Our group includes PostDocs, PhD students, and student assistants, and is headed by Prof. Felix Naumann. If you are interested in joining our team, please contact Felix Naumann.

For bachelor students we offer German lectures on database systems in addition to paper- or project-oriented seminars. Within a one-year bachelor project, students finalize their studies in cooperation with external partners. For master students we offer courses on information integration, data profiling, and information retrieval enhanced by specialized seminars, master projects and we advise master theses.

Most of our research is conducted in the context of larger research projects, in collaboration across students, across groups, and across universities. We strive to make available most of our datasets and source code.

Please do not hesitate to reach out directly to us, if you cannot find a paper, slides, or other research artifacts.

Gerardo Vitagliano

PhD Student

Prof.-Dr.-Helmert-Straße 2-3
D-14482 Potsdam

Phone: +49 331 5509 427
Room: F-2.05

Email: Gerardo Vitagliano

Research: ResearchGate, GitHub

As a Ph.D. student at the Information Systems Group and member of the HPI Research School, my research interests are on data preparation and information extraction.

I am most active in the research projects of the Data Preparation group, currently focusing on benchmarking data loading and detecting layout templates in multiregion data files.

Feel free to contact me for collaboration, thesis proposals, or anything closely or loosely related to these research interests:

Research Interests

Structural Data Preparation
Data Pollution
Representation Learning
Layout Inference in multiregion files

Projects

Pollock: A benchmark for CSV data loading.
Mondrian: An approach for automatic recognition of layout templates in multiregion files.

Publications

G. Vitagliano, L. Jiang, M. Hameed, F. Naumann: Mondrian: Spreadsheet Layout Detection. In preparation
G. Vitagliano, L. Jiang, M. Hameed, F. Naumann: Pollock: A Data Loading Benchmark. In preparation
M. Hameed, G. Vitagliano, L. Jiang, F. Naumann: SURAGH: Syntactic Pattern Matching to Identify Ill-Formed Records. Proceedings of the International Conference on Extending Database Technology (EDBT) - submitted, 2022
L. Jiang, G. Vitagliano, M. Hameed, F. Naumann: Aggregation Detection in CSV Files. Proceedings of the International Conference on Extending Database Technology (EDBT) - submitted, 2022
G. Vitagliano, L. Jiang, F. Naumann: Detecting Layout Templates in Complex Multiregion Files. PVLDB. Accepted (2021).
L. Jiang, G. Vitagliano, F. Naumann: Structure Detection in Verbose CSV Files. Proceedings of the International Conference on Extending Database Technology (EDBT), 2021
L. Jiang, G. Vitagliano, F. Naumann: A Scoring-based Approach for Data Preparator Suggestion. Lernen, Wissen, Daten, Analysen (LWDA), 2019

Teaching

Table Recognition. Seminar, Summer Semester 2021
Data Preparation for Science. Seminar, Winter Semester 2018/19

Chair

Prof. Dr. Felix Naumann

Information Systems

E-Mail: felix.naumann(at)hpi.de

Assistant: Anna Zobel

Office: Campus II, House F, F-2.01
Tel.: +49 (0)331 5509-280
Fax: +49 (0)331 5509-287
E-Mail: office-naumann(at)hpi.de

To visit us, please see these directions.

News

16.08.2022 | Lan Jiang defended his dissertation successfully!

> Go to article

05.08.2022 | Full paper accepted at CIKM 2022

We are excited to announce that our paper "Discovering Fine-Grained Semantics in Knowledge Graph Relations" has been … > Go to article

28.07.2022 | Tim Repke defended his dissertation successfully!

> Go to article

19.07.2022 | Paper accepted at VLDB 2022

We are excited to announce that our paper Frost: A Platform for Benchmarking and Exploring Data Matching Results has … > Go to article

04.07.2022 | Paper on NER for Artworks accepted for the Semantic Web Journal

We are excited to announce that our paper on the Generation of Training Data for Named Entity Recognition of Artworks … > Go to article

Project highlights

Metanome: Big Data Profiling

Data Preparation

Janus: Change exploration

KITQAR: AI and Data Quality