As deep learning (DL) continues to advance, effective feature extraction from large-scale data remains crucial for enhancing model performance. To leverage the advantages of the frequency domain, such as concentrated signal energy, prominent data features, and rich detailed characteristics, this paper proposes a novel frequency-domain feature extraction method. However, existing frequency component selection algorithms often struggle to adapt to diverse tasks, tend to yield only locally optimal solutions, and require prolonged processing times. To overcome these limitations, we introduce the Adaptive Fast Frequency Selection (AFFS) algorithm, which seamlessly integrates a frequency component selection factor layer into DL models to identify globally optimal frequency combinations suited to various downstream tasks. We further analyze the relationship between selected frequency components and model performance, providing theoretical guarantees regarding optimality, robustness, and generalization error bounds. Moreover, a fast selection procedure is developed to exploit the empirically observed rapid convergence of the selection-factor ranking, significantly accelerating the selection process. Extensive experiments on five datasets, ten DL models, and two subsequent tasks demonstrate that AFFS achieves superior performance: even when the input data size is reduced to only 10% of the original frequency features, model classification accuracy improves by approximately 1%, while the early stopping mechanism shortens the selection process by about 80%.

Matrix completion is a method for imputing missing data, typically based on the assumption of a low-rank structure. However, in practice, various factors can obscure this structure, leading to a perceived high-rank nature. Recently, a few studies begin to address high-rank data completion, their methods rely on specific hypothesis distributions that are often impractical to validate in real-world scenarios. Therefore, in this paper, we tackle high-rank data completion without any hypothesis distribution. Firstly, we theoretically demonstrate that the Radial Basis Function (RBF) kernel feature mapping has the capability to transform general high-rank data into low-rank data in a higher-dimensional feature space. Secondly, combining with the adaptive row and column neighbor information, a novel graph-based high-rank matrix completion algorithm is developed. Thirdly, experiments on four real datasets across three domains demonstrate that the proposed method reduces reconstruction error by up to 63% compared to the second-best method at the same missing rate. Furthermore, to achieve similar accuracy, it requires up to 40% fewer samples. Our code is released at https://github.com/shiqinyeah/Graph-HRMC.git.

Sparse data gathering has become a promising solution for reducing measurement costs by leveraging the inherent sparsity of data. However, most existing approaches rely on low-dimensional models such as compressive sensing or matrix completion, which are limited in capturing complex high-dimensional structures. To overcome these limitations, we proposeTensorMon, a novel tensor-based sparse data gathering framework that introduces a cuboid sampling strategy to more effectively exploit multidimensional correlations. Unlike traditional entry-based or tube-based sampling, TensorMon introduces the innovative concept ofcuboid sampling. We further develop a lightweight sampling scheduling algorithm and a non-iterative inference algorithm to ensure efficient measurement planning and accurate reconstruction of unmeasured data. Theoretical analysis establishes a new performance bound for our sampling strategy, which is significantly lower than those in existing literature. To validate our theoretical findings, we conduct extensive experiments on four real-world datasets: two network monitoring datasets, a city-scale crowd flow dataset, and a road traffic speed dataset. Experimental results demonstrate that TensorMon achieves substantial reductions in measurement cost, delivers high inference accuracy, and ensures rapid data recovery, highlighting its effectiveness and practicality across diverse application scenarios.

Tensor data is widely used in fields such as smart grids, cloud systems, and deep learning. As the scale of this data increases, storage and transmission costs rise significantly. Many tensor data exhibit low-rank structures, offering the potential for data compression through low-rank decomposition techniques. Tucker decomposition, a typical low-rank decomposition technique, achieves data compression and interpretability by capturing complex data correlations and representing the original tensor with a compact core tensor and factor matrices. However, the compression ratio provided by Tucker decomposition is often insufficient, particularly for large-scale tensors. To tackle this issue, we propose a robust low-rank tensor compression method that leverages Tucker decomposition with quantization and coding. Initially, we establish a robust Tucker decomposition framework that decomposes the low-rank tensor into a core tensor and factor matrices using well-designed Tucker rank-setting rules. This framework effectively handles noise and missing values. Subsequently, we conduct an in-depth analysis of the numerical characteristics of the core tensor and factor matrices within the Tucker decomposition framework. Based on their distinct characteristics, we design tailored quantization and coding schemes to compress the core tensor and factor matrices, respectively, thereby significantly improving the compression ratio of Tucker decomposition while maintaining high accu-racy. Through extensive experiments on four publicly available datasets (which can form 3 or 4 order tensors), we demonstrate that our approach can achieve compression ratios$4\times-10\times$higher than the best competitor, with recovery errors improved by 8% - 42%.

Network telemetry, characterized by its efficient push model and high-performance communication protocol (gRPC), offers a new avenue for collecting fine-grained real-time data. Despite its advantages, existing network telemetry systems lack a theoretical basis for setting measurement frequency, struggle to capture informative samples, and face challenges in setting a uniform frequency for multi-metric monitoring. We introduce FineMon, an innovative adaptive network telemetry scheme for precise, fine-grained, multi-metric data monitoring. FineMon leverages a novel Two-sided Frequency Adjustment (TFA) to dynamically adjust the measurement frequency on the Network Management System (NMS) and infrastructure sides. On the NMS side, we provide a theoretical basis for frequency determination, drawing on changes in the rank of multi-metric data to minimize monitoring overhead. On the infrastructure side, we adjust the frequency in real-time to capture significant data fluctuations. We propose a robust Enhanced-Subspace-based Tensor Completion (ESTC) to ensure accurate recovery of fine-grained data, even with noise or outliers. Through extensive experimentation with three real datasets, we demonstrate FineMon's superiority over existing schemes in reduced measurement overhead, enhanced accuracy, and effective capture of intricate temporal features.

