SARAIVA, M. C.; http://lattes.cnpq.br/3382719006017432; SARAIVA, Márcio de Carvalho.
Resumo:
Database systems are becoming increasingly popular in the scientific community to
support the exploration of scientific data, m this scenario, users may not have the
necessary knowledge about the domain of the database or not knowing formulate
SQL queries for data analysis. To solve this problem has been emerged many studies
about queries recommendation techniques. The recommendation methods of query in
database has been emphasis in maximize the accuracy of the recommendations, but
other aspects such as novelty and diversity may be important for recommendations.
In this context, this research aimed to improve the recommendations of SQL queries
regarding the metrics: relevance, novelty, diversity, an amount of new tables,
precision and recall. This goal was achieved through an approach to the
recommendation of queries using clusters of database users. The results of
experiments using real historical queries of the SkyServer project shows that through
the proposed approach we can generate recommendations for appropriate queries for
each user of the database used. Furthermore, the proposed approach was evaluate
comparing with techniques described in related work. The analysis shows that the
values of the metrics studied in this research are 64,6% higher in the proposed
approach than the techniques compared. These results potentially provide better
conditions for studies and future work using groups of users to generate
recommendations for database queries. The proposed approach also enabled the
design of user behavior with database, this information contributes to better
understanding the interaction of users with management systems database.