Probabilistic inference of object identifications for event stream analytics

Abstract

Recent years have witnessed the emergence of real-time object monitoring applications driven by the explosion of small inexpensive sensors. In many real-world applications, not all sensed events carry the identification of the object whose action they report on, so called "non-ID-ed" events. Reasons range from heterogeneous sensing devices to human's choosing to conceal their identifications. Such non-ID-ed events prevent us from performing objectbased analytics, such as tracking, alerting and pattern matching. We propose a probabilistic inference framework, called FISS, to tackle this problem by inferring the missing object identification associated with an event. Specifically, as a foundation we design a timevarying graphic model to capture correspondences between sensed events and objects. Upon this formal model, we elaborate how to adapt the Forward-backward (FB) inference algorithm to continuously infer probabilistic identifications for non-ID-ed events. However, we demonstrate that FB is neither scalable nor efficient over event streams. To overcome this deficiency, we propose a suite of strategies for optimizing its performance, including the selective smoothing technique that significantly reduces the number of random variables that need to be smoothed, and the finish-flag mechanism that enables early termination of backward computations. Our experimental results, using large-volume streams of a realworld healthcare application, demonstrate the accuracy, efficiency, and scalability of FISS. Especially FISS achieves on average 15x higher throughput than our basic FB inference.

On complexity and optimization of expensive queries in complex event processing

“…While pattern queries have been used for complex event processing in this area, query evaluation is often complicated by the uncertainty of the occurrence time and value of events because they are derived through probabilistic inference from incomplete, noisy raw data streams [9,27].…”