@techreport{TD:100295,
	att_abstract={{Operational network data, management data such as customer
care call logs and equipment system logs, is a very
important source of information for network operators to detect
problems in their networks. Unfortunately, there is lack
of efficient tools to automatically track and detect anomalous
events on operational data, causing ISP operators to rely
on manual inspection of this data. While anomaly detection
has been widely studied in the context of network data, operational
data presents several new challenges, including the
volatility and sparseness of data, and the need to perform fast
detection (complicating application of schemes that require
offline processing or large/stable data sets to converge).
To address these challenges, we propose Tiresias, an automated
approach to locating anomalous events on hierarchical
operational data. Tiresias leverages the hierarchical structure
of operational data to identify high-impact aggregates
(e.g., locations in the network, failure modes) likely to be associated
with anomalous events. To accommodate different
kinds of operational network data, Tiresias consists of an online
detection algorithm with low time and space complexity,
while preserving high detection accuracy. We present results
from two case studies using operational data collected at a
large commercial IPTV network operated by a Tier-1 ISP:
customer care calls log and set-top box crashes log. By comparing
with a reference set verified by the ISP�s operational
group, we validate that Tiresias can achieve > 94% accuracy
in locating anomalies. Tiresias also discovers several previously
unknown anomalies in the ISP�s customer care cases,
demonstrating its effectiveness.}},
	att_authors={nd1321, jw2129},
	att_categories={},
	att_copyright={{IEEE}},
	att_copyright_notice={{This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in 2012. {{, 2012-06-18}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:100295},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:100295_DS1_2011-05-17T02:04:31.037Z.pdf},
	author={Nicholas Duffield and Jia Wang and Chi-yao Hong and Matthew Caesar},
	institution={{IEEE ICDCS 2012}},
	month={June},
	title={{Tiresias: Online Anomaly Detection for Hierarchical Operational Network Data}},
	year=2012,
}