Large-scale online mobility monitoring with exponential histograms

The spread of digital signage and its instantaneous adaptability of content challenges out-of-home advertising to conduct performance evaluations in an online fashion. This implies a tremendous increase in the granularity of evaluations as well as a complete new way of data collection, storage and analysis. In this paper we propose a distributed system for the large-scale online monitoring of poster performance indicators based on the evaluation of mobility data collected by smartphones. In order to enable scalability in the order of millions of users and locations, we use a local data processing paradigm and apply exponential histograms for an efficient storage of visit statistics over sliding windows. In addition to an immediate event centralization we also explore a hierarchical architecture based on a merging technique for exponential histograms. We provide an evaluation on the basis of a real-world data set containing more than 300 million GPS points corresponding to the movement activity of nearly 3,000 persons. The experiments show the accuracy and efficiency of our system.