Robust PCA is a popular anomaly detection technique and has been widely used in many applications. Although Robust PCA is promising, it is usually designed in a two-order matrix form, which is inferior to the tensor that can capture multilinearity features of data. Moreover, the detection accuracy under Robust PCA further suffers due to its sensitivity to the rank parameter which is hard to set in practice and the limitation of PCA method in capturing the non-linear feature in the data. To address the issues, we propose a Robust Tensor Convolutional Autoencoder (RTCAE) where the autoencoder instead of SVD is exploited to recover the normal data from the corrupted measurement tensor data. However, directly exploiting deep autoencoder may suffer from the problem of high memory consumption and computation overhead due to the large number of parameters used in autoencoder. To make our anomaly detection lightweight, we further design a Light Convolutional Autoencoder (LightCAE) which contains a compressed autoencoder by exploiting tensor factorization to largely compress the parameters while significantly reducing the computation complexity. We conduct extensive experiments on three real data traces to compare the performance of our proposed schemes (RTCAE and lightCAE) with that of seven baseline algorithms. The experiment results demonstrate that our proposed RTCAE achieves the highest anomaly detection accuracy. Moreover, our LightCAE requires over 60 times smaller memory storage than that required in RTCAE while achieving the similar anomaly detection accuracy.

A Sketch is an excellent probabilistic data structure, which records the approximate statistics of data streams. Linear additivity is an important property of sketches. This paper studies how to keep the linear property after sketch compression. Most existing compression methods do not keep the linear property. We propose TreeSensing, an accurate, efficient, and flexible framework to linearly compress sketches. In TreeSensing, we first separate a sketch into two parts according to counter values. For the sketch with small counters, we propose a technique called TreeEncoding to compress it into a hierarchical structure. For the sketch with large counters, we propose a technique called SketchSensing to compress it using compressive sensing. We theoretically analyze the accuracy of TreeSensing. We use TreeSensing to compress 7 sketches and conduct two end-to-end experiments: distributed measurement and distributed machine learning. Experimental results show that TreeSensing outperforms prior art on both accuracy and efficiency, which achieves up to 100× smaller error and 5.1× higher speed than state-of-the-art Cluster-Reduce. All related codes are open-sourced.

Multi-set membership query is a fundamental issue for network functions such as packet processing and state machines monitoring. Given the rigid query speed and memory requirements, it would be promising if a multi-set query algorithm can be designed based on Bloom filter (BF), a space-efficient probabilistic data structure. However, existing efforts on multi-set query based on BF suffer from at least one of the following drawbacks: low query speed, low query accuracy, limitation in only supporting insertion and query operations, or limitation in the set size. To address the issues, we design a novel B h sequence-based Bloom filter (B h BF) for multi-set query, which supports four operations: insertion, query, deletion, and update. In B h BF, the set ID is encoded as a code in a B h sequence. Exploiting good properties of B h sequences, we can correctly decode the BF cells to obtain the set IDs even when the number of hash collisions is high, which brings high query accuracy. In B h BF, we propose two strategies to further speed up the query speed and increase the query accuracy. On the theoretical side, we analyze the false positive and classification failure rate of our B h BF. Our results from extensive experiments over two real datasets demonstrate that B h BF significantly advances state-of-the-art multi-set query algorithms.

A major factor that prevents the large scale deployment of Mobile Crowd Sensing (MCS) is its sensing and communication cost. Given the spatio-temporal correlation among the environment monitoring data, matrix completion (MC) can be exploited to only monitor a small part of locations and time, and infer the remaining data. Rather than only taking random measurements following the basic MC theory, to further reduce the cost of MCS while ensuring the quality of missing data inference, we propose an Active Sparse MCS (AS-MCS) scheme which includes a bipartite-graph-based sensing scheduling scheme to actively determine the sampling positions in each upcoming time slot, and a bipartite-graph-based matrix completion algorithm to robustly and accurately recover the un-sampled data in the presence of sensing and communications errors. We also incorporate the sensing cost into the bipartite-graph to facilitate low cost sample selection and consider the incentives for MCS. We have conducted extensive performance studies using the data sets from the monitoring of PM 2.5 air condition and road traffic speed, respectively. Our results demonstrate that our AS-MCS scheme can recover the missing data at very high accuracy with the sampling ratio only around $11%$, while the peer matrix completion algorithms with similar recovery performance requires up to 4-9 times the number of samples of ours for both the data sets.

Tensor completion can be applied to fill in the missing data, which is import for many data applications where the data are incomplete. To infer the missing data, existing tensor-completion algorithms generally assume that the tensor data have global low-rank structure and apply a single model to fit the overall observed data through the global optimization. However, there are different correlation levels among application data, thus the ranks of some sub-tensors can be even lower relative to that of the large tensor. Fitting a single model to all data will compromise the performance of data recovery. To increase the accuracy in missing data recovery, we propose to apply local tensor completion (Local-TC) to recover data from sub-tensors, with each containing data of higher correlations. Although promising, as the tensor data are only organized logically, it is difficult to determine the relationship among data. We propose to exploit locality-sensitive hash (LSH) to quickly find the data correlation and reorganize tensor data, based on which data entries with high correlations are put into the same sub-tensor. The experiment results demonstrate that Local-TC is very effective in increasing the recovery accuracy.