General-Purpose Inductive Programming for Data Wrangling Automation

Lidia Contreras-Ochando, Fernando Martínez-Plumed, Cesar Ferri, Jose Hernandez-Orallo, Maria Jose Ramirez-Quintana

Data acquisition, integration, transformation, cleansing and other highly tedious tasks take a large proportion of data science projects. These routine tasks are tedious basically because they are repetitive and, hence, automatable. As a consequence, progress in the automation of this process can lead to a dramatic reduction of the cost and duration of data science projects. Recently, Inductive Programming (IP) has shown a large potential as a paradigm for addressing this automation. This short paper elaborates on the recent success of induction using domain-specific languages (DSLs) for the automation of data wrangling process and advocating for the use of inductive programming over general-purpose declarative languages (GPDLs) using domain-specific background knowledge (DSBKs).