Conceptual Integration of Genome Databases via Reduced Autonomy and Domain-Specific Data Models

Transcription

1 Conceptual Integration of Genome Databases via Reduced Autonomy and Domain-Specific Data Models Mark Graves Dept of Cell Biology Houston, TX USA Ellen Bergeman Charles Lawrence

2 Problem Goal To integrate diverse sources of information to answer questions that are laborious or impossible to answer today. -- MIMDB 94 report Difficulty multiple, heterogeneous, autonomous database systems

3 Solution A database which is: 1.distributed across different sites 2.managed by several autonomous groups 3.conceptually integrated.

4 Database System Components * Component Database DBMS Other Software Users DB Administrator Developers Hardware Heterogeneity Heterogeneous Heterogeneous Heterogeneous Heterogeneous Heterogeneous Heterogeneous Heterogeneous

5 Database Heterogeneity Characterize database heterogeneity based on the schemas used for database design. Database Design Schemas [Teorey 1994]: Conceptual Logical Physical

6 Conceptual Interconnection Describe conceptual interconnection linguistically, in terms of: Syntax Semantics Pragmatics

7 Genome Database Requirements Technical Large, rapidly growing quantities of data Rapidly changing types of data Complex, interconnected structure Imprecise definition Sociological Independent operation Integrated data

8 Interconnection Requirements Technical Database-centered Data exchange language Natural for the domain Expressive Flexible Extensible Appropriate operations Sociological Data not limited to any one viewpoint Multiple, abstract solutions

9 Our Approach Create a formal description of the technical requirements for interconnection. Characterize the sociological requirements to evaluate the technical solution.

10 Domain-Specific Data Model domain-specific data model -- a formal means of representing and manipulating the structure and behavior of a database using the terminology of a domain (sublanguage).

11 Design Autonomy Developers of individual database systems have separate and independent control over the design of the system.

12 Restricted Conceptual Database Design Autonomy Conceptual Restricted Autonomy Logical Fully Autonomous Physical Fully Autonomous

13 Approaches to Restricted Autonomy 1.Restrict the area of coverage for interconnection. 2.Restrict the functionality of the language used for data exchange. 3.Develop a common conceptual schema to which each database is mappable.

14 Genomics-Specific Data Models Physical Map Genetic Map Genome Map Sequence Gene Regulation

15 Physical Map Data Model Type Constructors Physical Map Clone Distance Location Operations list all clones in a map merge two maps change size of a clone Constraints Any map that contains auxiliary information about a clone must also contain that clone. All clones are contained in some map.

16 Starting Point 1.Restrict the data model type constructors to be abstract data types. 2.No constraints. Caveat: Not sufficient for all of genome data, but a useful starting point.

17 Simple Example Types MARKER, SYMBOL, MARKER_TYPE, DNA_BASE Operations marker(name:symbol,type:marker_type): MARKER rflp(): MARKER_TYPE dinucleotide_repeat(dna_base,dna_base): MARKER_TYPE A,T,G,C: DNA_BASE

18 Conclusion 1.Both technical and sociological requirements of interconnection should be considered during development. 2.Restricted conceptual design autonomy may be a viable approach to interconnecting molecular biology database systems. 3.Domain-specific data models provide a useful formalization of the data exchange language.