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The next step is to identify database structures that are robust structures, that is good performers across the different types of query patterns and arrival rate levels.
We develop and demonstrate through a proof-of-concept case study, a new "query-driven" heuristics for database design that seeks to identify database structures that perform robustly in dynamic settings with dynamic queries.
By doing so, it becomes possible to use the same device as the one used in fingerprint-based searches: in order to identify database molecules that are similar to the query, it is simply necessary to rank them in terms of the number of substructures that belong to the same class as those in the query.
As head of the Environment Group, a unit within Citibank, which monitored the financial giant's global databases, Wynn knew that there was no single diagnostic product that could quickly identify database problems and, more important, fix the problem which arose routinely in running the database.
Here we present a stand-alone utility, Simrank, which allows users to rapidly identify database strings the most similar to query strings.
To name one example, RTPrimerDB (1) uses its own ID scheme to identify database entries, which is of little use to a researcher.
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Upon DBRegistry initialization, the names of available databases at the configured datasource are parsed using the Ensembl naming conventions and meta-data tables to identify database-type, species, assembly and schema release versions.
The identified database records with errors were manually checked against the data capture forms and corrected where needed.
Abstract, title, key words and subject headings specific to each of the identified database will be searched.
If the pill is not found in a pill identifying database, it could possibly be an illegal medication.
A request to complete the questionnaire survey was sent to the providers/coordinators of the identified databases.
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