This presentation, which is aimed at executives and managers, concentrating on the “big picture”, describes the data problem in NASA and some of the ways NASA is using Semantic Web technologies to tackle the data problem.

NASA has a data problem

Size

There’s a practically unimaginable amount of NASA data: unknown terabytes of data across disparate sites and in every data format possible.

Scale

1. structured, unstructured, semistructured
2. sensor and experimental data output from every conceivable kind of device, including thousands of bespoke or one off devices
3. metadata embedded in mountains of imagery (photographs, video, etc.)
4. all the usual “enterprise data” associated with an organization of NASA’s size and scope
5. data about people, their skills, and ongoing projects, including teams across dozens of sites, thousands of experts, and hundreds of thousands of existing and historical projects
6. it’s very complex and needs to be integrated in many ways.

For example, when planning to return to the Moon, NASA analysts have to use lunar planetology data to find potential landing sites. But what makes a landing site suitable is a complex question, involving seemingly (but not really) disparate data about lunar topography and geology, but also about people, their skills, historic projects, sensor data, as well as mission goals under various scenarios.

Importance

NASA’s data matters: it’s vital to helping humanity understand its place in the universe. Few human projects matter more.

Diversity

NASA’s data is very public and belongs not only to the US taxpayer but, also, in some sense, to the world. I18N issues matter, as do accessibility issues.

NASA has been working on its data problem

1. Metadata & KM councils and best practice groups
2. Catalogs, libraries, dictionaries of data collections
3. Hence, lots of well-curated data collections
4. Scientific and resarch communities with big stakes in getting things right
5. But much of this information isn’t Web-accessible or machine-readable or sufficiently expressive

NASA’s Using Semantic Web technologies

1. RDF to manage agile data integration
2. OWL ontologies to describe and reason about complex planetary data
3. Best-of-breed Semantic Web tools like mSpace to manage complex knowledge

POPS: A Case Study

1. Live demo of POPS (People, Organizations, Projects, Skils)
2. Starting modestly to build attractor services
3. Loosely coupled with existing enterprise architecture
4. Deployment as simple web service
5. Browse, search, then query
6. Using mSpace to allow users to navigate POPS data to discover novel connections and relationships
7. Social network visualizer in JSpace, a Java clone of mSpace

What’s Next?

1. More RDF-based data integration
2. Add search and ad hoc query (adopting SPARQL)
3. More OWL to describe and reason about more complex knowledge domains
4. Refactoring success stories into infrastructure services (search, query, OWL reasoning)