Detection of Events in Multiple Streams of Surveillance Data

Abstract

Simultaneous monitoring of multiple streams of data that carry corroborating evidence can be beneficial in many event detection applications. This chapter reviews analytic approaches that can be employed in such scenarios. We cover established statistical algorithms of multivariate time series baseline estimation and forecasting. They are relevant when multiple streams of data can be modeled jointly. We then present more recent methods which do not have to rely on such an assumption. We separately address techniques that deal with data in a specific form of a record of transactions annotated with multiple descriptors, often encountered in the practice of health surveillance. Future event detection algorithms will benefit from incorporation of machine learning methodology. This will enable adaptability, utilization of human feedback, and building reliable detectors using some examples of events of interest. That will lead to highly scalable and economical multi-stream event detection systems.