Databases Reference
In-Depth Information
Chapter 4
Mining Data Streams
Most of the algorithms described in this topic assume that we are mining a
database. That is, all our data is available when and if we want it. In this
chapter, we shall make another assumption: data arrives in a stream or streams,
and if it is not processed immediately or stored, then it is lost forever. Moreover,
we shall assume that the data arrives so rapidly that it is not feasible to store
it all in active storage (i.e., in a conventional database), and then interact with
it at the time of our choosing.
The algorithms for processing streams each involve summarization of the
stream in some way. We shall start by considering how to make a useful sample
of a stream and how to filter a stream to eliminate most of the “undesirable”
elements. We then show how to estimate the number of different elements in
a stream using much less storage than would be required if we listed all the
elements we have seen.
Another approach to summarizing a stream is to look at only a fixed-length
“window” consisting of the last n elements for some (typically large) n. We
then query the window as if it were a relation in a database. If there are
many streams and/or n is large, we may not be able to store the entire window
for every stream, so we need to summarize even the windows. We address the
fundamental problem of maintaining an approximate count on the number of 1's
in the window of a bit stream, while using much less space than would be needed
to store the entire window itself. This technique generalizes to approximating
various kinds of sums.
4.1
The Stream Data Model
Let us begin by discussing the elements of streams and stream processing. We
explain the difference between streams and databases and the special problems
that arise when dealing with streams.
Some typical applications where the
stream model applies will be examined.
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