Yixin Diao, Hani Jamjoom and David Loewenstern
IEEE International Conference on Cloud Computing
Bangalore, India, September 2009
Abstract. Problem management is a critical and expensive element for
delivering IT service management and touches various
levels of managed IT infrastructure. While problem
management has been mostly reactive, recent work is
studying how to leverage large problem ticket
information from similar IT infrastructures to
probatively predict the onset of problems. Because
of the sheer size and complexity of problem tickets,
supervised learning algorithms have been the method
of choice for problem ticket classification, relying
on labeled (or pre-classified) tickets from one
managed infrastructure to automatically create
signatures for similar infrastructures. However,
where there are insufficient preclassified data,
leveraging human expertise to develop classification
rules can be more efficient. In this paper, we
describe a rule-based crowdsourcing approach, where
experts can author classification rules and a social
networkingbased platform (called xPad) is used to
socialize and execute these rules by large
practitioner communities. Using real data sets from
several large IT delivery centers, we demonstrate
that this approach balances between two key
criteria: accuracy and cost effectiveness.
Bibtex.
@inproceedings{jamjoom-ICCC-09,
author = {Yixin and Diao and Hani and Jamjoom and David and Loewenstern},
title = {{Rule-Based Problem Classification in IT Service Management}},
booktitle = {IEEE International Conference on Cloud Computing},
address = {Bangalore, India},
month = {September},
year = {2009}
}