Distributed Online Tracking

In online tracking, an observer S receives a sequence of values, one per time instance, from a data source that is described by a function f. A tracker T wants to continuously maintain an approximation that is within an error threshold of the value f(t) at any time instance t, with small communication overhead. This problem was recently formalized and studied in [32, 34], and a principled approach with optimal competitive ratio was proposed. This work extends the study of online tracking to a distributed setting, where a tracker T wants to track a function f that is computed from a set of functions {f(1), .., f(m)} from m distributed ob- servers and respective data sources. This formulation finds numerous important and natural applications, e.g., sensor networks, distributed systems, measurement networks, and pub-sub systems. We formalize this problem and present effective online algorithms for various topologies of a distributed system/network for different aggregate functions. Experiments on large real data sets demonstrate the excellent performance of our methods in practice.