Adaptive stream resource management using Kalman Filters

By Ankur Jain, Edward Y. Chang, Yuan-fang Wang. ACM SIGMOD Conference, 2004

383 citations recorded by the source corpus.

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Approximate Data Collection in Sensor Networks using Probabilistic Models (2006, methodology): “This subsumes prior work as discussed in Section 2 [23] [15] [14] by taking advantage of predictions across both time and space.”
Online outlier detection in sensor data using non-parametric models (2006, background): “There has also been work on predicting and caching the values generated by the sensors [35, 26], which can re-”
Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases (2008, background): “Although it has been shown that it presents some flaws and limitations [7], and that finding an optimal k-anonymization is NP-hard [8], the k-anonymity model is still practically relevant and in recent years a large research effort has been devoted to develop algorithms for k-anonymity (see [9],…”
Rise and fall patterns of information diffusion: model and implications (2012, methodology): “Traditional approaches applied to data mining include Auto-Regression (AR) and variations [24], or Linear dynamical systems (LDS), Kalman filters (KF) and variants [13, 25, 26] but they are all linear methods.”
Energy conservation in wireless sensor networks: A survey (2009, methodology): “The authors of [63] exploit a similar approach, but use a Kalman filter as the core model for predictions.”