Networkmetrics: multivariate big data analysis in the context of the internet

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. Yet, they also have their specificities, mainly their typical Big Data nature and the presence of unstructured data. Exabytes of data in the format of text, video, audio, etc. are daily generated in the Internet. Part of these data need to be analyzed for disparate purposes, ranging from the optimization of the communications to cybersecurity and cyberwalfare. We argue that exploratory multivariate analysis is, indeed, useful to tackle these problems and has a role to play in the Big Data arena, currently limited to data mining techniques and visualization analytics. In absence of a better name, we call Networkmetrics the approach of treating Internet problems from the multivariate and exploratory perspective.

Linear transformations of multivariate calibration models in near infrared spectroscopy: A comparative study

“…Table 1 illustrates the cases of the most effectivity use. 4. In all cases indicated in the table Bias is negative when compression ratios are not large.…”

“…In this context, the multivariate big data analysis (MBDA) becomes the actual PAT problem. 4 MBDA requires continuous on-line collection and storage of large data volumes from different sources and ongoing correction of the multivariate calibration models. This is a very time-consuming process.…”

Anomaly‐based exploratory analysis and detection of exploits in android mediaserver

“…In this context, multivariate techniques have shown to be promising when dealing with complex observations of diverse nature and origin [46]. However, to the best of our knowledge, very few multivariate‐based malware detection proposals are deployed in the literature at present.…”