英语翻译是有关数据仓库与数据挖掘的 再申明一次啊 Searching and Mining Trillions of T

英语翻译
是有关数据仓库与数据挖掘的 再申明一次啊
Searching and Mining Trillions of Time Series
Subsequences under Dynamic Time Warping
ABSTRACT
Most time series data mining algorithms use similarity search as a
core subroutine,and thus the time taken for similarity search is the
bottleneck for virtually all time series data mining algorithms.The
difficulty of scaling search to large datasets largely explains why
most academic work on time series data mining has plateaued at
considering a few millions of time series objects,while much of
industry and science sits on billions of time series objects waiting
to be explored.In this work we show that by using a combination
of four novel ideas we can search and mine truly massive time
series for the first time.We demonstrate the following extremely
unintuitive fact; in large datasets we can exactly search under
DTW much more quickly than the current state-of-the-art
Euclidean distance search algorithms.We demonstrate our work on
the largest set of time series experiments ever attempted.In
particular,the largest dataset we consider is larger than the
combined size of all of the time series datasets considered in all data
mining papers ever published.We show that our ideas allow us to
solve higher-level time series data mining problem such as motif
discovery and clustering at scales that would otherwise be
untenable.In addition to mining massive datasets,we will show
that our ideas also have implications for real-time monitoring of
data streams,allowing us to handle much faster arrival rates
and/or use cheaper and lower powered devices than are currently
possible.