UCSD Neuron-Centered Database Amarnath Gupta Bertram Ludäscher Maryann Martone

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

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 ..)

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

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

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)

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