Growth Scale Prediction of Big Data for Information Systems Based on a Deep Learning SAEP Method

• Main Authors: Wenjuan Liu, Guosun Zeng, Kekun Hu
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Big data; information system; growth scale prediction; deep learning; stacked autoencoders (SAE)
• Online Access: https://ieeexplore.ieee.org/document/8960659/

With the explosive growth of big data in various application areas, it is becoming very important for information management system to know the real-time growth change and the long-term increasing trend of big data. Thus, in this paper, we propose a big data growth scale prediction method based on deep learning intelligence. By analyzing the features of big data in various information systems, we summarize several basic data types which are ubiquitous in all kinds of information systems, and give the calculation method of each data growth scale time series. Considering the complex correlation among various types of data produced in information systems and the demand for data scale prediction, a deep learning prediction model, stacked autoencoders prediction (SAEP), is built. The previous time series prediction only considered one time series, but the SAEP can consider multiple time series simultaneously. We use data growth scale time series with different time interval as training sample sets to study features of big data in information systems, and then we can obtain the parameters of the SAEP model for the expected time interval. In order to solve the training problem of deep neural network, we also give a layer-wise training algorithm whose basic idea is to train all the hidden layers of the SAEP model layer by layer and then fine tune the parameters of the whole network. Repeated experiments show that the prediction performance of the SAEP model is stable and obviously better than the traditional exponential regression prediction method.