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This article addresses a novel strategy that extends the query optimizer of a DBMS, so that it can also perform both logical and physical query plan optimizations in searches that include similarity predicates.
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Members of the Cornell Database Group have recently worked on novel approaches to query optimization that significantly extend the query sizes that can be optimized efficiently.
Thus, the paper proposes an architecture which offers a twofold approach to the statistical analysis of XML data, i.e. via a web service and by extending the query languages used in XML databases.
Indeed, adding new functions, supporting evolutions of the data model, extending the query language and then supporting well adapted optimization strategies, can require important coding effort.
We specifically addressed the long-standing problem of auxin and glucose crosstalk in plant development and extended the query to the possible role of G proteins in this complexity.
Individual repeat information cannot normally be garnered from fold prediction as all programs will, to varying degrees, tend to extend the query to template alignment substantially beyond the repeat segments, in particular if the adjacent regions are α-helical.
For example, imagine that we want to extend the query in Figure 2 with an additional restriction that depends on the 'gene encoding the polypeptide', not on the polypeptide itself.
We have extended the kernel query language of TSIMMIS (called OEM-QL) to a more powerful language (called LOREL, for LORE Language) that supports queries with advanced features such as subqueries, aggregates, and result restructuring.
QUERY NORMALIZER FOR WRAPPERS A query normalizer extends the functionality of a wrapper.
The language extends the capabilities of existing database query languages by allowing the user to define database predicates that express structural properties of strings (e.g. containment of certain patterns) or relations between several strings (e.g. similarity measures).
In a choice question that evaluated the ability to review a solution, the subject was shown an initial solved problem and was asked to choose from one of the three presented solutions which correctly extends the initial solution with additional query elements.
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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