Data preparation is the process of transforming data before serving them to downstream tasks, such as data analytics, data cleaning, and machine learning. Much data do not meet the requirements of the following tasks, leading users, including both expert data scientists and novice data users, to frequently conduct ad-hoc data preparation. It is reported that preparing data is both labour-intensive and tedious work, which accounts for 50%-80% of the time spent in the whole data lifecycle. 

We explore to build a data preparation framework to achieve two goals:

• Enable users to rapidly prepare data
• Enable repeatability of scientific experiments by deriving suitable data preparation specification

We propose for both metadata and preparators a respective taxonomy. We use the defined metadata and preparators to create standard specifications of data transformations. The whole taxonomies can be found here.

• Prof. Dr. Felix Naumann (Project leader)
• Lan Jiang
• Gerardo Vitagliano
• Mazhar Hameed
• Divesh Srivastava (external collaborator, AT&T Labs - Research)

Data Knoller - A systematic data preparation framework

Line Type Classification - Classify the types of lines in csv-like data files.

Line & cell type classification

We have compiled the related work corresponding to the individual research topics on data preparation. For the whole list please refer to here.

For further information on this project please contact Lan Jiang.