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Using an assembly algorithm is a fast and intuitive way to link (partially) homologous reads without requiring a database of reference sequences.
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The vast majority of histopathology research projects based on a series of tissue samples will require a database to store and organize the data.
The approach requires a database administrator (DBA) to specify an export schema detailing the content and structure of the archived data, and a mapping of the existing database to that schema.
Predicting microbial growth requires a database containing a large amount of information about the effects of the composition and processing of meat products on pathogen survival, growth, and death.
This requires a database with records of various population characteristics, such as age and gender in a specific country.
These methods heavily rely on the pre-existing knowledge about the molecules in the database, and require a database of small molecules with known binding targets.
AppR requires a database which we chose to be PostgreSQL 9.4 and a server for deployment for which we chose the well-known Apache Tomcat 7.0.68.
Such data models are fixed structure and require a database locking for a certain period of time upon migration processes, such as from one version to another version, adding more attributes to a table etc.
Since enterprise applications require a database to be useful, Oracle should dominate this market in the way that Microsoft leveraged its PC operating systems into a commanding lead in word processors and spreadsheets.
The prototype requires a database to store the project archive as information records, file access history and/or a central file repository to store the electronic files of the archive if digital files are part of the project archive.
One group, the empirical models (e.g. One Slope Model [4]), utilises relatively simple formulas containing empirical parameters to estimate path-loss and therefore do not require a database of exact positions of obstacles.
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