document: Bursty and hierarchical structure in streams A fundamental problem in text data mining is to extract meaningful structure from document streams that arrive continuously over time . E-mail and news articles are two natural examples of such streams , each characterized by topics that appear , grow in intensity for a period of time , and then fade away . The published literature in a particular research field can be seen to exhibit similar phenomena over a much longer time scale . Underlying much of the text mining work in this area is the following intuitive premise -- that the appearance of a topic in a document stream is signaled by a `` burst of activity , '' with certain features rising sharply in frequency as the topic emerges . The goal of the present work is to develop a formal approach for modeling such `` bursts , '' in such a way that they can be robustly and efficiently identified , and can provide an organizational framework for analyzing the underlying content . The approach is based on modeling the stream using an infinite-state automaton , in which bursts appear naturally as state transitions ; in some ways , it can be viewed as drawing an analogy with models from queueing theory for bursty network traffic . The resulting algorithms are highly efficient , and yield a nested representation of the set of bursts that imposes a hierarchical structure on the overall stream . Experiments with e-mail and research paper archives suggest that the resulting structures have a natural meaning in terms of the content that gave rise to them .
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