The theme: "Integration and Data Management Systems" focuses on the development of technologies and means for integration of distributed heterogeneous simulation systems and data sources within the decision-making workflows.
The theme provides natural scientists and decision makers with interactive tools for the construction and execution of decision-making workflows enabling them also to visualize and evaluate different policy interventions.
- 1 Requirements engineering -
Validation, completion, analysis and management of requirements
- 2 GLOWA Volta DSS Infrastructure -
Development of tools for integration of distributed resources and execution of decision-making workflows
- 3 GLOWA Volta DSS Workbench -
Development of tools for remote data access, building and monitoring of decision-making workflows
- 4 Data Management and GIS -
Building of a GLOWA Volta data stock, IT-consulting within the project
- GLOWA Volta Grid-based Infrastructure -
- Analytical Visualization Framework (AVF) -
- Decision-making workbench and client interface
- Framework for visual data processing, exploration and knowledge discovery
» More about AVF
- GLOWA Volta Data Service and Data Management Framework (DMF) -
- Well structured data service in the institutional level
- Framework for organizing assessment, storage, access and use of data
» More about DMF
- Geoportal -
- Web-based easy interactive access to the GLOWA Volta data stock
- Visual exploration and search facilities of maps and data
- Enable exchange of geoscientific information
» Direct Link to Geoportal
Management of natural resources is a complex task raising questions touching many different disciplines (biology, hydrology, etc.), which cannot be solved within a single application. Therefore, a scientifically sound DSS to be developed in the project has to integrate several scientific simulation systems and data sources. The integration of the GLOWA Volta resources is complicated by the fact that data sources and simulation systems are heterogeneous and highly distributed across institutional and national boundaries. The straightforward solution to merge the models to one monolitic system is not realistic because of the high complexity and heterogeneity of the models, which are often written in different programming languages for specific operating systems that run on special hardware platforms, such as Linux clusters or MS-Windows PCs. Furthermore, the implementations of the models require more and more computational power as well as data storage capacity.
One approach that promises to satisfy all these requirements is the new paradigm of distributed computing called Grid computing. The main vision of Grid computing is to realize a unified interface for arbitrary computational resources – in-cluding hardware, software and data – that everybody can use without having to care about the hardware infrastructure and the implementation details of the software components; just as easy as getting electricity through a standardized plug from the electric power grid. While it is unsure if this overall vision will ever be accomplished, it can be said that the Grid computing hype is coming to maturity and that important parts of the Grid computing technology have reached production status.
Person in charge: Dr. Serge Shumilov