Spatio-Temporal Tensor Sketching via Adaptive Sampling

TL;DR

This paper introduces SkeTenSmooth, an adaptive sampling tensor factorization framework that efficiently compresses and analyzes large spatio-temporal data streams, preserving key patterns while reducing memory usage.

Contribution

It proposes a novel adaptive sampling method for tensor sketching that captures dynamic data patterns more effectively than traditional fixed sampling approaches.

Findings

Significantly reduces memory cost in tensor analysis.

Outperforms random and fixed sampling methods in pattern retention.

Effective on real-world NYC taxi data.

Abstract

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.