Our paper on data context informed data wrangling has been accepted at IEEE Big Data'17

The paper presents a methodology to fully automate an end-to-end data wrangling process incorporating data context, which associates portions of a target schema with potentially spurious extensional data of types that are commonly available. Data context, i.e. instance-based evidence, together with data profiling paves the way to inform automation in several steps within the wrangling process, specifically, matching, mapping validation, value format transformation, and data repair.