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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/27754
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| Title: | Telecommunications Network Diagnosis |
| Authors: | Danyluk, Andrea
Provost, Foster
Carr, Brian |
| Issue Date: | 7-Nov-2008 |
| Series/Report no.: | CeDER-PP-2002-01 |
| Abstract: | The Scrubber 3 system monitors problems in the local loop of the
telephone network, making automated decisions on tens of millions of
cases a year, many of which lead to automated actions. Scrubber saves
Bell Atlantic millions of dollars annually, by reducing the number of
inappropriate technician dispatches. Scrubber's core knowledge base, the
Trouble Isolation Module (TIM), is a probability estimation tree
constructed via several data mining processes. TIM currently is deployed
in the Delphi system, which serves knowledge to multiple applications.
As compared to previous approaches, TIM is more general, more robust,
and easier to update when the network or user requirements change. Under
certain circumstances it also provides better classifications. In fact,
TIM's knowledge is general enough that it now serves a second deployed
application. One of the most interesting aspects of the construction of
TIM is that data mining was used not only in the traditional sense,
namely, building a model from a warehouse of actual historical cases.
Data mining also was used to produce an understandable model of the
knowledge contained in an earlier, successful diagnostic system. |
| URI: | http://hdl.handle.net/2451/27754 |
| Appears in Collections: | CeDER Published Papers
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