UCSD NeuronCentered Database Amarnath Gupta Bertram Ludscher Maryann
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’s query language XML documents XML View(s) Wrapper Web Site Wrappers also export: 1. Schemas & Metadata 2. Description of supported queries. . . Wrapper Database Image Features 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 – 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 – – – 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
- Slides: 7