Download Presentation here: 2018-06-04-GfZ-RainerHäner
Abstract:
Current developments in scientific infrastructures like the European Plate Observing System (EPOS) reveal a very high variety regarding the amount, the kind of data as well as a very high degree of heterogeneity concerning their processing and visualisation interfaces provided by the data suppliers. They also show a still present lack of standardisation concerning the encoding of data, metadata, and semantic information, partly due to a proliferation of domain and even application specific encodings. It is for this reason that one of the most challenging problems regarding the development of scientific infrastructures is to enable collaboration and interoperability across system and domain boundaries. Collaboration in this context not only means that information can be simply exchanged, but moreover that it can be further processed and thus easily, discovered (visualisation), utilised (analysis), and integrated (management) into existing workflows. Interoperability is the key factor in establishing scientific research environments and infrastructures, as well as in bringing together heterogeneous, geographically distributed risk management, monitoring, and early warning systems. Based on developments within EPOS, a reference architecture has been devised that comprises architectural blue-prints and interoperability models regarding the specification of business processes and logic as well as the management (e.g., encoding) of data, metadata, and semantics. The reference architecture supports analysing (Data Mining) both, static and real-time data in order to find correlations of disparate information that do not at first appear to be intuitively obvious: Analysed data (e.g., seismological monitoring) can be enhanced with relationships discovered by associating them (Data Fusion) with other data (e.g., Creepmeter monitoring), with digital models of geological structures, or with the simulation of geological processes. The presentation illustrates the utilisation of standardised statistical analysis and visualisation methods:
- Kriging (Subsurface Management)
- Tsunami simulation (Early Warning Systems)
- Prediction of tsunami arrival times
- Generation of time series for virtual and real sensor locations
- Time series analysis (Monitoring, Risk Management)
- Trend detection
- Correlation with Models and Simulation
- Spatial Cluster Analysis of earthquakes (Risk Management)
- Trend Analysis of land displacement (Risk Management)
- Neural Network Clustering of signals in borehole measurements (Virtual Research Environment)