Call for Papers: Managing Data and Metadata in Complex Enterprise Landscapes

The digital transformation generates huge amounts of heterogeneous data, across the entire lifecycle of all kinds of products and services and across all kinds of businesses. Extracting insights from these data by applying data analytics and AI constitutes a critical success factor for enterprises, e.g., to optimize processes and reinvent business models. Comprehensive analytics efforts and vast amounts of data have made enterprise data landscapes far more complex revealing globally distributed, federated and hybrid deployed structures of analytical and operational data systems. This poses new challenges to both data management and metadata management: new kinds of data platforms have emerged, e.g., data lakes, data catalogs and data marketplaces, semantic techniques for managing data and metadata are increasingly becoming popular in industry practice, data governance and data strategy concepts are developed to ensure the compliant and economically beneficial use of data.

In this special issue of Datenbank-Spektrum, we call for contributions on technical and organizational aspects of data management and metadata management in complex enterprise landscapes, interpreted broadly. We welcome original contributions – including technical papers, interdisciplinary and application-oriented papers, case studies and survey papers – relating to the following areas, but not limited to:

Data platform architectures and technologies, e.g., data lakes, data catalogs, data marketplaces, feature stores

Architecting and modeling data and metadata in data platforms, e.g., semantic data modeling for data lakes and data catalogs, reference data models, data model management, data model evolution

Data engineering and metadata management for analytics and AI, e.g., for data pipelines and MLOps

Data integration and data quality in complex enterprise landscapes, e.g., federated data integration, semantic data integration, distributed data quality assessments

Enterprise data architecture: organizing data and metadata across the enterprise landscape, e.g., across several data lakes, data catalogs and operational systems

Data governance and data strategy, e.g., data ownership and data stewardship across operational and analytical systems, organizational roles for data governance and data analytics, data offense and data defense concepts.

Deadline for submissions: March 1st, 2023

Issue delivery: DASP 2/2023 (July 2023)

Guest editors:

Christoph Gröger, Robert Bosch GmbH, Stuttgart

Holger Schwarz, University of Stuttgart

Paper formats: Full Paper (8–10 pages), Short Paper (4–5 pages), double-column (cf. author guidelines at Contributions either in German or in English are welcome.

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