SAMOA: Scalable Advanced Massive Online Analysis

http://www.samoa-project.net/ SAMOA is distributed streaming machine learning (ML) framework that contains a programing abstraction for distributed streaming ML algorithms. It is a project started at Yahoo Labs Barcelona. SAMOA enables development of new ML algorithms without dealing with the complexity of underlying streaming processing engines (SPE, such as Apache Storm and Apache S4). SAMOA users [...]

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RMOA: Massive online data stream classifications with R & MOA

http://bnosac.be/index.php/blog/32-rmoa-massive-online-data-stream-classifications-with-r-a-moa For R users who work with a lot of data or encounter RAM issues when building models on large datasets, MOA and in general data streams have some nice features. Namely: It uses a limited amount of memory. So this means no RAM issues when building models. Processes one example at a time, and [...]

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The streams Framework

http://www.jwall.org/streams/ The streams framework is a Java implementation of a simple stream processing environment by Christian Bockermann and Hendrik Blom at TU Dortmund University. It aims at providing a clean and easy-to-use Java-based platform to process streaming data. The core module of the streams library is a thin API layer of interfaces and classes that [...]

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New Release of MOA 14.04

We’ve made a new release of MOA 14.04. The new features of this release are: Change detection Tab Albert Bifet, Jesse Read, Bernhard Pfahringer, Geoff Holmes, Indre Zliobaite: CD-MOA: Change Detection Framework for Massive Online Analysis. IDA 2013: 92-103 New Tutorial on Clustering by Frederic Stahl. New version of Adaptive Model Rules for regression Ezilda [...]

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New release of MOA 13.11

We’ve made a new release of MOA 13.11. The new feature of this release is: Temporal dependency evaluation Albert Bifet, Jesse Read, Indre Zliobaite, Bernhard Pfahringer, Geoff Holmes: Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them. ECML/PKDD (1) 2013: 465-479 You find the download link for this release on the MOA homepage: [...]

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Temporal Dependency in Classification

The paper presented at ECML-PKDD 2013 titled “Pitfalls in benchmarking data stream classification and how to avoid them“, showed that classifying data streams has an important temporal component, which we are currently not considering in the evaluation of data-stream classifiers. A very simple classifier that considers this temporal component, the non-change classifier that predicts only [...]

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New recommender algorithms and evaluation

MOA has been extended in order to provide an interface to develop and visualize online recommender algorithms. This is a simple example in order to show the functionality of the EvaluateOnlineRecommender task in MOA. This task takes a rating predictor and a dataset (each training instance being a [user, item, rating] triplet) and evaluates how [...]

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New release of MOA 13.08

We’ve made a new release of MOA 13.08. The new features of this release are: new outlier detection tab Dimitrios Georgiadis, Maria Kontaki, Anastasios Gounaris, Apostolos N. Papadopoulos, Kostas Tsichlas, Yannis Manolopoulos: Continuous outlier detection in data streams: an extensible framework and state-of-the-art algorithms. SIGMOD Conference 2013: 1061-1064 new regression tab FIMT-DD regression tree Elena [...]

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Pre-release of MOA 13.08

We are preparing a new release of MOA 13.08. The new release of MOA will contain the FIMT-DD regression tree, the Adaptive Model Rules, a recommender system based in BRISMFPredictor, and some more features in clustering and a new outlier detection tab. You will find the source code at the repository: https://code.google.com/p/moa/ Cheers, The MOA Team

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CFP – KDD BIGMINE Workshop on Big Data Mining

Big Data Mining 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BigMine-13) Conference Dates: August 11-14, 2013 Workshop Date: Aug 11, 2013 Chicago, USA http://www.bigdata-mining.org Key dates: Papers due: June 6th 23:59PM Acceptance notification: June 25, 2013 Workshop Final Paper Due: July 2, 2013 Paper submission [...]

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ADAMS – a different take on workflows

A fascinating new workflow for MOA and Weka is available. The Advanced Data mining And Machine learning System (ADAMS) is a novel, flexible workflow engine aimed at quickly building and maintaining real-world, complex knowledge workflows. It is written in Java and uses Maven as its build system. The framework was open-sourced in September 2012, released [...]

