UCSD NeuronCentered Database

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UCSD Neuron-Centered Database

• Amarnath Gupta
• Bertram Ludäscher
• Maryann Martone

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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

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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 ..)

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Integration through Mediation
User Query
Mediator
Mediators 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
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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

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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)

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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 SDSCs 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