An efficient query processing with approval of data reliability using RBF neural networks with web enabled data warehouse

To rise above the limitation of the Traditional load
forecasting method using data warehousing system, a new load
forecasting system basing on Radial Basis Gaussian kernel
Function (RBF) neural network is proposed in this project.
Genetic algorithm adopting the actual coding, crossover and
mutation probability was applied to optimize the parameters of
the neural network, and a faster growing rate was reached.
Theoretical analysis and models prove that this model has
more accuracy than the traditional one. There are several
methods available to integrate information source, but only
few methods focus on evaluating the reliability of the source
and its information. The emergence of the web and dedicated
data warehouses offer different kinds of ways to collect
additional data to make better decisions. The reliable and trust
of these data depends on many different aspects and metainformation:
data source, experimental protocol. Developing
generic tools to evaluate this reliability represents a true
challenge for the proper use of distributed data. In this project,
RBF neural network based approach to evaluate data
reliability from a set of criteria has been proposed. Customized
criteria and intuitive decisions are provided, information
reliability and reassurance are most important components of
a data warehousing system, as their power in a while retrieval
and examination.