  Data preprocessing techniques are devoted to correct or alleviate errors in
data. Discretization and feature selection are two of the most extended data
preprocessing techniques. Although we can find many proposals for static Big
Data preprocessing, there is little research devoted to the continuous Big Data
problem. Apache Flink is a recent and novel Big Data framework, following the
MapReduce paradigm, focused on distributed stream and batch data processing. In
this paper we propose a data stream library for Big Data preprocessing, named
DPASF, under Apache Flink. We have implemented six of the most popular data
preprocessing algorithms, three for discretization and the rest for feature
selection. The algorithms have been tested using two Big Data datasets.
Experimental results show that preprocessing can not only reduce the size of
the data, but to maintain or even improve the original accuracy in a short
time. DPASF contains useful algorithms when dealing with Big Data data streams.
The preprocessing algorithms included in the library are able to tackle Big
Datasets efficiently and to correct imperfections in the data.
