Improved data storage performance based modified-SPEED algorithm

Abstract

With the rising demand for smart devices and smart home systems, automation and activity prediction has become a vital aspect of people's everyday lives. Researchers have focused on developing approaches that detect user activity patterns and used them to predict future actions. One such system is Modified Sequence Prediction via Enhanced Episode Discovery (M-SPEED), which uses spatiotemporal daily life activities to analyze user behaviors. However, the low accuracy of this algorithm can be a limiting factor inefficient activity prediction. Also, the computational overhead of run time and memory causes this algorithm to show poor performance in large datasets. This research focuses on modifying the M-SPEED algorithm to improve its capability to run on a larger dataset while at the same time improving run time. The accuracy is also improved to make it more effective in real-world applications. Proof of algorithm efficiency is provided to ensure system validity, and simulation is carried out on real-life data. The results demonstrate a 66.69% improvement in cumulative memory efficiency, 37% faster run time, and 8.22% better accuracy confirming the proposal's effectiveness