Spatio-Temporal Tensor Sketching via Adaptive Sampling
This work addresses the storage and analysis challenges for large spatio-temporal data in applications like city planning, but it is incremental as it builds on existing tensor factorization methods with adaptive sampling.
The paper tackles the problem of high memory cost in analyzing massive spatio-temporal tensors by proposing SkeTenSmooth, a framework that uses adaptive sampling to compress tensors in a streaming fashion, which greatly reduces memory cost and outperforms random and fixed-rate sampling methods in retaining underlying patterns.
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.