1. The WISE public website First protoype WISE viewer Stefan Jensen EEA project manager on WISE WISE end user workshop Brussels
The access limitations etc. Is handled in the WISE portal user administration.
The resulting flash application(s) made available at the client PC will be dependent on the user credentials.

2. The WISE public web site
Public interface to water related information
Providing single gateway to European datasets (spatial and non-spatial) with water related content
WISE viewer is key part of the service
Portal and other services tbd.
Target Group
Experts (EU, Member countries)
Informed Public
You should explain some basics about the user and find some feedback for items of discussion, such as; ‘What will be the final Target Group’ The system as we see it now will allow for the Informed Public, but also caters for more advanced users

3. Prototype WISE public viewer
Advantages of a WISE viewer
Combining disparate datasets gives a new perspective
E.g. looking at nitrate levels in River Basin District rather than country
In future providing a gateway to related reports and information
Examples of using the WISE public viewer:
Here you can cover some initial advantages of such a viewer. There are advantages in combining disparate data sets, also if the viewer allows the user to find links to more information on datasets this is very useful. There are many more points you can discuss here, such as the linking to analytical servers

4. Searching for Copenhagen and showing EIONET water stations and classified land use
Search information. The WISE viewer might give you several different ways of searching information and jump to that location.

5. CCM and WFD data combined around the gulf of Helsinki
You should note that it is simply a viewer with limited analytical functionality. If queries were to be made between different servers on big datasets, this will slow the service down and probably frustrate the user.
Also the Viewer is totally dependent on the quality of the data supplied by the members of state. Expertise is needed on these datasets to pull the maximum value from them. Therefore close collaboration will be needed between the different parties involved.

6. CCM, WFD and EIONET water information mapped on Sicily
You should note that it is simply a viewer with limited analytical functionality. If queries were to be made between different servers on big datasets, this will slow the service down and probably frustrate the user.
Also the Viewer is totally dependent on the quality of the data supplied by the members of state. Expertise is needed on these datasets to pull the maximum value from them. Therefore close collaboration will be needed between the different parties involved.

7. UWWTD data in the south of France
You should note that it is simply a viewer with limited analytical functionality. If queries were to be made between different servers on big datasets, this will slow the service down and probably frustrate the user.
Also the Viewer is totally dependent on the quality of the data supplied by the members of state. Expertise is needed on these datasets to pull the maximum value from them. Therefore close collaboration will be needed between the different parties involved.

8. UWWTD data in the north of England
You should note that it is simply a viewer with limited analytical functionality. If queries were to be made between different servers on big datasets, this will slow the service down and probably frustrate the user.
Also the Viewer is totally dependent on the quality of the data supplied by the members of state. Expertise is needed on these datasets to pull the maximum value from them. Therefore close collaboration will be needed between the different parties involved.

9. UWWTD, CCM, EIONET Water and Corine landcover in a sub-region of Northern England
You should note that it is simply a viewer with limited analytical functionality. If queries were to be made between different servers on big datasets, this will slow the service down and probably frustrate the user.
Also the Viewer is totally dependent on the quality of the data supplied by the members of state. Expertise is needed on these datasets to pull the maximum value from them. Therefore close collaboration will be needed between the different parties involved.

10. Selecting and querying of EIONET water station data around Hull (Northern England)
You should note that it is simply a viewer with limited analytical functionality. If queries were to be made between different servers on big datasets, this will slow the service down and probably frustrate the user.
Also the Viewer is totally dependent on the quality of the data supplied by the members of state. Expertise is needed on these datasets to pull the maximum value from them. Therefore close collaboration will be needed between the different parties involved.

11. Some current WISE Viewer limits
Limitations
Viewer and not a GIS tool
Size of data packets
Areas for further works
Data quality and availability
Expert knowledge of available data
You should note that it is simply a viewer with limited analytical functionality. If queries were to be made between different servers on big datasets, this will slow the service down and probably frustrate the user.
Also the Viewer is totally dependent on the quality of the data supplied by the members of state. Expertise is needed on these datasets to pull the maximum value from them. Therefore close collaboration will be needed between the different parties involved.

12. Future options to be build in 2006 and beyond
You should note that it is simply a viewer with limited analytical functionality. If queries were to be made between different servers on big datasets, this will slow the service down and probably frustrate the user.
Also the Viewer is totally dependent on the quality of the data supplied by the members of state. Expertise is needed on these datasets to pull the maximum value from them. Therefore close collaboration will be needed between the different parties involved.

13. Future ‘Gateway’ functionality – links to data sets
An example of how Analytical services such as XMLA (XML for analytical services) can be linked to a WISE viewer.
Both services use very different technologies but can be brought together in a portal/gateway concept.

14. Future ‘Gateway’ functionality – semantic links
Here you can see the functionality where the EEA ALISS server will be integrated into the viewer to provide the user the option to discover more information on layers and topics he / she browse. In this example the user switched on the Intercalibration layer, the viewer then picks up on the keyword ‘Intercalibration’ and searches for relevant topics on that keyword. Links to these are then provided in a popup window

15. Future options: Selection land cover classes and using business graphics on DB entries
We could allow to set filters on top layers so users can eliminate unessesary information. This examples show how we select only a number of the landcover classes to show urban sprawl.

16. Harmonisation issues: CCM catchments and river-segments, Teleatlas rivers, EIONET water
You should note that it is simply a viewer with limited analytical functionality. If queries were to be made between different servers on big datasets, this will slow the service down and probably frustrate the user.
Also the Viewer is totally dependent on the quality of the data supplied by the members of state. Expertise is needed on these datasets to pull the maximum value from them. Therefore close collaboration will be needed between the different parties involved.

17. Entry page to the WISE public web site

18. Access under http://dataservice.eea.eu.int/wise
Viewer options
Explain that more than one viewer can be hooked up here

19. The WISE web site acts as a portal to other mapping services
Services Page
Explain that the viewer is based on background services – these being BOTH ESRI AXL and WMS which allows anybody to connect to it with any technology of their choice. Thus the JRC could connect to the WISE data easily without a viewer and more importantly experts can connect with a bespoke GIS platform such as ArcInfo to do expert queries.

20. Next steps Issues for input Viewer functionality
Methodological questions on data modelling
Collecting user input
- Commenting on WISE viewer
Responding to a formalised questionnaire in January
You should explain some basics about the user and find some feedback for items of discussion, such as; ‘What will be the final Target Group’ The system as we see it now will allow for the Informed Public, but also caters for more advanced users

21. Thank you! for your attention and future cooperation on behalf of the people working on WISE
Layers can be grouped in different ways. These layers can come from different map services and be grouped by a simple XML file. Multi linguality is one of the options.