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Are you using MOA?

Are you using MOA?  Are you doing some interesting stuff using MOA? What are your thoughts about MOA and its future?  We would love to hear your experiences with MOA. Please send us a message (abifet@cs.waikato.ac.nz), we would love to hear what you think.     

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New release of MOA 12.08

We’ve made a new release of MOA 12.08. The new features of this release are: new rule classification methods : VFDR Rules from Learning Decision Rules from Data Streams, IJCAI 2011, J. Gama, P. Kosina migrated to proper maven project NaiveBayesMultinomial and SGD updated with adaptive DoubleVector for weights new multilabel classifiers: Scalable and efficient [...]

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CFP – Data Streams Track – ACM SAC 2013

============================================================ ACM SAC 2013 The 28th Annual ACM Symposium on Applied Computing in Coimbra, Portugal, March 18-22, 2013. http://www.acm.org/conferences/sac/sac2013/ DATA STREAMS TRACK http://www.cs.waikato.ac.nz/~abifet/SAC2013/ ============================================================ CALL FOR PAPERS The rapid development in information science and technology in general and in growth complexity and volume of data in particular has introduced new challenges for the research community. [...]

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Summer School on Massive Data Mining, August 8-10, 2012

August 8-10, 2012, IT University of Copenhagen, Denmark The summer school is aimed at PhD students and young researchers both from the algorithms community and the data mining community. A typical participant will be working in a group that aims at publishing in algorithms conferences such as ESA and SODA, and/or in data mining conferences [...]

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Big Data Mining (BigMine-12)

Call for Papers Big Data Mining (BigMine-12)1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BigMine-12) – a KDD2012 Workshop KDD2012 Conference Dates: August 12-16, 2012BigMine-12 Workshop Date: Aug 12, 2012Beijing, China http://www.bigdata-mining.org Key dates: Papers due: May 9, 2012 Acceptance notification: May 23, 2012Workshop Final Paper [...]

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New release of MOA 12.03

We’ve made a new release of MOA 12.03. The new features of this release are: new measure graphic visualization for classification Classifiers are now in subpackages: classifiers.tree, classifiers.bayes, classifiers.functions, classifiers.meta, classifiers.drift,… HoeffdingTree, HoeffdingTreeNB, and HoeffdingTreeNBAdaptive are now only one classifier: HoeffdingTree with an option to select how to do the classification at leaves. By default, [...]

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PRICAI 2012 Special Session on Scalable Big Data Mining

http://cs.waikato.ac.nz/~abifet/pricai2012/ September 3 – 7, 2012 Kuching, Sarawak, Malaysia ==============================================  CALL FOR PAPERS Data have become a torrent flowing in many important areas. Big data refers to datasets whose size is beyond the ability of current state-of-the art analytic tools. Streaming data is an specific approach to deal with big data that is evolving and [...]

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Upcoming Conference: “Machine-Learning with Real-time & Streaming Applications”

FIRST CONFERENCE ANNOUNCEMENT: From Data to Knowledge: Machine-Learning with Real-time & Streaming Applications May 7-11 2012 On the Campus of the University of California, Berkeley http://lyra.berkeley.edu/CDIConf/  * * CONFIRMED INVITED SPEAKERS * * Olfa Nasraoui (Louisville), Petros Drineas (RPI), Muthu Muthukrishnan (Rutgers), Alex Szalay (John Hopkins), David Bader (Georgia Tech), Eamonn Keogh (UC Riverside), Joao [...]

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IBLStreams (Instance Based Learner on Streams for Regression and Classification)

IBLStreams (Instance Based Learner on Streams) is an instance-based learning algorithm for classification and regression problems on data streams by Ammar Shaker, Eyke Hüllermeier and Jürgen Beringer. The method is able to handle large streams with low requirements in terms of memory and computational power. Moreover, it disposes of mechanisms for adapting to concept drift [...]

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