Long Beach, CA, USA
Mar. 1, 2010 to Mar. 6, 2010
Luca Foschini , Department of Computer Science, UC Santa Barbara, CA 93106-5110, USA
Sorabh Gandhi , Department of Computer Science, UC Santa Barbara, CA 93106-5110, USA
In this paper, we present an abstract framework for online approximation of time-series data that yields a unified set of algorithms for several popular models: data streams, amnesic approximation, and out-of-order stream approximation. Our framework essentially develops a popular greedy method of bucket-merging into a more generic form, for which we can prove space-quality approximation bounds. When specialized to piecewise linear bucket approximations and commonly used error metrics, such as L<inf>2</inf> or L<inf>∞</inf>, our framework leads to provable error bounds where none were known before, offers new results, or yields simpler and unified algorithms. The conceptual simplicity of our scheme translates into highly practical implementations, as borne out in our simulation studies: the algorithms produce near-optimal approximations, require very small memory footprints, and run extremely fast.
Luca Foschini, Sorabh Gandhi, "Space-efficient online approximation of time series data: Streams, amnesia, and out-of-order", ICDE, 2010, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2010, pp. 924-935, doi:10.1109/ICDE.2010.5447930