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Relaxed Functional Dependency Discovery in Heterogeneous Data Lakes

  • Rihan HaiEmail author
  • Christoph Quix
  • Dan Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11788)

Abstract

Functional dependencies are important for the definition of constraints and relationships that have to be satisfied by every database instance. Relaxed functional dependencies (RFDs) can be used for data exploration and profiling in datasets with lower data quality. In this work, we present an approach for RFD discovery in heterogeneous data lakes. More specifically, the goal of this work is to find RFDs from structured, semi-structured, and graph data. Our solution brings novelty to this problem in the following aspects: (1) We introduce a generic metamodel to the problem of RFD discovery, which allows us to define and detect RFDs for data stored in heterogeneous sources in an integrated manner. (2) We apply clustering techniques during RFD discovery for partitioning and pruning. (3) We performed an intensive evaluation with nine datasets, which shows that our approach is effective for discovering meaningful RFDs, reducing redundancy, and detecting inconsistent data.

Notes

Acknowledgements

The authors would like to thank the German Research Foundation DFG for the kind support within the Cluster of Excellence “Internet of Production” (Project ID: EXC 2023/390621612).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.RWTH Aachen UniversityAachenGermany
  2. 2.Hochschule Niederrhein, University of Applied SciencesKrefeldGermany
  3. 3.Fraunhofer Institute for Applied Information Technology FITSankt AugustinGermany

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