10,688 research outputs found
A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets
The term "outlier" can generally be defined as an observation that is significantly different from
the other values in a data set. The outliers may be instances of error or indicate events. The
task of outlier detection aims at identifying such outliers in order to improve the analysis of
data and further discover interesting and useful knowledge about unusual events within numerous
applications domains. In this paper, we report on contemporary unsupervised outlier detection
techniques for multiple types of data sets and provide a comprehensive taxonomy framework and
two decision trees to select the most suitable technique based on data set. Furthermore, we
highlight the advantages, disadvantages and performance issues of each class of outlier detection
techniques under this taxonomy framework
Increasing resilience of ATM networks using traffic monitoring and automated anomaly analysis
Systematic network monitoring can be the cornerstone for
the dependable operation of safety-critical distributed
systems. In this paper, we present our vision for informed
anomaly detection through network monitoring and
resilience measurements to increase the operators'
visibility of ATM communication networks. We raise the
question of how to determine the optimal level of
automation in this safety-critical context, and we present a
novel passive network monitoring system that can reveal
network utilisation trends and traffic patterns in diverse
timescales. Using network measurements, we derive
resilience metrics and visualisations to enhance the
operators' knowledge of the network and traffic behaviour,
and allow for network planning and provisioning based on
informed what-if analysis
An Exploratory Study of Field Failures
Field failures, that is, failures caused by faults that escape the testing
phase leading to failures in the field, are unavoidable. Improving verification
and validation activities before deployment can identify and timely remove many
but not all faults, and users may still experience a number of annoying
problems while using their software systems. This paper investigates the nature
of field failures, to understand to what extent further improving in-house
verification and validation activities can reduce the number of failures in the
field, and frames the need of new approaches that operate in the field. We
report the results of the analysis of the bug reports of five applications
belonging to three different ecosystems, propose a taxonomy of field failures,
and discuss the reasons why failures belonging to the identified classes cannot
be detected at design time but shall be addressed at runtime. We observe that
many faults (70%) are intrinsically hard to detect at design-time
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