Large-scale internet services aim to remain highly available and responsive in the presence
of unexpected failures. Providing this service often requires monitoring and analyzing tens of …

We consider the sparse Fourier transform problem: given a complex vector x of length n, and
a parameter k, estimate the k largest (in magnitude) coefficients of the Fourier transform of x …

Time series shapelets are small, local patterns in a time series that are highly predictive of a
class and are thus very useful features for building classifiers and for certain visualization …

We consider the problem of computing the k-sparse approximation to the discrete Fourier
transform of an n-dimensional signal. We show: An O (k log n)-time randomized algorithm for …

Time series clustering has become an increasingly important research topic over the past
decade. Most existing methods for time series clustering rely on distances calculated from …

The last decade has seen a flurry of research on all-pairs-similarity-search (or similarity
joins) for text, DNA and a handful of other datatypes, and these systems have been applied …

Time series motifs are repeated patterns in long and noisy time series. Motifs are typically
used to understand the dynamics of the source because repeated patterns with high …

In the explosive growth of time-series data (TSD), the scale of TSD suggests that the scale
and capability of many Internet of Things (IoT)-based applications has already been …

Many modern applications produce massive amounts of data series that need to be
analyzed, requiring efficient similarity search operations. However, the state-of-the-art data …

Data series similarity search is a core operation for several data series analysis applications
across many different domains. However, the state-of-the-art techniques fail to deliver the …