Paper Info
Title
Finding anomalies in time-series using visual correlation for interactive root cause analysis
Abstract
Monitoring computer networks often includes gathering vast amounts of time-series data from thousands of computer systems and network devices. Threshold alerting is easy to accomplish with state-of-the-art technologies. However, to find correlations and similar behaviors between the different devices is challenging. We developed a visual analytics application to tackle this challenge by integrating similarity models and analytics combined with well-known, but task-adapted, time-series visualizations. We show in a case study, how this system can be used to visually identify correlations and anomalies in large data sets and identify and investigate security-related events.
Year
DOI
Venue
2013
10.1145/2517957.2517966
VizSEC
Keywords
Field
DocType
interactive root cause analysis,computer system,visual analytics application,time-series data,visual correlation,large data set,case study,monitoring computer network,different device,time-series visualization,network device,security-related event,time series,visual analytics,network security,correlation
Data mining,Data set,Computer science,Root cause analysis,Networking hardware,Network security,Visual analytics,Correlation,Artificial intelligence,Analytics,Machine learning
Conference
Citations 
PageRank 
References 
11
0.65
12
Authors
3
Name
Order
Citations
PageRank
Florian Stoffel11069.38
Fabian Fischer219912.94
Daniel A. Keim377041141.60