About
Mission
Semantic data management is a paradigm, a common name for a range of techniques for manipulating and using data based on its meaning. It is about specification, interpretation, and exploitation of the semantics of the data for:
- more efficient, faster and cheaper data integration
- data access, regardless the physical location and representation
- more comprehensive data analysis and interlinking
- flexible querying against multi-schema datasets
- interlinking of text and Web content with structured data
- hybrid information retrieval techniques
- easier data exploration and understanding
Semantic data management enables sustainable solutions for a range of IT environments, where the use of today's mainstream technology is either inefficient or entirely unfeasible: enterprise data integration, life science research, data sharing in SaaS architectures, querying linked data on the Web. In a nutshell, semantic data management fosters the economy of knowledge, facilitating more comprehensive usage of larger scale and more complex datasets at lower cost.
SemData represents a series of events and initiatives aiming to facilitate the development and adaption of semantic data management concepts, standards, tools, and practices.
Organizers
Jointly organised by the projects LarKC and SOA4All, as well as the NoE PlanetData in cooperation with STI International.
Topics
Distribution, interoperability, and benchmarking: “classical” semantic storage issues such as distributed repositories (data partitioning, replication, and federation); interoperability and integration with RDBMS; performance evaluation and benchmarking
Virtualized semantic repositories: identification and composition of (fragments of) datasets in manners that abstract the applications from the specific setup of the data management service (e.g., local vs. remote, federation and distribution)
Semantic data bus: a communication layer bridging the gap between the data layer and the application layer (e.g., service infrastructures or buses, reasoning platforms such as the LarKC plug-in infrastructure)
Embedded data processing: “move the logic close to the data” mechanisms, allowing application-specific data processing to be performed within the engine, e.g., stored procedures and extension APIs
Adaptive indexing and multi-modal retrieval: strategies for dynamic materialization towards specific data- and query-patterns; indexing structures for specific types of data and queries (FTS, co-occurrence, concordance, temporal, spatial)