Name
Abstract | ||
|---|---|---|
The analysis of large scale data logged from complex cyber-physical systems, such as microgrids, often entails the discovery of invariants capturing functional as well as operational relationships underlying such large systems. We describe a latent factor approach to infer invariants underlying system variables and how we can leverage these relationships to monitor a cyber-physical system. In particular we illustrate how this approach helps rapidly identify outliers during system operation. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2783258.2788605 | ACM Knowledge Discovery and Data Mining |
Keywords | Field | DocType |
Regression,Latent Factors,System Invariants,Outlier Detection | Anomaly detection,Data mining,Leverage (finance),Regression,Computer science,Outlier,Cyber-physical system,Factor regression model,Artificial intelligence,Invariant (mathematics),Machine learning | Conference |
Citations | PageRank | References |
6 | 0.55 | 20 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marjan Momtazpour | 1 | 14 | 2.45 |
| Jinghe Zhang | 2 | 6 | 0.55 |
| Saifur Rahman | 3 | 18 | 3.22 |
| Ratnesh K. Sharma | 4 | 483 | 53.37 |
| Naren Ramakrishnan | 5 | 1913 | 176.25 |