Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Anomaly Detection for Industrial Big Data   As the Industrial Internet of Things (IIoT) grows, systems are increasingly
being monitored by arrays of sensors returning time-series data at
ever-increasing 'volume, velocity and variety' (i.e. Industrial Big Data). An
obvious use for these data is real-time systems condition monitoring and
prognostic time to failure analysis (remaining useful life, RUL). (e.g. See
white papers by Senseye.io, and output of the NASA Prognostics Center of
Excellence (PCoE).) However, as noted by Agrawal and Choudhary 'Our ability to
collect "big data" has greatly surpassed our capability to analyze it,
underscoring the emergence of the fourth paradigm of science, which is
data-driven discovery.' In order to fully utilize the potential of Industrial
Big Data we need data-driven techniques that operate at scales that process
models cannot. Here we present a prototype technique for data-driven anomaly
detection to operate at industrial scale. The method generalizes to application
with almost any multivariate dataset based on independent ordinations of
repeated (bootstrapped) partitions of the dataset and inspection of the joint
distribution of ordinal distances.

2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph