The problem of finding outliers in data has broad applications in areas as diverse as data cleaning, fraud detection, network monitoring, invasive species monitoring, etc. While there are dozens of techniques that have been proposed to solve this problem for static data collections, very simple distance-based outlier detection methods are known to be competitive or superior to more complex methods. However, distance-based methods have time and space complexities that make them impractical for streaming data and/or resource limited sensors. In this work, we show that simple data-editing techniques can make distance-based outlier detection practical for very fast streams and resource limited sensors. Our technique generalizes to produce two algorithms, which, relative to the original algorithm, can guarantee to produce no false positives, or guarantee to produce no false negatives. Our methods are independent of both data type and distance measure, and are thus broadly applicable.

Many time series data mining problems require subsequence similarity search as a subroutine. Dozens of similarity/distance measures have been proposed in the last decade and there is increasing evidence that Dynamic Time Warping (DTW) is the best measure across a wide range of domains. Given DTW's usefulness and ubiquity, there has been a large community-wide effort to mitigate its relative lethargy. Proposed speedup techniques include early abandoning strategies, lower-bound based pruning, indexing and embedding. In this work we argue that we are now close to exhausting all possible speedup from software, and that we must turn to hardware-based solutions. With this motivation, we investigate both GPU (Graphics Processing Unit) and FPGA (Field Programmable Gate Array) based acceleration of subsequence similarity search under the DTW measure. As we shall show, our novel algorithms allow GPUs to achieve two orders of magnitude speedup and FPGAs to produce four orders of magnitude speedup. We conduct detailed case studies on the classification of astronomical observations and demonstrate that our ideas allow us to tackle problems that would be untenable otherwise.