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Statistical Analysis of Massive Data Streams Proceedings of a Workshop (2004) / Chapter Skim
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Pages 320-321

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
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From page 320...
... Returning to the navigation discussion, the tree structure could be leveraged again and produce one type of model aggregation where capture models would reside at each of the interior nodes of the tree in addition to the leaf nodes. These interior models create a hierarchy of models where an interior model in an upper level of the tree represents the aggregate statistics of all the child nodes below it.
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From page 321...
... Based on this abnormal event the capture models can be reconfigured with filters to focus in on only the portion of the distribution where the spike occurs, then start exporting additional information about the suspect traffic such as protocol and destination port, which will help identify the type of traffic. The ability to establish normative distributions of various characteristics of a stream and then dynamically explore deviations from the norms adds considerable analysis capability.
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Key Terms

  • capture models
  • capture model
  • subscriber usage
  • model aggregation
  • data streams

  • hash rule
  • data stream
  • massive data
  • aggregation time
  • time interval

  • call drill
  • drill forward
  • tree structure
  • data reduction
  • table data

  • usage distribution


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