1. UCSD Neuron-Centered Database
Amarnath Gupta
Bertram Ludäscher
Maryann Martone

2. What is Neuron-Centering (AKA The Holy Grail)?
Designing a database system such that it can be used to represent, store and access
Any property, measurements, …
of Any Nerve Cell or its constituent parts
from Any part of the brain
acquired through Any experiment
at Any spatial resolution
located at Any physical site
in a way that any biologist and biological applications can use or interface with it

3. Designing the database
Three problems
Modeling the neuronal structure
To what level of detail?
Modeling correlated information building on the neuronal structure
Structured as complex graphs
Integrating heterogeneous data (a short detour)
Quantitative morphology
Protein localization
Time-series study from physiological experiments …
Current Schema (and evolving ..)

4. Integration through Mediation
User Query
Mediator
Mediator’s query language
XML documents
XML View(s)
XML View(s)
XML View(s)
Wrappers also export:
1. Schemas & Metadata
2. Description of
supported queries...
Wrapper
Wrapper
Database
Image
Features
Web Site
and back

5. The Knowledge-Base Situate every data object in its anatomical context
a programmable knowledge-base that integrates and correlates every observed piece of data
An illustration
New data is registered with the knowledge-base
Insertion of new data reconciles the current knowledge-base with the new information by:
Extending the knowledge-base
Creating new views with complex rules to encode additional domain knowledge

6. Query Processing Query Types Our current approach Exploratory queries
Ad-hoc queries
Our current approach
Databases and knowledge-bases are integrated through a mediator built using a deductive database
Many queries such as protein localization need complex grouping of data across the nodes of the knowledge-base
We support some “traversal” queries on graph of data and knowledge entities
Painted Neurons as maps: exploring XML/VML-based interfaces (Ilya Zaslavsky, SDSC, UCSD)

7. Next Steps Modeling Querying Maturing the schema More data types
Richer knowledge-base constructs (e.g. has-part-of)
Connecting with atlases as spatial data objects
Integration with SDSC’s large-scale distributed data handling system
Querying
Capabilities to handle more generic graph queries
Better integration of pure querying with other functionality such as statistical computation
More expressive query interfaces