Networkmetrics: Multivariate Big Data Analysis in the Context of the Internet
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José Camacho; Roberto Magán-Carrión; Pedro García-Teodoro; James J. Treinen
- Abstract:
- Multivariate problems are found in all areas of knowledge. In chemistry and related
disciplines, the chemometric community was developed in a joint effort to understand and
solve problems mainly from a multivariate and exploratory perspective. This perspective is,
indeed, of broader applicability, even in areas of knowledge far from chemistry. In this paper,
we focus on the Internet: the net of devices that allow an interconnected world where all types
of data can be shared and unprecedented communication services can be provided. Problems
in the Internet, or in general in networking, are not very different from chemometric problems.
Building on this parallelism, we review four classes of problems in networking: estimation,
anomaly detection, optimization and classification. We present an illustrative set of problems
and show how a multivariate perspective may lead to significant improvements from stateof-the-art techniques. In absence of a better name we call the approach of treating these
problems from that multivariate perspective networkmetrics. Networkmetric problems have
their own specificities, mainly their typical Big Data nature and the presence of unstructured
data. We argue that multivariate analysis is, indeed, useful to tackle these specificities.
- Research areas:
- Year:
- 2016
- Type of Publication:
- Article
- Keywords:
- Multivariate analysis; Networking; Networkmetrics; Big Data
- Journal:
- Submitted to Journal of Chemometrics (Wiley)
- Pages:
- 45
- Month:
- March
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