Selecting representative metrics datasets for efficient detection of anomalous data
US10200393B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 29, 2018 |
| Grant date | Feb 5, 2019 |
| Priority date | — |
| Expiry date | May 29, 2038 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L63/1416
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
Certain embodiments involve selecting metrics that are representative of large metrics datasets and that are usable for efficiently performing anomaly detection. For example, metrics datasets are grouped into clusters based on, for each of the clusters, a similarity of data values in a respective pair of datasets from the metrics datasets. Principal component datasets are determined for the clusters. A principal component dataset for a cluster includes a linear combination of a subset of metrics datasets included in the cluster. Each representative metric is selected based on the metrics dataset having a highest contribution to a principal component dataset in the cluster into which the metrics dataset is grouped. An anomaly detection is executed in a manner that is restricted to a subset of the metrics datasets corresponding to the representative metrics.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.