02185nas a2200265   4500000000100000000000100001008004100002260001200043653002100055653002400076653000900100653002400109100002400133700001500157700001700172700001800189700002300207700002500230245005500255856008100310300001200391490000600403520149600409022001401905       2022                            d  c06/202210aBurst Processing10aData Pre-processing10aJava10aPipeline Frameworks1 aMaría Novo-Lourés1 aYeray Lage1 aReyes Pavón1 aRosalía Laza1 aDavid Ruano-Ordás1 aJosé Ramón Méndez00aImproving Pipelining Tools for Pre-processing Data  uhttps://www.ijimai.org/journal/sites/default/files/2022-05/ijimai_7_4_19.pdf  a214-2240 v73 aThe last several years have seen the emergence of data mining and its transformation into a powerful tool that adds value to business and research. Data mining makes it possible to explore and find unseen connections between variables and facts observed in different domains, helping us to better understand reality. The programming methods and frameworks used to analyse data have evolved over time. Currently, the use of pipelining schemes is the most reliable way of analysing data and due to this, several important companies are currently offering this kind of services. Moreover, several frameworks compatible with different programming languages are available for the development of computational pipelines and many research studies have addressed the optimization of data processing speed. However, as this study shows, the presence of early error detection techniques and developer support mechanisms is very limited in these frameworks. In this context, this study introduces different improvements, such as the design of different types of constraints for the early detection of errors, the creation of functions to facilitate debugging of concrete tasks included in a pipeline, the invalidation of erroneous instances and/or the introduction of the burst-processing scheme. Adding these functionalities, we developed Big Data Pipelining for Java (BDP4J, https://github.com/sing-group/bdp4j), a fully functional new pipelining framework that shows the potential of these features.  a1989-1660