Abstract
In this paper, we present a compression framework, for molecular dynamics (MD) simulation data, which yields significant performance by combining the strength of principal component analysis (PCA) and discrete cosine transform (DCT). Though it is a lossy compression technique, the effect on analytics performed on decompressed data is very minimal. Compression ratio up to 13 is achieved with acceptable errors in results of analytical functions.
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Kumar, A., Zhu, X., Tu, YC., Pandit, S. (2013). Compression in Molecular Simulation Datasets. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_4
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DOI: https://doi.org/10.1007/978-3-642-42057-3_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42056-6
Online ISBN: 978-3-642-42057-3
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