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

• Strudel - Structure detection in verbose CSV files
• AggreCol - Aggregation detection in Verbose CSV files
• Mondrian - Detecting layout templates in complex multiregion files
• Pollock - A data loading benchmark                                    
• MaGRiTTE - Learning structural embeddings of data files
• Survey -  Data preparation from industry perspective: A survey
• Suragh - Detecting ill-formed Records in CSV Files
• Tasheeh - Cleaning ill-formed Records in CSV Files

• Mazhar Hameed, Gerardo Vitagliano, Fabian Panse, Felix  Naumann: TASHEEH: Repairing Row-Structure in Raw CSV Files. Proceedings of the International Conference on Extending Database Technology (EDBT), 2024
• Mazhar Hameed, Gerardo Vitagliano, Felix Naumann: MORPHER: Structural Transformation of ill-formed Rows. Proceedings of the International Conference on Information and Knowledge Management (CIKM), 2023
• Gerardo Vitagliano, Mazhar Hameed, Lucas Reisener, Lan Jiang, Eugene Wu, Felix Naumann: Pollock: A Data Loading Benchmark. Proceedings of the VLDB Endowment (PVLDB), 2023.
• Gerardo Vitagliano, Mazhar Hameed, Felix Naumann: Structural embedding of data files with MaGRiTTE. Table Representation Learning Workshop at NeurIPS (TRL@NIPS), 2022.
• Gerardo Vitagliano, Lucas Reisener, Lan Jiang, Mazhar Hameed, Felix Naumann: Mondrian: Spreadsheet Layout Detection. Proceedings of the International Conference on Management of Data (SIGMOD), 2022.
• Lan Jiang, Gerardo Vitagliano, Mazhar Hameed, Felix Naumann: Aggregation Detection in CSV Files. Proceedings of the International Conference on Extending Database Technology (EDBT), 2022
• Mazhar Hameed, Gerardo Vitagliano, Lan Jiang, Felix Naumann: SURAGH: Syntactic Pattern Matching to Identify Ill-Formed Records. Proceedings of the International Conference on Extending Database Technology (EDBT), 2022.
• Gerardo Vitagliano, Lan Jiang, Felix Naumann: Detecting Layout Templates in Complex Multiregion Files. Proceedings of the VLDB Endowment (PVLDB), 2022
  [Paper]  [ACM] 
• Lan Jiang, Gerardo Vitagliano, Felix Naumann: Structure Detection in Verbose CSV Files. Proceedings of the International Conference on Extending Database Technology (EDBT), 2021
  [Paper]  [GitHub] 
• Mazhar Hameed, Felix Naumann: Data Preparation: A Survey of Commercial Tools. SIGMOD Record 49:(3), 2020
  [Paper]  [ACM] 
• Koumarelas, Ioannis, Lan Jiang, and Felix Naumann. Data Preparation for Duplicate Detection. Journal of Data and Information Quality (JDIQ) 12, no. 3 (2020): 1–24.
• Lan Jiang, Gerardo Vitagliano, Felix Naumann: A Scoring-based Approach for Data Preparator Suggestion. Lernen, Wissen, Daten, Analysen (LWDA), 2019
  [Paper] 

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 fell free to contact us: Felix Naumann, Lan Jiang, Gerardo Vitagliano, Mazhar Hameed.