Real-time analysis and management of big time-series data

The ability to process and analyze large volumes of time-series data is in increasing demand in various domains including health care, finance, energy and utilities, transportation, and cybersecurity. Despite the broad use of time-series data worldwide, the design of a system to easily manage, analyze, and visualize large multidimensional time series, with dimensions on the order of hundreds of thousands, is still a challenging endeavor. This paper describes the Streaming Time-Series Analysis and Management (STAM) system as a solution to this problem. STAM provides the capability to glean actionable information from continuously changing time series with thousands of dimensions, in real time. STAM exploits the IBM InfoSphere (R) Streams platform and allows for general-purpose large-scale time-series analytics for applications including anomaly detection, modeling, smoothing, forecasting, and tracking. In addition, the system provides user-friendly tools for managing, deploying, and initiating analytics on large-scale data streams of interest, and provides a web-based graphical visualization interface that allows highlighting of events of interest with interactive menus. In this paper, we describe the system and illustrate its use in a large-scale system-monitoring application.