Data Lake

Data lakes have been proposed as a solution to deal with the heterogeneity of big data, as they should provide a storage system for any kind of raw data. Metadata is of particular importance in such a system to have information about the structure and semantics of the data. The group has been working on the enhancement of the data lake system Constance. It is based on a modular architecture with components for schema matching, schema mapping, query rewriting, and wrapping of data sources. This system is applied in other research projects, e.g., mi-Mappa, HUMIT, and charMAnt, as basic platform for data integration.

Publications

2017

• Christoph Quix, Sandra Geisler, Rihan Hai, Sanchit Alekh

  Ontology Matching for Patent Classification

  Published in 12th International Workshop on Ontology Matching (OM-2017), co-located with the 16th International Semantic Web Conference (ISWC-2017), Vienna, Austria, October 21, 2017.

2016

• Rihan Hai, Sandra Geisler, Christoph Quix

  Constance: An Intelligent Data Lake System

  Published in In Proceedings of the 2016 International Conference on Management of Data (SIGMOD), pp. 2097-2100. ACM.

• Rihan Hai

  Towards an Intelligent Data Lake System for Heterogeneous Data Sources

  Published in 35th International Conference on Conceptual Modeling (ER), PhD Symposium

• Christoph Quix, Rihan Hai, Ivan Vatov

  GEMMS: A Generic and Extensible Metadata Management System for Data Lakes

  Published in Proceedings of the CAiSE'16 Forum, at the 28th International Conference on Advanced Information Systems Engineering (CAiSE 2016), Ljubljana, Slovenia, June 13-17, 2016.

• Christoph Quix, Rihan Hai

  Metadata Extraction and Management in Data Lakes with GEMMS

  Published in Complex Systems Informatics and Modeling Quarterly, No. 9, pp. 67-83, 2016