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Distributed Outlier Detection using Compressive Sensing
2015
Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data - SIGMOD '15
Computing outliers and related statistical aggregation functions from large-scale big data sources is a critical operation in many cloud computing scenarios, e.g. service quality assurance, fraud detection, or novelty discovery. Such problems commonly have to be solved in a distributed environment where each node only has a local slice of the entirety of the data. To process a query on the global data, each node must transmit its local slice of data or an aggregated subset thereof to a global
doi:10.1145/2723372.2747641
dblp:conf/sigmod/YanZHSMZM15
fatcat:s2wfkvpehnefnhcdrzlynrnpde