Accurate failure identification is crucial for ensuring the reliability of microservice systems. However, existing failure identification approaches are usually based on data-driven correlation learning strategies. These approaches focus on statistical correlation in observability data and ignore the causal mechanism in microservice systems, which leads to insufficient robustness and interpretability. In this study, we propose DCGFI, a robust and interpretable failure identification approach based on multimodal observability data, which uses dual causal graphs to integrate causal modeling, data-driven strategy and knowledge driven strategy to achieve effective failure identification for microservice systems. DCGFI first constructs data causal graphs and knowledge causal graphs based on multimodal observability data. Then, DCGFI uses a causal graph convolutional network to learn data causal graphs and update knowledge causal graphs. Finally, DCGFI combines data causal graphs with knowledge causal graphs to jointly optimize the failure identification process. Experimental results on two datasets demonstrate that DCGFI outperforms all baselines on both Macro-F1 and Micro-F1 and exhibits good robustness across different datasets.

Traditional recommendation usually focuses on utilizing only one target user behavior (e.g., purchase) but ignoring other auxiliary behaviors (e.g., click, add to cart). Early efforts of multi-behavior recommendation often emphasize the differences between multiple behaviors, i.e., they aim to extract useful information by distinguishing different behaviors. However, the commonality between them, which reflects user's common preference for items associated with different behaviors, is largely ignored. Meanwhile, the multi-behavior recommendation still severely suffers from limited supervision signal issue. In this paper, we propose a novel self-supervised graph collaborative filtering model for multi-behavior recommendation named S-MBRec. Specifically, for each behavior, we execute the GCNs to learn the user and item embeddings. Then we design a supervised task, distinguishing the importance of different behaviors, to capture the differences between embeddings. Meanwhile, we propose a star-style contrastive learning task to capture the embedding commonality between target and auxiliary behaviors, so as to alleviate the sparsity of supervision signal, reduce the redundancy among auxiliary behavior, and extract the most critical information. Finally, we jointly optimize the above two tasks. Extensive experiments, in comparison with state-of-the-arts, well demonstrate the effectiveness of S-MBRec, where the maximum improvement can reach to 20%.

The smart home brings together devices, the cloud, data, and people to make home living more comfortable and safer. Trigger-action programming enables users to connect smart devices using if-this-then-that (IFTTT)-style rules. With the increasing number of devices in smart home systems, multiple running rules that act on actuators in contradictory ways may cause unexpected and unpredictable interference problems, which can put residents and their belongings at risk. Previous studies have considered explicit interference problems related to multiple rules targeting a single actuator, whereas implicit interference (interference across different actuators) detection is still challenging and not yet well studied owing to the effort-intensive and time-consuming annotation work of obtaining device information. The lack of knowledge about devices is a critical reason that affects the accuracy and efficiency in implicit interference detection. In this article, we propose A3ID, an automatic detection method for implicit interference based on knowledge graphs. Using natural language processing (NLP) techniques and a lexical database, A3ID can extract knowledge of devices from a knowledge graph, including functionality, effect, and scope. Then, it analyzes and detects interferences among the different devices semantically in three steps, without human intervention. Furthermore, it provides user-friendly explanations in a well-designed structure to specify possible reasons for the implicit interference problems. Our experiment on 11 859 IFTTT-style rules shows that A3ID outperforms state-of-the-art methods by more than 33% in the F1-score for the detection of implicit interference. Moreover, evaluations on an extended data set for devices from ConceptNet (a knowledge graph) and five smart home systems suggest that A3ID also has favorable performance with other devices not limited to the smart home domain.

How to apply Web achievements to the Business Process Simulation (BPS) to compensate for the deficiencies of existing simulation technology and improve the ability of BPS is facing a major problem. Based on simulation technology development trend, this article proposed a BPS platform which is service-oriented and SAAS-based. With this platform, the BPS as a web service on the Internet that provides simulation services for tenants to customize simulation service. This paper put forward to two kinds of simulation methods for simulation engine: User-Instance Simulation and Process-Instance Simulation, and propose a superposition of two kinds of simulation methods called Interaction Simulation method (ISM), this simulation method horizontal and vertical viewing angle of a Business Process execution makes the simulation results more realistic and credible. Based on this method we propose a set of complete simulation model derived from the process definition model. Finally, we design and implement a prototype system.

The process sub-model is the core of workflow model, verifying process sub-model before deployment can reduce the cost of shutdown, inspection and repair at run-time. This paper proposes verification algorithms, which focus on structural errors of process sub-model, and those algorithms will use the characteristics of the extended Petri net discussed in this paper. Simultaneously, this paper explains how to design a XPDL Validator based on SaaS and it emphasis on mapping XPDL process definition to extended Petri net.