A Hybrid Framework for Effective Prediction of Online Streaming Data

Abstract

Abstract In this paper, we present a hybrid model to perform the training and testing of prediction model with online streaming data. Prediction of online streaming data is a time critical task. Huge volume of data that is being generated online need to be ingested to a prediction model and to be used to train and test the prediction model dynamically which improves the learning rate. The existing approaches for dynamic training and testing use the local infrastructure or virtual machines from the cloud infrastructure to increase the learning rate of the prediction model with streaming data. Recently many applications prefer serverless cloud infrastructure than virtual machines. However, using the serverless infrastructure for the entire prediction process will have time and space tradeoffs due to its autonomic feature. Hence in this paper we propose a hybrid approach that uses the three different environments such as the local infrastructure, virtual machine and serverless cloud for different stages. A novel approach to select the suitable environment to train and test the LSTM based air quality prediction model with stream data is proposed with increased learning rate and reduced resource utilization.