  Distributed Search Engine Architecture (DSEA) hosts numerous independent
topic-specific search engines and selects a subset of the databases to search
within the architecture. The objective of this approach is to reduce the amount
of space needed to perform a search by querying only a subset of the total data
available. In order to manipulate data across many databases, it is most
efficient to identify a smaller subset of databases that would be most likely
to return the data of specific interest that can then be examined in greater
detail. The selection index has been most commonly used as a method for
choosing the most applicable databases as it captures broad information about
each database and its indexed documents. Employing this type of database allows
the researcher to find information more quickly, not only with less cost, but
it also minimizes the potential for biases. This paper investigates the
effectiveness of different databases selected within the framework and scope of
the distributed search engine architecture. The purpose of the study is to
improve the quality of distributed information retrieval.
