Since big data have imposed a paradigm change in the way data are stored, managed, and queried, information systems have evolved into complex data platforms or data ecosystems supporting data-intensive storage, computation, and analysis of data with heterogeneous structures. Yet, a smart and comprehensive support for data scientists and architects to govern the data through the whole life-cycle is still lacking.

Data management support in data platforms requires the collection of a wide set of metadata capturing the distinguishing features of the data; this enables advanced functionalities spanning from research and data profiling to provenance control, orchestration of data transformation pipelines, incremental data integration, and efficient querying. The challenges begin with the management of metadata itself in terms of modeling effort, storage, complexity of retrieval activities, and effective exploitation. Besides addressing the Vs of big data, the enabled functionalities must cope with the heterogeneity of storage and computation engines – that include DBMSs supporting multiple data models and cloud storage systems with limited control and predictability – while meeting suitability requirements for less-skilled users.

This workshop calls for researchers and practitioners to propose innovative solutions to address the aforementioned challenges, by welcoming papers that contribute to the advancement of data platforms in optimizing and simplifying the different aspects of data and metadata management and fruition.

The scope of the workshop includes, but is not limited to the following topics.

• Metadata modeling for data platforms
• Techniques for metadata discovery and management
• Advanced search, exploration, and profiling of data and metadata
• Semantic enrichment of metadata
• Data governance
• Data wrangling
• Provenance and data versioning control
• Orchestration and optimization of data transformation pipelines
• Data integration and querying in multimodel databases, multistores, polystores
• Query processing, optimization, and performance
• Entity resolution and data fusion
• Big data management and querying
• Artificial Intelligence solutions for data platforms
• AutoML techniques
• Cloud computing and architectures
• Advanced architectures for data lakes and data platforms
• Analysis, design, implementation, and testing of data platforms
• Case studies and project experiences

[NEW 02.03.22] Program published.

[NEW 22.12.21] Submission deadline has been extended to January 20, 2022.

[NEW 10.12.21] Submission deadline has been extended to December 22, 2021.

[NEW 03.12.21] ACM Proceedings Format updated.

[NEW 02.11.21] Authors of the best papers will be invited to submit an extended version to a Special Issue with Elsevier's Future Generation Computer Systems (FGCS) journal (IF: 7.187).

Submissions should present original results and substantial new work not currently under review or published elsewhere. DataPlat 2022 will follow a single-blind review process to evaluate submissions on the basis of originality, relevance, quality, and technical contribution. The following submissions are accepted:

• Regular and short research papers (up to 10 and 5 pages, respectively)
• Vision papers (up to 5 pages)
• Application papers (up to 5 pages)

Papers must be submitted via Easychair, in PDF according to the EDBT Proceedings Format.

DataPlat 2022 Submission Site on EasyChair

Accepted papers will be published online at CEUR. Workshop papers must follow the ACM Proceedings Format, appropriately modified for EDBT/ICDT 2022 workshops (available here). Please follow all instructions available here. DataPlat uses single-blind reviewing, which means that the authors should list their names and affiliations as part of their submission.

All accepted papers are expected to be presented at the workshop, and at least one author is required to register.