Description
Semantic data management refers to approaches that focus on maintaining and using data in terms of its meaning. While there exist effective solutions for semantic data management, Big Data characteristics like volume, variety, velocity, and veracity prevent such solutions from being used on a large scale. In particular, data management tasks that require automated inferencing may be affected negatively by Big Data characteristics, and novel methods are required to address these issues efficiently.
The aim of this track is to gather researchers and developers from the Semantic Web, Databases, and Artificial Intelligence fields to discuss research issues, experiences, and results in designing, implementing, deploying, and evaluating theories, techniques, and applications related to semantic data management on Big Data sources.
Topics
Topics of interest include, but are not limited to:
- Distributed infrastructures for Semantic Data Management over Big Data sources
- Semantic Data Management Techniques for Big Data
- Query processing of Semantic Data
- Access Control and Privacy in Semantic Data
- Synchronization Models
- Semantic Data Integration and Quality Assessment
- Traceability and Trustworthiness
- Ranking of Semantic Data Semantic Data Analytics
- Storage Models for Semantic Data
- Semantic Searching and Browsing
- Management of Provenance of Semantic Data
- Semantic Data Management for Dynamic and Temporal Data
- Semantic Data Management and Polyglot Persistence
- Empirical Studies of Semantic Data Management Techniques
- Benchmarks for Semantic Data Management Techniques
- Domain-specific Semantic Data Management approaches for, e.g., life sciences, e-government, healthcare, and finance
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