@techreport{TD:101511,
	att_abstract={{The explosive increase in cellular network traffic, users, and applications, as well as the corresponding shifts in user ex- pectations, has created heavy needs and demands on cellular data providers. In this paper we address one such need: min- ing the logs of cellular voice and data traffic to rapidly detect network performance anomalies and other events of interest. The core challenge in solving this problem is the issue that it is impossible to predict beforehand where in the traffic the event may appear, requiring us to be able to query arbi- trary subsets of the network traffic (e.g., longer than usual round-trip times for users in a specific urban area to connect to Facebook using a particular model of phone). Since it is infeasible to store all combinations of such data, especially when it is collected in real-time, we need to be able to sum- marize the traffic data using succinct sketch data structures to answer these queries. 
The major contribution of this paper is the introduction of a scheme, called Crossroads, that can be used to compute the intersection of the measurements between two overlapping streams. For instance, in the above example, it is possible to compute the intersection of all the data going between the downtown area and Facebook with all the data generated by the model of phone to detect anomalous RTT behavior. In effect, this gives us a way to essentially “square root” the number of sketches that we need to maintain, transforming a prohibitively expensive problem to one that is tractable in practice. We provide rigorous analysis of our sketch and the trade-offs between memory footprint and accuracy. We also demonstrate the efficacy of our solution via simulation on data collected at a major cellular service carrier in the US.}},
	att_authors={jw2129, zg2325, hy312y},
	att_categories={},
	att_copyright={{}},
	att_copyright_notice={{}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:101511},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:101511_DS1_2014-10-30T14:54:04.178Z.pptx},
	author={Jia Wang and ZHenglin Yu and Zihui Ge and He Yan and jun Xu and ashwin Lall},
	institution={{ACM IMC 2014 Conference}},
	month={November},
	title={{Crossroads: A Practical Data Sketching Solution for Mining Intersection of Streams}},
	year=2014,
}