An Alarm Correlation Algorithm Based on Similarity Distance and Deep Network

Zhao, Boxu; Luo, Guangsheng

doi:10.1007/978-3-319-42297-8_34

Cited by 1 publication

(1 citation statement)

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“…To find correlations and causalities in data sets is central in many data analysis problems. It can concern the detection and localization of faults in computer networks as well as the impact of variable changes in biochemical reactions (see for instance [ZL16] and [DMAF15]). Whereas correlation analysis focuses on establishing relationships between variables, causal analysis aims to reveal cause‐effect relationships between variables.…”

Section: Introductionmentioning

confidence: 99%

Understanding Indirect Causal Relationships in Node‐Link Graphs

Bae

Helldin

Riveiro

2017

Computer Graphics Forum

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To find correlations and cause and effect relationships in multivariate data sets is central in many data analysis problems. A common way of representing causal relations among variables is to use node‐link diagrams, where nodes depict variables and edges show relationships between them. When performing a causal analysis, analysts may be biased by the position of collected evidences, especially when they are at the top of a list. This is of crucial importance since finding a root cause or a derived effect, and searching for causal chains of inferences are essential analytic tasks when investigating causal relationships. In this paper, we examine whether sequential ordering influences understanding of indirect causal relationships and whether it improves readability of multi‐attribute causal diagrams. Moreover, we see how people reason to identify a root cause or a derived effect. The results of our design study show that sequential ordering does not play a crucial role when analyzing causal relationships, but many connections from/to a variable and higher strength/certainty values may influence the process of finding a root cause and a derived effect.

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Section: Introductionmentioning

confidence: 99%