Description
Title: INTERPOLATION OF STREAMING DATA IN REAL TIME
Abstract: An estimator of the first derivative of the interpolated function that is more precise than those based on finite difference schemas is one of the key components of the real-time C1-continuous cubic spline interpolation of streaming data. We formally define (in closed form) two such greedy look-ahead heuristic estimators based on the calculus of variations, denoted MinBE and MinAJ2, as well as the corresponding cubic splines that they produce. Then, using a variety of numerical experiments and performance measures, they are evaluated in comparison. The results demonstrate that, for all of the evaluated performance measures, the cubic Hermite splines produced by the heuristic MinAJ2 significantly outperformed those based on finite difference schemas (including convergence). The suggested strategy is very broad. Time-series and other streams of univariate functional data can be directly affected by it. After splitting, parametrically defined multidimensional curves can also be handled. The algorithm’s streaming nature makes it useful for processing data sets that are too large to fit in memory (e.g., edge computing devices, embedded time-series databases).
Keywords: streaming algorithm, online algorithm, spline interpolation, cubic Hermite spline
Paper Quality: SCOPUS / Web of Science Level Research Paper
Paper type: Analysis Based Research Paper
Subject: Computer Science
Writer Experience: 20+ Years
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