Scope of Research Topics for the Workshop：

• Contextualizing vendor data by defining intended use-specific validity and consistency checks.

• Reconciling differences and cross-linking data from multiple data vendors.

• Algorithms and approaches for spotting outliers and inconsistent data.

• Statistical and mathematical models for deriving missing data.

• Deterministic and probabilistic approaches to detecting duplicate data.

• Data quality metrics and trustworthiness.

• Maintaining data validity and consistency across recent and older datasets.

• Data quality role in high-level semantic frameworks such as schema.org

• Data quality issues in Knowledge Graph driven semantic search.

• Data quality improvements through visual analytics.

• Data quality aspects that contribute to gender bias in machine learning.

• Data quality issues in application domains including but not limited to sensor data streams, linked data, data integration, scientific workflows, machine learning for natural language understanding, Internet of Things (IoT), prediction models in empirical software engineering, team software process frameworks, cyber-physical systems, assisted living systems, citizen science, and drug databases.