Long Beach, CA, USA
Mar. 1, 2010 to Mar. 6, 2010
Julie Letchner , Computer Science&Engineering Department, University of Washington, Seattle, USA
Christopher Re , Department of Computer Sciences, University of Wisconsin, Madison, USA
Magdalena Balazinska , Computer Science&Engineering Department, University of Washington, Seattle, USA
Matthai Philipose , Intel Research Seattle, Washington, USA
A large amount of the world's data is both sequential and imprecise. Such data is commonly modeled as Markovian streams; examples include words/sentences inferred from raw audio signals, or discrete location sequences inferred from RFID or GPS data. The rich semantics and large volumes of these streams make them difficult to query efficiently. In this paper, we study the effects—on both efficiency and accuracy—of two common stream approximations. Through experiments on a realworld RFID data set, we identify conditions under which these approximations can improve performance by several orders of magnitude, with only minimal effects on query results. We also identify cases when the full rich semantics are necessary.
Julie Letchner, Christopher Re, Magdalena Balazinska, Matthai Philipose, "Approximation trade-offs in Markovian stream processing: An empirical study", ICDE, 2010, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2010, pp. 936-939, doi:10.1109/ICDE.2010.5447926