Using Local Inference in Massively Distributed Systems

As the scale of today’s networked techno-social systems continues to increase, the analysis of their global phenomena becomes increasingly difficult, due to the continuous production of streams of data scattered among distributed, possibly resource-constrained nodes, and requiring reliable resolution in (near) real-time. The goal of LIFT is to enable the local detection of global phenomena and the efficient and effective detection of phase changes in very large data streams, where it is impossible or ineffective to accumulate all data into a single place. In addition, this will give rise to new methods for analyzing privacy-sensitive data, where it is not desirable to move data away from the point where it is collected. This will be facilitated by developing a theory based on the novel Safe-Zone-Approach and related methodologies.