD in Theoretical Physics from University College London and an MSc in Pure Mathematics from Queen Mary and Westfield College London. He financed his undergraduate degree improving his English as a sightseeing tour guide in Berlin, Germany and volunteered as a rock climbing, swimming, and programming coach for underserved communities. FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai Facebook: https://www.facebook.com/datacouncilai
Views: 1221 Data Council
I ♥ Logs: Apache Kafka and Real-Time Data Integration
 
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An introduction to Apache Kafka for data integration, ETL, and stream processing.
Views: 49797 Jay Kreps
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: 5138 Microsoft Research
Stream processing and real-time data pipelines - Vladimir Schreiner
 
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Vladimir Schreiner: Stream processing and real-time data pipelines Stream processing is a trending programming technique. It brings the big data use-cases to a real time. In a first part we’ll discuss the building blocks of a stream processing pipeline as well as the new challenges it brings: dealing with infinite data, unordered and late events or fault-tolerance. Another usual concern of the streaming tools is a complexity — one has to learn, setup and maintain multiple moving parts to become productive. That’s not a case of Hazelcast Jet — zero-infrastructure streaming library. I’ll show you how to write full-blown streaming pipelines in less than a hundred lines of Java code for applications such as Bitcoin, Twitter sentiment analysis and real-time worldwide commercial aircraft monitoring.
Views: 71 code.kiwi.com
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: 626 KDD2017 video
Real-time stream data mining based on CanTree and Gtree | Final Year Projects 2016 - 2017
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 5 myproject bazaar
Stream Data Mining: A Big Data Perspective
 
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Author: Latifur Khan, Department of Computer Science, Erik Jonsson School of Engineering & Computer Science, The University of Texas at Dallas Abstract: Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Data streams demonstrate several unique properties that together conform to the characteristics of big data (i.e., volume, velocity, variety and veracity) and add challenges to data stream mining. In this talk we will present an organized picture on how to handle various data mining techniques in data streams. Most existing data stream classification techniques ignore one important aspect of stream data: arrival of a novel class. We address this issue and propose a data stream classification technique that integrates a novel class detection mechanism into traditional classifiers, enabling automatic detection of novel classes before the true labels of the novel class instances arrive. Novel class detection problem becomes more challenging in the presence of concept-drift, when the underlying data distributions evolve in streams. In this talk we will show how to make fast and correct classification decisions under this constraint with limited labeled training data and apply them to real benchmark data. In addition, we will present a number of stream classification applications such as adaptive malicious code detection, website fingerprinting, evolving insider threat detection and textual stream classification. This research was funded in part by NSF, NASA, Air Force Office of Scientific Research (AFOSR) and Raytheon. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 948 KDD2016 video
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: 72052 sentdex
Key Concepts on Data streams
 
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Spark Streaming Key Concepts on Dstreams, Spark Streaming Message Fault Tolerance Mechanism, Different Sources for Spark Streaming, Spark Streaming Real time examples, Dstream Transformations - Standard (Stateless) RDD Transformations & Stateful transformations, Stateful operations - Window Operations & Update operations, Sample Spark Streaming Code Explaination, Spark Streaming Application Maven Build & Package Dependencies
Streaming Data
 
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A brief introduction to Streaming Data.
Views: 162 Frank Blau
Machine Learning and Data Streams - David Thompson (SETI Talks)
 
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SETI Talks archive://seti.org/talks Next-generation science instruments such as the SKA, LSST, and terrestrial sensor networks will dramatically increase the volume of collected data. This enables detection of very rare transient anomalies, but also creates new challenges since comprehensive storage is impossible and analysis must occur in real time. Dr. Thompson will discuss machine learning approaches for online anomaly detection in data streams. Pattern recognition triages the incoming data for comprehensive analysis of candidate events, retaining robustness against changing noise conditions and interferences. Examples from radio astronomy (the Very Long Baseline Array Fast Transients Experiment) demonstrate the practical benefits of an adaptive approach.
Views: 2190 SETI Institute
Webinar: Real-Time Data Processing with Apache Flink
 
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Extremely fast data processing is a requirement for any modern enterprise app. data Artisans’ Apache Flink provides efficient, fast, consistent, and robust handling of massive streams of events, as well as batch processing as a special case of stream processing. With Flink available on Mesosphere DC/OS, users can deploy a Flink cluster with the click of a button, and run Flink elastically with other fast data technologies such as Kafka and Cassandra. In this webinar, you will learn how to install a Flink cluster on DC/OS and how to use Flink to build real-time applications. Join Jamie Grier and Mike Winters from data Artisans, and Joerg Schad, distributed systems engineer at Mesosphere, to learn more about Flink and DC/OS.
Views: 3785 Mesosphere
Lecture 37 — Counting 1 's (Advanced) | 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. .
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: 581 Devoxx FR
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: 1396 Microsoft Research
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.
Concept Drift: Monitoring Model Quality in Streaming Machine Learning Applications
 
