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One of the difficulties users face in querying a complex biological DB, such as those in the BioCyc collection, is understanding the DB schema.
The DB schema changes over time, and the data migrates between tables.
We can diminish the gap from general plaintext search systems through minimizing computational overhead in the retrieval stage and applying efficient DB schema.
From the next subsection, we analyze our results in two respects of the applicability of DB schema and the influence of functions.
The previous schemes have focused on the development of new encryption algorithms, while we apply general DB schema to the encrypted database instead of developing an efficient encryption algorithm.
It can be resorted at will on any column in the output page as seen in Figure 3. Documentation is provided for several aspects of the SAQP and of the DB schema being queried.
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XML is also a primary method of marshalling input to databases and there are many tools which allow the construction of db schemas from XML schemas.
Secondly, for more efficient application in a real world, we develop the database table in order to apply the efficient DB schemas (normalization using primary key and foreign key) to encrypted documents.
Obviously, query and report generation performance depends on the size of the DB, the schema structure, and the organization/distribution of data within and across the tables.
The linear-in-dB gain tuning schema is designed using a novel exponential current generator which also offers temperature compensation of the VGA's gain.
Interactive documentation within the SAQP describes the schema of the queried DBs.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com