Data Wrangling in Database Systems: Purging of Dirty Data

Data Wrangling for Big Data: Towards a Lingua Franca for Data Wrangling

Wrangling Galaxy’s reference data

UNLABELLED The Galaxy platform has developed into a fully featured collaborative workbench, with goals of inherently capturing provenance to enable reproducible data analysis, and of making it straightforward to run one's own server. However, many Galaxy platform tools rely on the presence of reference data, such as alignment indexes, to function efficiently. Until now, the building of this cac...

Wrangling categorical data in R

Data wrangling is a critical foundation of data science, and wrangling of categorical data is an important component of this process. However, categorical data can introduce unique issues in data wrangling, particularly in real-world settings with collaborators and periodically-updated dynamic data. This paper discusses common problems arising from categorical variable transformations in R, dem...

Towards Automated Relational Data Wrangling

It is well-known in data science that 80% of the work is devoted to preprocessing and only 20% to the actual machine learning or data mining step. This motivates us to explore different ways to (help) automate that preprocessing step. This note focusses on the question whether it is possible to (help) automate the data wrangling process for tabular data in data science.

Data Wrangling: Making data useful again

Data analysis has become an everyday business and advancements of data management routines open up new opportunities. Nevertheless, transforming and assembling newly acquired data into a suitable form remains tedious. It is often stated, that data cleaning is a critical part of the overall process, but also consumes sublime amounts of time and resources. Data Wrangling is not only about transfo...