@techreport{TD:100552,
	att_abstract={{Despite advances in measurement technology, it is
still challenging to reliably compile large-scale network datasets.
For example, because of flaws in the measurement systems or
difficulties posed by the measurement problem itself, missing,
ambiguous or indirect data are common when dealing with
real-world networks. In the case where such data have spatio-temporal
structure, it is natural to try to leverage this structure
to deal with the challenges posed by the problematic nature of the
data. Our work involving network datasets draws on ideas from
the area of compressive sensing and matrix completion, where
sparsity is exploited in estimating quantities of interest. However,
the standard results on compressive sensing are (i) reliant on
conditions which generally don�t hold for network datasets, and
(ii) don�t allow us to exploit all we know about their spatio-temporal
structure. In this paper we overcome these limitations
with an algorithm that has at its heart the same ideas espoused
in compressive sensing, but adapted to the problem of network
datasets. We show how this algorithm can be used in a variety
of ways, in particular on traffic data, to solve problems such as
simple interpolation of missing values, traffic matrix inference
from link data, prediction, and anomaly detection. The elegance
of the approach lies in the fact that it unifies all of these tasks,
and allows them to be performed even when as much as 98% of
the data is missing.}},
	att_authors={ww9241},
	att_categories={C_CCF.1, C_CCF.8, C_NSS.9},
	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 IEEE/ACM Transactions on Networking. {{, Volume 20}}{{, Issue 3}}{{, 2012-06-01}}{{, 10.1109/TNET.2011.2169424}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={traffic matrix, matrix completion, compressive sensing, missing data problem, traffic matrix inference, anomaly detection },
	att_techdoc={true},
	att_techdoc_key={TD:100552},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:100552_DS1_2011-06-03T15:21:40.951Z.pdf},
	author={Matthew Roughan, University of Adelaide and Yin Zhang and Walter Willinger and Lili Qiu},
	institution={{IEEE/ACM Transactions on Networking}},
	month={June},
	title={{Spatio-Temporal Compressive Sensing and Internet Traffic Matrices (Extended Version)}},
	year=2012,
}