Managing and Mining Sensor Data by Charu C. Aggarwal

By Charu C. Aggarwal

Advances in know-how have bring about a capability to gather info with using numerous sensor applied sciences. particularly sensor notes became more cost-effective and extra effective, and have even been built-in into daily units of use, equivalent to cellphones. This has bring about a far better scale of applicability and mining of sensor information units. The human-centric element of sensor facts has created super possibilities in integrating social facets of sensor info assortment into the mining strategy.

Managing and Mining Sensor Data is a contributed quantity through admired leaders during this box, focusing on advanced-level scholars in desktop technological know-how as a secondary textual content booklet or reference. Practitioners and researchers operating during this box also will locate this ebook important.

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To this end, a multitude of model-based regression, transformation and filtering techniques have been proposed for approximation of sensor data streams. This section categorizes and reviews the most important model-based approaches towards compression of sensor data. These models often exploit spatio-temporal correlations within data streams to compress the data within a certain error norm; this is also known as lossy compression. Moreover, several standard orthogonal transformation methods (like, Fourier or wavelet transform) reduce the amount of storage space required by reducing the dimensionality of data.

DV(i+1)m . 6) The source does not communicate with the sink if |¯ v(i+1)j − v(i+1)j | < δ, where δ is a user-defined threshold. If this condition is not satisfied, the source communicates to the sink the smallest number of sensor values, such that the δ threshold would be satisfied. Similarly, if the sink does not receive any sensor values from the source, it computes the expected sensor values v¯(i+1)j and uses them as an approximation to the raw sensor values. If the sink receives a few sensor values form the source, then, before computing the expected values, the sink updates its dynamic probabilistic model.

As sensor values are a representation of physical processes, it is naturally possible to uncover the following properties: continuity of the sampling processes and correlations between different sampling processes. In principle, regression-based models 24 MANAGING AND MINING SENSOR DATA exploit either or both of these properties. , sensor value), and then consider the regression curves as standards over which the inferred sensor values reside. The two most popular regression-based approaches use polynomial and Chebyshev regression for cleaning sensor values.

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