Encyclopedia of Big Data Technologies

Living Edition
| Editors: Sherif Sakr, Albert Zomaya

StreamMine3G: Elastic and Fault Tolerant Large Scale Stream Processing

  • André Martin
  • Andrey Brito
  • Christof Fetzer
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-63962-8_145-1


During the past decade, we have been witnessing a massive growth of data. In particular the advent of new mobile devices such as smartphones, tablets and online services like Facebook and Twitter created a complete new era for data processing. Although there exist already well-established approaches such as MapReduce (Dean and Ghemawat 2008) and its open-source implementation Hadoop (2015) in order to cope with these large amounts of data, there is a recent trend of moving away from batch processing to low-latency online processing using event stream processing (ESP) systems. Inspired by the simplicity of the MapReduce programming paradigm, a number of open-source as well as commercial ESP systems have evolved over the past years such as Apache S4 (Neumeyer et al. 2010; Apache 2015) (originally pushed by Yahoo!), Apache Storm (2015) (Twitter), and Apache Samza (2015) (LinkedIn), addressing the strong need for data processing in near real time.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.TU DresdenDresdenGermany
  2. 2.UFCGCampina GrandeBrazil

Section editors and affiliations

  • Asterios Katsifodimos
    • 1
  • Pramod Bhatotia
    • 2
  1. 1.Delft University of TechnologyDelftNetherlands
  2. 2.School of InformaticsUniversity of EdinburghEdinburghUnited Kingdom