On the Practicality of Subspace Tracking in Information Systems

Abstract

Modeling and characterizing information systems’ observation data (i.e., logs) is fundamental for proper system configuration, security analysis, and monitoring system status. Due to the underlying dynamics of such systems, observations can be viewed as high–dimensional, time–varying, multivariate data. One broad class for concisely modeling systems with such data points is low–rank modeling where the observations manifest themselves in a lower-dimensional subspace. Subspace Tracking plays an important role in many applications, such as signal processing, image tracking and recognition, and machine learning. However, it is not well understood which tracker is suitable for a given information system in a practical setting. In this paper, we present a comprehensive comparative analysis of three state-of-the-art low–rank modeling approaches; GROUSE, PETRELS, and RankMin. These algorithms will be compared in terms of their convergence and stability, parameter sensitivity, and robustness in dealing with missing data for synthetic and real information systems data sets, and then summarize our findings.