Big Data Stream Learning with SAMOA

被引:30
|
作者
Bifet, Albert [1 ]
De Francisci Morales, Gianmarco [2 ]
机构
[1] HUAWEI Noahs Ark Lab, Hong Kong, Hong Kong, Peoples R China
[2] Yahoo Labs, Barcelona, Spain
关键词
D O I
10.1109/ICDMW.2014.24
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Big data is flowing into every area of our life, professional and personal. Big data is defined as datasets whose size is beyond the ability of typical software tools to capture, store, manage and analyze, due to the time and memory complexity. Velocity is one of the main properties of big data. In this demo, we present SAMOA (Scalable Advanced Massive Online Analysis), an open-source platform for mining big data streams. It provides a collection of distributed streaming algorithms for the most common data mining and machine learning tasks such as classification, clustering, and regression, as well as programming abstractions to develop new algorithms. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as Storm, S4, and Samza. SAMOA is written in Java and is available at http://samoa-project.net under the Apache Software License version 2.0.
引用
收藏
页码:1199 / 1202
页数:4
相关论文
共 50 条
  • [1] Deep Incremental Learning for Big Data Stream Analytics
    Alex, Suja A.
    Nayahi, J. Jesu Vedha
    [J]. PROCEEDING OF THE INTERNATIONAL CONFERENCE ON COMPUTER NETWORKS, BIG DATA AND IOT (ICCBI-2018), 2020, 31 : 600 - 614
  • [2] Telescopic broad Bayesian learning for big data stream
    Yuen, Ka-Veng
    Kuok, Sin-Chi
    [J]. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, 2024,
  • [3] Incremental Learning Framework for Mining Big Data Stream
    Eisa, Alaa
    EL-Rashidy, Nora
    Alshehri, Mohammad Dahman
    El-bakry, Hazem M.
    Abdelrazek, Samir
    [J]. CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 71 (02): : 2901 - 2921
  • [4] SAMOA: A Platform for Mining Big Data Streams
    De Francisci Morales, Gianmarco
    [J]. PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'13 COMPANION), 2013, : 777 - 778
  • [5] Data stream classification and big data analytics
    Krawczyk, Bartosz
    Wozniak, Michal
    Stefanowski, Jerzy
    [J]. NEUROCOMPUTING, 2015, 150 : 238 - 239
  • [6] Stream-Based Extreme Learning Machine Approach for Big Data Problems
    Horta, Euler Guimaraes
    de Castro, Cristiano Leite
    Braga, Antonio Padua
    [J]. MATHEMATICAL PROBLEMS IN ENGINEERING, 2015, 2015
  • [7] A Classifier Using Online Bagging Ensemble Method for Big Data Stream Learning
    Lv, Yanxia
    Peng, Sancheng
    Yuan, Ying
    Wang, Cong
    Yin, Pengfei
    Liu, Jiemin
    Wang, Cuirong
    [J]. TSINGHUA SCIENCE AND TECHNOLOGY, 2019, 24 (04) : 379 - 388
  • [8] A Classifier Using Online Bagging Ensemble Method for Big Data Stream Learning
    Yanxia Lv
    Sancheng Peng
    Ying Yuan
    Cong Wang
    Pengfei Yin
    Jiemin Liu
    Cuirong Wang
    [J]. Tsinghua Science and Technology, 2019, (04) : 379 - 388
  • [9] A Classifier Using Online Bagging Ensemble Method for Big Data Stream Learning
    Yanxia Lv
    Sancheng Peng
    Ying Yuan
    Cong Wang
    Pengfei Yin
    Jiemin Liu
    Cuirong Wang
    [J]. Tsinghua Science and Technology., 2019, 24 (04) - 388
  • [10] IoT Big Data Stream Mining
    Morales, Gianmarco De Francisci
    Bifet, Albert
    Khan, Latifur
    Gama, Joao
    Fan, Wei
    [J]. KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, : 2119 - 2120