TY  - GEN
T1  - Data stream treatment using sliding windows with MapReduce
A1  - Basgall, María José
A2  - Hasperué, Waldo
A2  - Naiouf, Ricardo Marcelo
LA  - English
UL  - http://vufind10-pruebas.sigbunlp.bibliotecas.unlp.edu.ar/Record/dif.56861
AB  - 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.
NO  - Formato de archivo PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca)
KW  - BIG DATA
KW  - BASES DE DATOS
KW  - FLUJO DE DATOS
ER  -