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ABSTRACTCarnegie Mellon, School of Computer ScienceCapturing the Spatio-Temporal Behavior of Real Traffic Data Mengzhi Wang, Anastassia Ailamaki, Christos Faloutsos Carnegie Mellon University Traffic data, like disk and memory accesses, typically exhibits burstiness, temporal locality, and spatial locality. However, except for qualitative speculations, it is not even known how to measure the spatio-temporal correlation, let alone how to reproduce it realistically. In this paper, we propose the "entropy plots" to quantify correlation and develop a new statistical model, the 'PQRS' model, to capture the burstiness and correlation of the real spatio-temporal traffic. Moreover, the model requires very few parameters and offers linear scalability. Experiments with multiple real data sets show that our model can mimic real traces very well. FULL PAPER: pdf |