IEEE Symposium on Services for Data & Model Ecosystems (SDMEs)

Effectively unlocking the value of data, and being fairly compensated for data, has become the ‘holy grail’ for data technologies and the data-driven world. This includes data within an organization but also enabling seamless data flows across businesses, companies, and organisations. Data-hungry machine learning such as the training of large language models as well as a legal patchwork of regulation on data use in AI exacerbates the problem. The current practice in data management and governance in many areas or most organisations, and existing data sharing solutions, however, have significantly limited applications of data for internal and external applications crossing companies and organisations. The question on how to effectively unlock data value remains to be answered for data owners and data users alike. This necessitates more holistic research on the concept of data economy and its ecosystem, rather than narrowly focusing on a single element, such as sharing technologies, trading platforms, or marketplaces. Data ecosystems (DEs) are the future of data management that allow companies to share data and collaborate to get valuable insights
and develop value-added services.

The exploration of complex interactions within the data ecosystem is a focal point of model ecosystems (MEs) research. The integration of machine learning algorithms holds the potential to enhance adaptability and self-optimization, providing a more precise understanding of the entry and exit dynamics of businesses, companies, and organizations based on evolving data inputs. Despite its promise, the model ecosystem remains inadequately studied within the framework of current data sharing policies and privacy conditions. Key questions persist regarding how to effectively navigate the dynamics and complexities inherent in model ecosystems, and how to address privacy, security, and copyright concerns. We call for pioneer papers to address these gaps and contribute to the advancement of knowledge in this critical domain.

Services computing (SC) has been developing standards and technologies for the integration of software, applications, and collaboration of services and processes by overcoming system heterogeneity. SC technologies definitely can play a significant role in DEs, in which novel tools, best practices, solutions, and platforms will allow: (1) for organisations to transform their data into data products with governance, and privacy
protection, to extend and broaden data applications, and to enable data exchange and flow crossing organizations, domains and sectors; (2) for data consumers to search, query, explore, and combine data products for advanced data analytics and providing value-added services.

We welcome innovative contributions that further the idea of data & model ecosystems and tackle the challenges that consequently result from the complexity of data & model ecosystems.

Topics of interest

The suggested topics of interest include, but are not restricted to:

• Data resource categorisation and analysis
• From data to data-as-products design, description, and presentation/packaging
• Data transparency, provenance, and traceability in data ecosystems
• Privacy models for data product in data ecosystems
• Metadata management, semantic modelling, and enrichment in data products
• Data product pricing modelMethods and tools for data source to data product transformation
• Mechanism and tools for data product exploration, scenario generation, and composition
• Data product catalogue generation
• Data quality management and data curation in data ecosystems
• Applications and services for data management and exchange in data ecosystems
• Measurement of data use in models, both at training and at inference time
• Data Attribution in models
• Data royalty collection
• Review of Data mesh, databricks, and other relevant technologies for data ecosystems
• Machine learning and self-optimization in model ecosystems
• Privacy and security perseverance for model ecosystems
• Data sharing policies for model ecosystems
• Model ecosystems dynamics analysis
• Model management in model ecosystems
• Ethical considerations and regulatory frameworks for model ecosystems