Time-Series Big Data Stream Evaluation

被引：0
|
作者：
Mursanto, Petrus [1 ]
Wibisono, Ari [1 ]
Bayu, Wendy D. W. T. [1 ]
Ahli, Valian Fil [1 ]
Rizki, May Iffah [1 ]
Hasani, Lintang Matahari [1 ]
Adibah, Jihan [1 ]
机构：
[1] Univ Indonesia, Fac Comp Sci, Depok, Indonesia
来源：
2020 5TH INTERNATIONAL WORKSHOP ON BIG DATA AND INFORMATION SECURITY (IWBIS 2020) | 2020年
关键词：
Intelligent Systems; Data Stream; Chernoff Bound; Standard Deviation; FIMT-DD; Big Data;
D O I：
10.1109/iwbis50925.2020.9255607
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 processing is a challenging job. Extensive time-series data need a method of preparation, management, and feature calculation for each data arrival. FIMT-DD is an algorithm for processing predictive regression for big data. The splitting criteria in the standard FIMT-DD algorithm use a Hoeffding Bound. We propose to change the splitting criteria to Chernoff bound. The experimental results and the performance comparisons that we did have better results than the standard method. We use three real-world datasets. The improvement that we propose can produce a 2.3% accuracy improvement for traffic demand data.
引用
收藏
页码：43 / 47
页数：5
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