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Provides an overview of existing work in explanation support for data-driven processes. The book classifies explainability requirements across three dimensions: the target of the explanation ('What'), the audience of the explanation ('Who'), and the purpose of the explanation ('Why').
By explaining how the result of an operation was derived from its inputs, data provenance has proven to be a useful tool that is applicable in a wide variety of applications. This monograph gives a comprehensive introduction to data provenance concepts, algorithms and methodology developed in the last few decades.
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