Correlated time series (CTS) forecasting plays an essential role in many practical applications, such as traffic management and server load control. Many deep learning models have been proposed to improve the accuracy of CTS forecasting. However, while models have become increasingly complex and computationally intensive, they struggle to improve accuracy. Pursuing a different direction, this study aims instead to enable much more efficient, lightweight models that preserve accuracy while being able to be deployed on resource-constrained devices. To achieve this goal, we characterize popular CTS forecasting models and yield two observations that indicate directions for lightweight CTS forecasting. On this basis, we propose the LightCTS framework that adopts plain stacking of temporal and spatial operators instead of alternate stacking that is much more computationally expensive. Moreover, LightCTS features light temporal and spatial operator modules, called L-TCN and GL-Former, that offer improved computational efficiency without compromising their feature extraction capabilities. LightCTS also encompasses a last-shot compression scheme to reduce redundant temporal features and speed up subsequent computations. Experiments with single-step and multi-step forecasting benchmark datasets show that LightCTS is capable of nearly state-of-the-art accuracy at much reduced computational and storage overheads.

With the proliferation of GPS-enabled smart devices and increased availability of wireless network, spatial crowdsourcing (SC) has been recently proposed as a framework to automatically request workers (i.e., smart device carriers) to perform location-sensitive tasks (e.g., taking scenic photos, reporting events). In this paper, we study a destination-aware task assignment problem that concerns the optimal strategy of assigning each task to proper worker such that the total number of completed tasks can be maximized whilst all workers can reach their destinations before deadlines after performing assigned tasks. Finding the global optimal assignment turns out to be an intractable problem since it does not imply optimal assignment for individual worker. Observing that the task assignment dependency only exists amongst subsets of workers, we utilize tree-decomposition technique to separate workers into independent clusters and develop an efficient depth-first search algorithm with progressive bounds to prune non-promising assignments. In order to make our proposed framework applicable to more scenarios, we further optimize the original framework by proposing strategies to reduce the overall travel cost and allow each task to be assigned to multiple workers. Extensive empirical studies verify that the proposed technique and optimization strategies perform effectively and settle the problem nicely.

The pervasiveness of GPS-enabled devices and wireless communication technologies results in massive trajectory data, incurring expensive cost for storage, transmission, and query processing. To relieve this problem, we propose a novel framework for compressing trajectories, REST (Reference-based Spatio-temporal trajectory compression), by which a raw trajectory is represented by concatenation of a series of historical (sub-)trajectories (called reference trajectories) that form the compressed trajectory within a given spatio-temporal deviation threshold. In order to construct a reference trajectory set that can most benefit the subsequent compression, we propose three kinds of techniques to select reference trajectories wisely from a large dataset. To address the computational issue caused by the large number of combinations of reference trajectories, we propose efficient greedy algorithms that run in the blink of an eye and dynamic programming algorithms that can achieve the optimal compression ratio. In addition, by indexing the reference trajectories directly with an in-memory R-tree and building connections to the raw trajectories with inverted index, we develop an extremely efficient algorithm that can answer spatio-temporal range queries over trajectories in their compressed form. Extensive experiments on a real trajectory dataset demonstrate the superiority of our framework over existing representative approaches.

The ubiquity of mobile devices and the accompanying deployment of sensing technologies have resulted in a massive amount of trajectory data. One important fundamental task is trajectory similarity computation, which is to determine how similar two trajectories are. To enable effective and efficient trajectory similarity computation, we propose a novel robust model, namely Contrastive Learning based Trajectory Similarity Computation (CL-TSim). Specifically, we employ a contrastive learning mechanism to learn the latent representations of trajectories and then calculate the dissimilarity between trajectories based on these representations. Compared with sequential auto-encoders that are the mainstream deep learning architectures for trajectory similarity computation, CL-TSim does not require a decoder and step-by-step reconstruction, thus improving the training efficiency significantly. Moreover, considering the non-uniform sampling rate and noisy points in trajectories, we adopt two type of augmentations, i.e., point dowm-sampling and point distorting, to enhance the robustness of the proposed model. Extensive experiments are conducted on two widely-used real-world datasets, i.e., Porto and ChengDu, which demonstrate the superior effectiveness and efficiency of the proposed model.

With the ubiquity of smart devices, Spatial Crowdsourcing (SC) has emerged as a new transformative platform engaging mobile users to perform tasks by physically traveling to specified locations. In this paper, we propose a novel preference-aware task assignment system based on workers' temporal preferences, which includes two components: History-based Context-aware Tensor Decomposition (HCTD) for workers' temporal preferences modeling and preference-aware task assignment. We model workers' preferences with a three-dimension tensor. Supplementing missing entries of the tensor through HCTD with the assistant of historical data and other two context matrices, we recover workers' preferences for different categories of tasks in different time slots. Several preference-aware individual task assignment algorithms are then devised, aiming to maximize the total number of task assignments at every time instance, where we give higher priorities to workers who are more interested in the tasks. To make our proposed framework applicable to more scenarios, we further optimize the original framework by proposing strategies to allow each task to be assigned to a group of workers such that the task can be completed by these workers simultaneously, where workers' tolerable waiting time and consensus are considered. We conduct extensive experiments using a real dataset, verifying the practicability of the methods.

With the pervasiveness of GPS-enabled smart devices and increased wireless communication technologies, spatial crowdsourcing (SC) has drawn increasing attention in assigning location-sensitive tasks to moving workers. In real-world scenarios, for the complex tasks, SC is more likely to assign each task to more than one worker, called group task assignment (GTA), for the reason that an individual worker cannot complete the task well by herself. It is a challenging issue to assign worker groups the tasks that they are interested in and willing to perform. In this paper, we propose a novel framework for group task assignment based on worker groups’ preferences, which includes two components: social impact-based preference modeling (SIPM) and preference-aware group task assignment (PGTA). SIPM employs a bipartite graph embedding model and the attention mechanism to learn the social impact-based preferences of different worker groups on different task categories. PGTA utilizes an optimal task assignment algorithm based on the tree decomposition technique to maximize the overall task assignments, in which we give higher priorities to the worker groups showing more interests in the tasks. We further optimize the original framework by proposing strategies to improve the effectiveness of group task assignment, wherein a deep learning method and the group consensus are taken into consideration. Extensive empirical studies verify that the proposed techniques and optimization strategies can settle the problem nicely.