Search

Fragmentation, allocation and replication are techniques widely used in relational databases to improve the performance of operations and reduce their cost in distributed environments. This article shows an analysis of different methods for database fragmentation, allocation and replication and a Web application called FRAGMENT that adopts the work technique that was selected in the analysis stage, because it presents a fragmentation and replication method, it is applied to a cloud environment, it is easy to implement, it focuses on improving the performance of the operations executed on the database, it shows everything necessary for its implementation and is based on a cost model. FRAGMENT analyzes the operations performed in any table of a database, proposes fragmentation schemes based on the most expensive attributes and allocates and replicates a scheme chosen by the user in a distributed environment in the cloud. This work shows a common problem in fragmentation methods, overlapping fragments, and provides an algorithm with an approach to address it. This algorithm results in the predicates that will define each fragment in a distributed environment. To validate the implemented technique, a second web application is presented, dedicated to simulate operations on sites and focused on producing a log file for the main application. Experiments with the TPC-E benchmark demonstrated lower response time of the queries executed against the distributed database generated by FRAGMENT compared with a centralized database.

Automatic plant identification has been an important issue in the last years. Most of the state-of-the-art methods for this purpose use leaf features to predict the species. Despite there are many methods to extract different leaf features, just few of them are focused on discriminating between simple and compound leaves. In this work, we introduce a method to detect compound leaves. Our method uses concentric circles to explore the surface of the leaf to count the changes of color in binary images, then, the changes are analyzed to detect compound leaves. The method predicts correctly more than 96% of the leaves in the Flavia data set. We also tested the method with some images of leaves available on the Internet, with 100% of correctness. The information on whether a leaf is ​​or not compound, was used on experiments to observe whether this improves the performance of classifiers.