Databases and Systems

gain performance by distributing queries across multiple processors in parallel,
though such gains are rare in practice.
The OPM system, described in the article by Markowitz et al, uses a
middleware layer to impose a uniform object-oriented data model 3 on a potentially
heterogeneous set of back-end databases. Davidson et al’s BioKleisli takes a similar
approach but uses as its common data model a model adopted from logic
programming which is similar to the relational model but more powerful.
The SRS system described by Carter et al occupies an interesting middle
ground between federation and warehousing. In SRS the datasets are warehoused in a
single computer for searching, but remain organized in their original formats. New
databases are added to the warehouse by supplying parsers for their flat-file formats;
the files themselves are unaffected, which makes updating an SRS system uniquely
simple compared to most warehouse designs.
The Biology Workbench project exemplifies another approach to data
integration made possible by the Web. It does not physically bring the data together
like SRS, nor does it create a virtual unified database like BioKleisli or the OPM
multidatabase capability. Instead, it integrates at the level of the front end by
providing a thin veneer of user interface which provides access to a number of
capabilities on the Web. A similar concept is employed by the BCM Search Launcher
[16]. A key challenge in bioinformatics software development is the need to
continuously evolve software systems to incorporate or adapt to new technologies
while maintaining compatibility with existing (legacy) systems. Traditional software
development practice has been described in terms of a “waterfall” model, in which
development progresses continuously “downstream” from requirements analysis to
design to implementation. This model provides little guidance to bioinformatics
developers, whose task more closely resembles that of an auto mechanic trying to
redesign a car while driving it. The rapid prototyping model, in which systems are
built by successive approximation from crude first attempts, comes closer to the
mark, but still assumes the luxury of an extended prototyping phase. The component-
based design model, in which systems can be quickly assembled from reusable
components, is one that many in bioinformatics are pinning their hopes on. Jungfer et
al advocate the use of CORBA, an industry standard for designing distributed object-
oriented software systems, which has been adopted at the European Bioinformatics
Institute and elsewhere as a middleware layer to handle communication between back
³ A data model is a set of primitive constructs, such as sets, relations or objects,
which can be used to build database schemas. The most common data models are the
relational and the object-oriented. A data modelling language expresses the concepts
of some data model in a specific notation for writing down schemas; the SQL
language used in relational databases includes a data modelling component called the
create table statement. A schema describes the structure of data in a particular
database. A database management system (DBMS) such as Oracle TM or Sybase
interprets a schema definition written in a data modelling language as instructions for
creating a database with the specified structure.
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