Abstract
G-SDAM is a semantic data mediation middleware for Grids which allows the integration
of multiple data sources using different data structures and semantics. While traditional data
integration requires all data sources to follow common rules concerning the structure of data (and
even more, requires the users to have knowledge about the structure of each single data source to be
accessed), the G-SDAM approach allows a seamless and transparent view on distributed data. Local
data sources as well as users accessing those data can both be part of Virtual Organizations since
G-SDAM is planned to build upon the Globus Toolkit.
The enabling element of the middleware is ontologymanagement including mapping between different
data models (e.g. relational models, hierarchical file systems, and also ontologies itself) to a global
OWL-based ontology database residing on a Global Repository Node (GRN). The GRN acts as the
gateway to the distributed data sources. Queries can be composed in SPARQL using global semantics
which can be seen as a common language. Without an agreement on facts and terms, seamless data
integration would not be possible. For each application domain there will be different global semantics
defined by domain experts. Ontology versioning support and further requirements concerning
security, copyright issues, data provenance, etc. are also addressed by the G-SDAM approach.
Original language | English |
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Title of host publication | Austrian Grid Symposium 2006 |
Publisher | OCG |
Number of pages | 14 |
Publication status | Published - 2006 |
Fields of science
- 102001 Artificial intelligence
- 102006 Computer supported cooperative work (CSCW)
- 102010 Database systems
- 102014 Information design
- 102015 Information systems
- 102016 IT security
- 102028 Knowledge engineering
- 102019 Machine learning
- 102022 Software development
- 102025 Distributed systems
- 502007 E-commerce
- 505002 Data protection
- 506002 E-government
- 509018 Knowledge management
- 202007 Computer integrated manufacturing (CIM)
- 102033 Data mining
- 102035 Data science