Bao Rong Chang1, Hsiu-Fen Tsai2,*, Yu-Chieh Lin1
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 783-815, 2023, DOI:10.32604/cmes.2022.020128
- 31 August 2022
Abstract Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that
will cause execution inefficiency and low system throughput. This paper aims to enhance the capability of data
retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low
throughput problems. First, integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data
searching and distributed indexing, which reduces the search scope of the database and dramatically speeds up
data searching. Next, exploiting a deep neural network to predict the approximate execution time More >
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