Indexing and Mining Scientific Data: Beyond One Billion Data Series
Themis Palpanas (Trento)
Info
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Date |
13th March 2012 (week , Hilary Term 2012) |
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Time |
11:30 |
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Place |
147 |
Abstract
There is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to
index and mine very large collections of data series. Examples of such applications come from astronomy, biology, the web,
and other domains. It is not unusual for these applications to involve numbers of data series in the order of hundreds of
millions to billions. However, all relevant techniques that have been proposed in the literature so far have not considered
any data collections much larger than one-million data series.
In this paper, we describe iSAX 2.0 and its improvements,
iSAX 2.0 Clustered and iSAX2+, three methods designed for indexing and mining truly massive collections of data series.
We show that the main bottleneck in mining such massive datasets is the time taken to build the index, and we thus introduce
a novel bulk loading mechanism, the first of this kind specifically tailored to a data series index.
We show
how our methods allows mining on datasets that would otherwise be completely untenable, including the first published experiments
to index one billion data series, and experiments in mining massive data from domains as diverse as entomology, DNA and web-scale
image collections.
Further info
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Related series |
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