| 000 | 01479naa a2200241 a 4500 | ||
|---|---|---|---|
| 003 | AR-LpUFIB | ||
| 005 | 20250311170452.0 | ||
| 008 | 230201s2016 xx r 000 0 eng d | ||
| 024 | 8 |
_aDIF-M7755 _b7975 _zDIF007085 |
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| 040 |
_aAR-LpUFIB _bspa _cAR-LpUFIB |
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| 100 | 1 | _aBasgall, María José | |
| 245 | 1 | 0 | _aData stream treatment using sliding windows with MapReduce |
| 300 | _a1 archivo (1,2 MB) | ||
| 500 | _aFormato de archivo PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca) | ||
| 520 | _aKnowledge 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. | ||
| 534 | _aJournal of Computer Science & Technology, 16(2), pp. 76-83. | ||
| 650 | 4 | _aBIG DATA | |
| 650 | 4 | _aBASES DE DATOS | |
| 650 | 4 | _aFLUJO DE DATOS | |
| 700 | 1 | _aHasperué, Waldo | |
| 700 | 1 | _aNaiouf, Ricardo Marcelo | |
| 942 | _cCP | ||
| 999 |
_c56861 _d56861 |
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