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• W2778865947 abstract "Collaborative Filtering algorithms are frequently employed in e-commerce. However, this kind of algorithms can also be useful in other domains. In an information system thousands of bytes are sent through the network every second. Analyzing this data can require too much time and many resources, but it is necessary for ensuring the right operation of the network. Results are used for profiling, security analysis, traffic engineering and many other purposes. Nowadays, as a complement to a deep inspection of the data, it is more and more common to monitor packet flows, since it consumes less resources and it allows to react faster to any network situation. In a typical ow monitoring system, flows are exported to a collector, which stores the information before being analyzed. However, many collectors work based on time slots, so they do not analyze the flows when they are just received, generating a delay. In this work we demonstrate how Collaborative Filtering algorithms can be applied to this new domain. In particular, using information about past flows, these algorithms can anticipate future flows before being captured. This way, time required for detecting and responding to different network situations is reduced." @default.
• W2778865947 created "2018-01-05" @default.
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• W2778865947 date "2017-12-01" @default.
• W2778865947 modified "2023-09-23" @default.
• W2778865947 title "Advancing Network Flow Information Using Collaborative Filtering" @default.
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• W2778865947 doi "https://doi.org/10.1142/s021848851740013x" @default.
• W2778865947 hasPublicationYear "2017" @default.
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