1. An Open Source GIS Architecture Connected and Linked Data
for
Connected and Linked Data
Jerry Hayes
Frank Hardisty (Advisor)

2. Connected Data in GIS Today
Many use cases in GIS
Creates logical model to represent topology.
Storage of logical model is not optimal.
Problem: Relational database abstraction impedes performance and scalability.

3. Linked Data in GIS Today
Links described by semantic relationships.
Semantics enable data discovery.
Early stages of adoption in GIS.
Problem: Vast potential of the Semantic Web is unrealized in GIS!

4. How GIS Stores Connected Data
Uses relational database tables
Having trouble “visualizing” the network ? … so do machines!
Abstraction introduces unnecessary overhead.
Bad for large datasets!

5. Graph Databases for Connected Data
Stores connected data in its native format.
Much easier to visualize network … machines are happier too!
Removes unnecessary overhead.
Good for large datasets!

6. Database Performance Comparisons
Performance comparisons are difficult.
… how “connected is the connected data?
Preprocessing data helps mitigate issues.
… ESRI’s preprocessed logical network model.
In general …
i) RDBMS are optimized for aggregation queries
ii) Graph databases are optimized for traversing.

7. Graph Database Characteristics
Two basic properties define graph databases.
Native Graph Processing
Native Graph Storage

8. Linked Data … the Next Frontier
Connects data to data on the Web
Uses Resource Descriptive Framework (RDF).
Creating quality linked data is challenging!
Only useful in sufficient quality and quantity.

9. Accessing Linked Data Many RDF datasets are now available
LinkedGeoData for GIS applications.
Data quality, availability and stability concerns.
Tools are available for accessing RDF models.

10. Open Source System Architecture
Server side is stateless.
PostGIS used for ..
Storing physical model.
Data visualization.
Neo4j used for …
Storing logical model
Graph traversals
Implemented in the IBM Cloud

11. Servlet Architecture Provides RESTful API. Enables spatial analytics
Enables “data” discovery.
Integrates physical and logical model processing.
Implemented in the IBM Cloud