As a single-task classification method, support matrix machine (SMM) is widely used in mechanical equipment fault diagnosis. However, when diagnosing multi-objective tasks, it is difficult to fully utilize the common information between multiple tasks, resulting in limited information contained in the model. Meanwhile, SMM is extremely sensitive to abnormal samples, which is not conducive to model construction. To address the aforementioned issues, a novel multiple restriction cross-border matrix machine (MRCBMM) is proposed. In MRCBMM, a weighted constraint group (WCG) is firstly designed to adjust the influence of different abnormal samples on the hyperplane, thereby determining the optimal position of the hyperplane. Meanwhile, MRCBMM defines a matrix kernel expansion (MKE) that maps matrix samples to high-dimensional space to fully utilize the structural information of the original signal. In addition, to achieve cross-border diagnosis of MRCBMM, a regularized multi-task learning framework is constructed to complete the features and parameters. Two sets of multi-objective rotating mechanical fault datasets are used for validation, and the results show that MRCBMM improves diagnostic accuracy by approximately 2% in scenarios containing abnormal samples compared with existing methods, and consistently achieves over 98% accuracy on clean datasets.

Mechanical fault diagnosis faces significant challenges due to the susceptibility of traditional matrix classifiers to abnormal samples and their inability to leverage cross domain information. To address these limitations, this paper introduces a novel fuzzy cross domain matrix machine (FCDMM), which integrates fuzzy membership modeling with multi-task learning to enhance robustness and enable knowledge transfer across related tasks. FCDMM constructs fuzzy-based decision hyperplanes and designs adaptive boundary factors to mitigate the influence of outliers. Extensive experiments on multiple domain fault datasets demonstrate that FCDMM achieves remarkable accuracy rates of 98.86 % for bearing faults and 99.57 % for gear faults, significantly outperforming state-of-the-art methods.

The microservice architecture has been commonly adopted by large scale software systems exemplified by a wide range of online services. Service monitoring through anomaly detection and root cause analysis (RCA) is crucial for these microservice systems to provide stable and continued services. However, compared with monolithic systems, software systems based on the layered microservice architecture are inherently complex and commonly involve entities at different levels of granularity. Therefore, for effective service monitoring, these systems have a special requirement of multi-granular RCA. Furthermore, as a large proportion of anomalies in microservice systems pertain to problematic code, to timely troubleshoot these anomalies, these systems have another special requirement of RCA at the finest code-level. Microservice systems rely on telemetry data to perform service monitoring and RCA of service anomalies. The majority of existing RCA approaches are only based on a single type of telemetry data and as a result can only support uni-granular RCA at either application-level or service-level. Although there are attempts to combine metric and tracing data in RCA, their objective is to improve RCA's efficiency or accuracy rather than to support multi-granular RCA. In this article, we propose a new RCA solutionTrinityRCLthat is able to localize the root causes of anomalies at multiple levels of granularity including application-level, service-level, host-level, and metric-level, with the unique capability of code-level localization by harnessing all three types of telemetry data to construct a causal graph representing the intricate, dynamic, and nondeterministic relationships among the various entities related to the anomalies. By implementing and deployingTrinityRCLin a real production environment, we evaluateTrinityRCLagainst two baseline methods and the results show thatTrinityRCLhas a significant performance advantage in terms of accuracy at the same level of granularity with comparable efficiency and is particularly effective to support large-scale systems with massive telemetry data.

Spatial queries such as range query and kNN query in road networks have received a growing number of attention in real life. Considering the large population of the users and the high overhead of network distance computation, it is extremely important to guarantee the efficiency and scalability of query processing. Motivated by the scalable and secure properties of wireless broadcast model, this paper presents an air index called Network Partition Index (NPI) to support efficient spatial query processing in road networks via wireless broadcast. The main idea is to partition the road network into a number of regions and then build the index to carry some pre-computation information of each region. We also propose multiple client-side algorithms to facilitate the processing of different spatial queries such as kNN query, range query and CNN query. A comprehensive experimental study has been conducted to demonstrate the efficiency of our scheme.

Aggregate nearest neighbor query, which returns a common interesting point that minimizes the aggregate distance for a given query point set, is one of the most important operations in spatial databases and their application domains. This paper addresses the problem of finding the aggregate nearest neighbor for a merged set that consists of the given query point set and multiple points needed to be selected from a candidate set, which we name as merged aggregate nearest neighbor(MANN) query. This paper proposes an effective algorithm to process MANN query in road networks based on our pruning strategies. Extensive experiments are conducted to examine the behaviors of the solutions and the overall experiments show that our strategies to minimize the response time are effective and achieve several orders of magnitude speedup compared with the baseline methods.

Due to the proliferation of Location-Based Service and popularity of online geo-tagged web pages, spatial keyword search has attracted significant attention from both academic and industrial communities. In this paper, we study the problem of finding the nearest aggregate point from multiple query points travelling through a set objects described by a given set of keywords, as well as the optimal routes from the query points to the aggregate point. This problem is defined as the Aggregate Keyword Routing (AKR) Query. We devise an exact algorithm to AKR query based on ellipse pruning. Next we propose an efficient approximate algorithm for AKR: Center Based Assignment (CBA). The performance of the proposed algorithms are evaluated with real data, the results demonstrate the efficiency and the effectiveness.

The ACM SIGSPATIAL Cup 2012 is about map matching, a problem of correctly matching a sequence of GPS sampling points to the roads on a digital map. This paper describes one of the winning submissions of the competition. The approach applies multi-threading technology to map matching in order to reduce running time and we propose an improvement to the Hidden Markov Model (HMM) map matching algorithm.

Wireless broadcast provides a scalable and secure spatial data dissemination approach for geographical applications in wireless mobile environments. Among various location-based services, the shortest path query on road networks is one of the most popular and essential services in our daily life. In this paper, we propose an energy-efficient scheme for on air shortest path query processing on road networks, which leverages an elaborate air index called BagIndex based upon the novel Hilbert-based heuristic tree decomposition for the road networks. Experimental results show that the proposed approach incurs less energy consumption on both communication and computation than the previous schemes.