Water Environmental Hub (WEHUB) - The Water and Environmental Hub (WEHUB) project is a cloud-based, open source web platform that’s making it easier for individuals and organizations to find, access, and share water and environmental data. What differentiates our approach from many others is our scalable and robust, cloud-based IT platform and web-based applications that support a wide variety of datasets and international data standards. This allows the WEHUB to provide open and transparent access to many different types of environmental datasets from many different public and private sector data providers. The WEHUB platform’s scalable and robust architecture supports a distributed approach to data management and sharing. The WEHUB allows any user or group to easily search, access and share water and environmental data from multiple providers and sources through a single web-based window. The WEHUB’s advanced “translator” feature allows it to pull data on-demand from a growing variety of different data sources, and present this data to users in a unified, standardized and consistent format. The WEHUB’s comprehensive and searchable data catalogue directly connects users with a requested dataset, and a full metadata profile. Users can easily add their own data to the WEHUB’s catalogue, selectively share their collection of data, comment on other datasets, or build mobile and web-based applications using the WEHUB’s API toolkit. The vision of WEHUB is to make all water and environmental data open and accessible via the web, freeing resources previously consumed by searching for, and acquiring, those data. This collaborative approach will enable new water perspectives while accelerating water research, integrated management, innovation and economic diversification.
Using the advanced search tool we often get results first that cover very broad extents or temporal ranges. Eventually we need to improve this behavior by adding ranking logic. We've dealt with this in the past a couple ways:
1) On the complicated slow end we used generalized features and an overlay algorithm to come up with an exact ranking. This was cached so wasn't so bad... but not the best way. We did this right of PostGIS querys.
2) Simpler end would be to take all possible results and rank by narrowest first. So take the delta of lat and delta of long and the sort accordingly. Likewise with time. If you search for january 1st-2003 and you find many datasets, by ranking datasets with the narrowest time range first you are more likely to find temporally relevant data.
alxjalxj
commented
13 years ago 
kayakwest
commented
13 years ago
Using the advanced search tool we often get results first that cover very broad extents or temporal ranges. Eventually we need to improve this behavior by adding ranking logic. We've dealt with this in the past a couple ways: 1) On the complicated slow end we used generalized features and an overlay algorithm to come up with an exact ranking. This was cached so wasn't so bad... but not the best way. We did this right of PostGIS querys. 2) Simpler end would be to take all possible results and rank by narrowest first. So take the delta of lat and delta of long and the sort accordingly. Likewise with time. If you search for january 1st-2003 and you find many datasets, by ranking datasets with the narrowest time range first you are more likely to find temporally relevant data.