I. Introduction
Sparse tensor decomposition (TD) is a popular method for analyzing multi-way data in applications such as signal processing, topic monitoring, and trend analysis [1]. In many of these areas, data arrives in a streaming fashion over time (e.g., new updates on social media), and this poses two significant challenges in using traditional TD algorithms to analyze the data – the complete data is not available a priori, and the amount of accumulated data grows linearly with time. To address these challenges, a number of streaming TD algorithms have been proposed [2]–[5]. Among these, CP-stream [2] represents the state-of-the-art in terms of execution time, fitting error, and scalability on parallel systems. As such, we use CP-stream as the baseline for comparison against our work presented in this paper.