Abstract: A computer-implemented method for sensing streaming data comprises recursively sampling an input stream of data using overlapping windowing to obtain at least one previous measurement regarding the input data stream, and employing the at least one previous measurement for obtaining a subsequent measurement.

Abstract: A method for data mining on compressed data vectors by a certain metric being expressible as a function of the Euclidean distance is suggested. In a first step, for each compressed data vector, positions and values of such coefficients having the largest energy in the compressed data vector are stored. In a second step, for each compressed data vector, the coefficients having not the largest energy in the compressed data vector are discarded. In a third step, for each compressed data vector, a compression error is determined in dependence on the discarded coefficients in the compressed data vector. In a fourth step, at least one of an upper and a lower bound for the certain metric is retrieved in dependence on the stored positions and the stored values of the coefficients having the largest energy and the determined compression errors.

Abstract: A computer-implemented method for sensing streaming data comprises recursively sampling an input stream of data using overlapping windowing to obtain at least one previous measurement regarding the input data stream, and employing the at least one previous measurement for obtaining a subsequent measurement.

Abstract: A method for data mining on compressed data vectors by a certain metric being expressible as a function of the Euclidean distance is suggested. In a first step, for each compressed data vector, positions and values of such coefficients having the largest energy in the compressed data vector are stored. In a second step, for each compressed data vector, the coefficients having not the largest energy in the compressed data vector are discarded. In a third step, for each compressed data vector, a compression error is determined in dependence on the discarded coefficients in the compressed data vector. In a fourth step, at least one of an upper and a lower bound for the certain metric is retrieved in dependence on the stored positions and the stored values of the coefficients having the largest energy and the determined compression errors.

Abstract: The invention notably relates to a computer-implemented method for compressing data. The data is partitioned into clusters of pieces of data resulting from K-means clustering. Each cluster has a centroid. The method comprises applying (S10) a compression scheme to the data. The compression scheme preserves the centroid of each cluster and reduces the variance of each cluster. The method also comprises rescaling (S20) the data by moving the pieces of data towards the centroid of their cluster. Such a method improves the compression of data partitioned into clusters.