Data stream treatment using sliding windows with MapReduce


  • María José Basgall Instituto de Investigación en Informática (III-LIDI), Facultad de Informática - Universidad Nacional de La Plata
  • Waldo Hasperué Instituto de Investigación en Informática (III-LIDI), Facultad de Informática - Universidad Nacional de La Plata
  • Marcelo Naiouf Instituto de Investigación en Informática (III-LIDI), Facultad de Informática - Universidad Nacional de La Plata


big data, mapreduce, stream processing


Knowledge Discovery in Databases (KDD) techniques present limitations when the volume of data to process is very large. Any KDD algorithm needs to do several iterations on the complete set of data in order to carry out its work. For continuous data stream processing it is necessary to store part of it in a temporal window. In this paper, we present a technique that uses the size of the temporal window in a dynamic way, based on the frequency of the data arrival and the response time of the KDD task. The obtained results show that this technique reaches a great size window where each example of the stream is used in more than one iteration of the KDD task.


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How to Cite

Basgall, M. J., Hasperué, W., & Naiouf, M. (2016). Data stream treatment using sliding windows with MapReduce. Journal of Computer Science and Technology, 16(02), p. 76–83. Retrieved from



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