HydroZIP: How Hydrological Knowledge Can Be Used to Improve Compression of Hydrological Data

Provided by: EPFL
Topic: Data Management
Format: PDF
From algorithmic information theory, which connects the information content of a data set to the shortest computer program that can produce it, it is known that there are strong analogies between compression, knowledge, inference and prediction. The more the authors know about a data generating process, the better they can predict and compress the data. A model that is inferred from data should ideally be a compact description of those data. In theory, this means that hydrological knowledge could be incorporated into compression algorithms to more efficiently compress hydrological data and to outperform general purpose compression algorithms.

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