Statistical Analysis of Massive Data Streams Proceedings of a Workshop (2004) / Chapter Skim
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Pages 299-307
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From page 299... ...
narticularIv telephony, mobile, satellite and so forth. Now, the network service providers, as ~ am sure you know, have some very serious challenges.
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Economic storage and management of the Internet usage data is a severe problem. Of course, they want the information right away and, of course, it has to scale.
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At least, that is very common in the service providers. It is a fraction of the actual traffic, but as a fraction, it stays about the same.
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It is these three things together here, encapsulator, rule engine, and a distributed data store. In a large installation, you can have scores to hundreds of these, and the whole idea is putting a lot of intelligence right un close to the source of this high speed streaming data.
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So, the service provider knows it is a legitimate describer, as well as the usage information coming from the router itself. What we call them is normalized metered events.
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Inside any of these capture models you have a - inside there is a matrix where you have a number of different variables that you can track. If you are doing binning, then the other axis is the bins.
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Fraud is only when someone is using your account, because you are still paying. Then, abuse is basically violation of the end user agreement that you signed when you signed up with the service provider.
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So, some of the paradigm shifts that ~ find are challenging when ~ talk to service providers is really, the knee jerk reaction is, oh, ~ want to store everything, and it is just prohibitively expensive. T find that T have to be a business consultant and not just a technologist when talking to people.
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From page 307... ...
~ would say most of our -- in fact, our current cleployment that we are working on is a very large sized service provider in Canada.
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