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Most machine learning algorithms are designed to work on stationary data. Yet, real-life streaming data is rarely stationary. Models lose prediction accuracy over time if they are not retrained. Without model quality monitoring, retraining decisions are suboptimal and costly. Here, we review the monitoring methods and evaluate them for applicability in modern fast data and streaming applications.
Views: 1875 Lightbend
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: 282 Viveak Sharma
Decision Trees for Mining Data Streams Based on the Gaussian Approximation
 
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Decision Trees for Mining Data Streams Based on the Gaussian Approximation +91-9994232214,8144199666, [email protected], www.ieeeprojectsin.com, www.ieee-projects-chennai.com IEEE PROJECTS 2014 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:[email protected] http://ieeeprojectsin.com/Cloud-Computing http://ieeeprojectsin.com/Data-Mining http://ieeeprojectsin.com/Android http://ieeeprojectsin.com/Image-Processing http://ieeeprojectsin.com/Networking http://ieeeprojectsin.com/Network-Security http://ieeeprojectsin.com/Mobile-Computing http://ieeeprojectsin.com/Parallel-Distributed http://ieeeprojectsin.com/Wireless-Communication http://ieeeprojectsin.com/NS2-Projects http://ieeeprojectsin.com/Matlab Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support
Views: 63 PROJECTS2014
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/ www.intricity.com
Views: 997 Intricity101
Dynamic Clustering of Streaming Short Documents
 
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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/
Views: 458 KDD2016 video
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: 3105 WekaMOOC
Extending Apache Flink stream processing with Apache Samoa ML methods - Piotr Wawrzyniak
 
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Flink Forward Berlin, September 2017 #flinkforward Piotr Wawrzyniak, Chief R&D Specialist at Orange Polska S.A. Many stream processing applications can benefit from or need to rely on the prediction made with machine learning (ML) methods. In this presentation, new features of Apache Samoa are presented with a real data processing scenario. These features make Apache SAMOA fully accessible for Apache Flink users: (1) the data stream processed within Apache Flink is forwarded to Apache Samoa stream mining engine to perform predictions with stream-oriented ML models, (2) ML models evolve after every labelled instance and, at the same time, new predictions are sent back to Apache Flink. In both cases, Apache Kafka is used for data exchange. Hence, Apache Samoa is used as stream mining engine, provided with input data from, and sending predictions to Apache Flink. During the presentation, real life aspects are illustrated with code examples, such as input and prediction stream integration and monitoring latency of data processing and stream mining. https://www.slideshare.net/FlinkForward/flink-forward-berlin-2017-piotr-wawrzyniak-extending-apache-flink-stream-processing-with-apache-samoa-machine-learning-methods https://data-artisans.com/
Views: 567 Flink Forward
Akamai DataStream overview
 
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Providing developers and admins with comprehensive insights into the internet’s middle mile.
Streams Mining Toolkit - Base case
 
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InfoSphere Streams Mining Toolkit
Views: 811 IBMStreams
Continuous Data Governance with Spring Cloud Data Flow
 
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IoT has introduced many new opportunities in the areas of smarthomes, healthcare, automotive and logistics. So much so that the evolution of the technology has outpaced the much-needed governance and compliance to control false events and unwanted outcomes. As the IoT industry usage matures so does its need for applying integrity to the automation and machine driven decisions. The accuracy of real-time data streaming after applying transformations is paramount. False positives happen, and will continue to happen regardless of future technology. The ability to quickly diagnose a chain of decisions quickly can determine whether your company sinks or swims. Enfuse.io is building enterprise governance frameworks for application accountability. The solution allows Enfuse.io to provide verifications of thousands of machine driven decisions a second, mid-stream. These verifications are based off of a set of rules, actions and re-actions, largely known in Blockchain as smart-contracts. These contracts can guarantee that the action intended and initiated by a stream of data is validated before driving a potentially very important outcome. Corelogic is an Enfuse client which benefits from an immutable audit trail provided by blockchain through an easy to use software development kit which acts as a layer between the blockchain and Corelogic's data driven applications. Speaker: Cahlen Humphreys Co-founder, Enfuse Filmed at SpringOne Platform 2018
Views: 667 SpringDeveloper
Introducing Data Stream by Eventbase
 
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Data Stream by Eventbase helps enterprises put the power of event data to work. Learn more: https://www.eventbase.com/event-data-stream
Views: 28 Eventbase Tech