Working topics of GeoBI initiative, the open community making open source Location Intelligence adoption pervasive
The following list includes the topics on which the activities of the initiative are based. It is periodically revised by the Technical Committee. Some activities, coming from the contribution of the initiative members, are listed in the Roadmap.
UI and interactivity
By visualizing information directly on the map, spatial analysis allows users to quickly identify patterns, trends or critical areas, with an effectiveness that would otherwise be impossible through traditional reporting systems.As a matter of fact, the base techniques that are commonly used to visually represent information directly on a map are superior to all other types of traditional analytics, when the analyzed data can be someway spatially located. However, there is still a lot of space to further improve these techniques. The following list shows the possible areas of improvements in User Interfaces of Location Intelligence (LI) applications:
- map thematization strategies
- in-memory analysis filters
- map customization
- searching and browsing within a map
- sharing & cooperation
Basic LI engines
Analytical engines are usually divided into two families: basic and advanced. Basic analytical engines are the most used in BI projects and are on their way to become commodity in the BI world. Reports, OLAP analysis and dashboards are usually considered to be the clearest example of basic analytical engines.Location Intelligence applications must extend these engines in order to create a brand new family of spatial-enabled basic analytical engines, composed of the following key engines:
- Spatial Report
- Spatial OLAP
- Spatial Dashboard
Advanced LI Engines
Advanced analytical engines are the ones on which BI Suites compete in order to differentiate their value proposition and create competitive advantage in the BI market. Examples of advanced analytical engines are ad-hoc querying and reporting, mashboards, Data Mining and Planning/What-If. Location Intelligence applications must also work on these more advanced analytical engines, in order to create a brand new family of spatial-enabled engines, which are composed as follows:- spatial ad-hoc querying & reporting
- spatial mashboards
- spatial mining
- spatial planning/what-if
Real-time Location Intelligence
Real-time Business Intelligence can be usefully applied to Location Intelligence applications, for example to analyze data streams on the fly that come from sensors spread on a particular territory.In order to realize Real-time Location Intelligence applications, the following issues must be faced:
- Extending CEP, in order to process events not only over different time windows but also over different spatial boundaries
- Extending the notification system, in order to filter alarms by their location in space as well
- Creating spatial-enabled monitoring consoles.
Open Standards
In order to keep the TCO of Location Intelligence applications low, it's important to define open and shared standards so as to integrate the different key components that compose the whole application. Some standards that can be used in Location Intelligence applications have already been defined by the Open Geospatial Consortium (OGC), while other ones can still be defined and submitted to them.Infrastructure and Metadata
LI engines are only the top of the iceberg. In order to create Location Intelligence applications, a complex back-end infrastructure capable of integrating BI and GIS must be realized. This infrastructure and the metadata that allow its different key components to work together is one of the first issues to face in order to create robust and scalable Location Intelligence applications.Here is a list of infrastructural components that need to be carefully analysed:
- Different types of LI architectures (costs and scalability)
- Spatial-enabled DB (performance and limitations)
- Spatial ETL
- Spatial-enabled metadata repository
- Spatial CMS
- Spatial data security (profiled/role-based access)
Knowledge dissemination
In order to disseminate BI knowledge in the GIS world and vice-versa, breaking down barriers that still exist between these two worlds, the initiative will provide new contents shared with the whole community:- Articles (whitepapers, tutorials, benchmarks)
- Presentations (webinars, events)
- Wiki
- Forum
