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In this paper, we will argue that, while Big Data enthusiasts imply that the analysis of massive data sets can produce serendipitous (that is, unexpected and fortunate) discoveries, the way those models are currently designed not only does not create serendipity so easily but also frequently generates zemblanitous (that is, expected and unfortunate) findings. [ABSTRACT FROM AUTHOR]

In this paper we investigate if sentences presented as the result of the application of statistical models and artificial intelligence to large volumes of data - the so-called 'Big Data' - can be characterized as semantically true, or as quasi-true, or even if such sentences can only be characterized as probably quasifalse and, in a certain way, post-true; that is, if, in the context of Big Data, the representation of a data domain can be configured as a total structure, or as a partial structure provided with a set of sentences assumed to be true, or if such representation cannot be configured as a partial structure provided with a set of sentences assumed to be true. [ABSTRACT FROM AUTHOR]