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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So, you can parallel them, going to different applications, or you can put them into sort of sequential themes for more sophisticated rule processing. So, the work that ~ have been doing has developed what ~ call capture models.
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These terms mean different things, but in the service provider industry, fraud and theft are different. Theft is when they are losing money.
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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... ...
So, it is 80SL cable and we did one backbone, media backbone service provider -- well, they dealt with commercial clients. So, they had a few thousands, but very large pipes.
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