DATAPLAT 2022

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.

• 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