  Mining massive spatio-temporal data can help a variety of real-world
applications such as city capacity planning, event management, and social
network analysis. The tensor representation can be used to capture the
correlation between space and time and simultaneously exploit the latent
structure of the spatial and temporal patterns in an unsupervised fashion.
However, the increasing volume of spatio-temporal data has made it
prohibitively expensive to store and analyze using tensor factorization.
  In this paper, we propose SkeTenSmooth, a novel tensor factorization
framework that uses adaptive sampling to compress the tensor in a temporally
streaming fashion and preserves the underlying global structure. SkeTenSmooth
adaptively samples incoming tensor slices according to the detected data
dynamics. Thus, the sketches are more representative and informative of the
tensor dynamic patterns. In addition, we propose a robust tensor factorization
method that can deal with the sketched tensor and recover the original
patterns. Experiments on the New York City Yellow Taxi data show that
SkeTenSmooth greatly reduces the memory cost and outperforms random sampling
and fixed rate sampling method in terms of retaining the underlying patterns.
