Multivariate time series data often demonstrate sparse and irregular characteristics in real-world signal processing applications, making anomaly detection challenging. This paper introduces STMG-AD, a spatio-temporal mixed graph neural controlled differential equation method with adaptive connection sampling, designed specifically for anomaly detection in irregular multivariate time series. By integrating causal graphs with graph attention networks and employing adaptive connection sampling coupled with Monte Carlo dropout, STMG-AD can enhance the robustness and accuracy of anomaly detection. Experiments on various real-world datasets demonstrate its superiority over existing methods in detecting anomalies in irregular multivariate time series.

In recent years, the In-band Network Telemetry (INT) method with fine measurement ability has been gradually deployed in various datacenter networks. However, in real-world production practice, it is difficult to replace all devices simultaneously, and an essential requirement is to support the brownfield deployment, which means replacing the devices gradually. Considering the above situation, due to the "black switch", a nonprogrammable switch, it is difficult for existing measurement methods to independently identify and locate grey failure in time. In this paper, we propose "Brownfield Measurement" (BMM), a practical identification and localization method of grey failures in the incremental deployment environment. This method aims to integrate the INT and network tomography (NT) technology with a redesigned collaboration measurement mechanism and penetrate the advantages of INT into the "black region". At the same time, considering that the original INT methods based on the central control model are prone to controller bottlenecks due to large overhead, BMM is also designed as a method based on edge device measurement, and some tasks that originally needed to be run by the controller are delegated to the network element devices located at the edge. This not only reduces the burden on the controller, but also effectively reduces additional overheads. Finally, our experiments prove that the proposed method has good identification performance with key indicators, such as latency and packet loss rate in the brownfield deployment environment, to realize quick failure localization with a great precision rate at the edge switches.

In this paper, we propose two dependence link addition strategies to enhance the robustness of interdependent Cyber-Physical Systems. One is based on intra-degree and receiving capability difference and the other is based on intra-degree and receiving capability ratio. Numerical simulations demonstrate that the two strategies are better than adding dependence links randomly.

Summary Skyline computation is particularly useful in multi‐criteria decision‐making applications. However, it is inadequate to answer queries that need to analyze not only individual points but also groups of points. Compared to the traditional skyline computation, computing group‐based skyline is much more complicated and expensive. This computational challenge promotes us to use modern computing platforms to accelerate the computation. In this paper, we introduce a novel multi‐core algorithm to compute group‐based skyline. We first compute the skyline layers of a data set in parallel, which are a critical intermediate result. In the algorithm, we maintain an efficiently updatable data structure for the shared global skyline layers, which is used to minimize dominance tests and maintain high throughput. Then we design an efficient parallel algorithm to find group‐based skyline based on the skyline layers. Extensive experimental results on real and synthetic data sets show that our algorithms achieve 10‐fold speedup with 16 parallel threads over state‐of‐the‐art sequential algorithms on challenging workloads.

Summary Query processing over uncertain preferences is very common in real‐life situations, because many times, we cannot model users' preferences as strict partial orders. In this paper, we investigate skyline queries over uncertain preferences. The latest state‐of‐the‐art algorithm, calledUsky‐basealgorithm, makes significant advances. However, it still needs to be perfected in 2 aspects. (1) Theoretic analysis: The correctness of the algorithm is not fully verified. (2) Efficiency: Due to the heavy calculation introduced by adoptinginclusion‐exclusion principleto express the skyline probability, it needs massive time when computing skyline probabilities for large data sets. To address the above 2 concerns, we first review theUsky‐basealgorithm and lemmas it based on. Then we propose a novel parallel algorithm, calledParallel‐sky, to compute skyline probability of a given object. Moreover, we propose an adding algorithm and a deleting algorithm to deal with dynamic scenarios where new objects are added in and outdated objects are deleted out. Furthermore, we extend our algorithm from computing skyline probability of a given object to all objects in a data set. We conduct extensive experiments on real and synthetic data sets to validate the effectiveness and efficiency of our proposals.