Enabling Multi-process Discovery on Graph Databases

Eldin, Ali Nour; Assy, Nour; Kobeissi, Meriana; Baudot, Jonathan; Gaaloul, Walid

doi:10.1007/978-3-031-17834-4_7

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…3. Data processing [16,17]: According to the four layers defined by the data model and the constraints between objects, information extraction and entity alignment are completed based on the multi-source data of the access module. In the process of data fusion, potential relationships are mined through intelligent algorithms for some missing relationships.…”

Section: Dynamic Operation and Maintenance Map Constructionmentioning

confidence: 99%

Fault Location Method Based on Dynamic Operation and Maintenance Map and Common Alarm Points Analysis

Wu,

Guan

2024

Algorithms

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Under a distributed information system, the scale of various operational components such as applications, operating systems, databases, servers, and networks is immense, with intricate access relationships. The silo effect of each professional is prominent, and the linkage mechanism is insufficient, making it difficult to locate the infrastructure components that cause exceptions under a particular application. Current research only plays a role in local scenarios, and its accuracy and generalization are still very limited. This paper proposes a novel fault location method based on dynamic operation maps and alarm common point analysis. During the fault period, various alarm entities are associated with dynamic operation maps, and alarm common points are obtained based on graph search addressing methods, covering deployment relationship common points, connection common points (physical and logical), and access flow common points. This method, compared with knowledge graph approaches, eliminates the complex process of knowledge graph construction, making it more concise and efficient. Furthermore, in contrast to indicator correlation analysis methods, this approach supplements with configuration correlation information, resulting in more precise positioning. Through practical validation, its fault hit rate exceeds 82%, which is significantly better than the existing main methods.

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Section: Dynamic Operation and Maintenance Map Constructionmentioning

confidence: 99%