A Data Mining Approach To Support The
Development Of New Fuels And Technology
Author(s)
G. S. Terra, C. L. Curotto & N. F. F. Ebecken
Abstract
In the present work, data mining techniques are used to model the non trivial
relationships between properties that characterize the fuels, engine technologies
and car emissions. Using models to predict car emissions from fuel properties
and technologies engines can improve their development process. To support
models of relational data, using the Object Linking and Embedding Database for
Data Mining technology, a Simple Naive Bayes Incremental classifier was
implemented in Microsoft® SQL Server™, supporting numeric input attributes,
multiple prediction attributes and incremental update of data. Computational
experiments using real word data sets were made to evaluate the results obtained
by this classifier.
1 Introduction
The automobile industries are stimulated to develop an efficient car, with new
technologies, that improves the autonomy and reduce the pollutant emission. The
downstream oil company also contributes when develops new fuels that provides
a positive interaction with those new car technologies. The fuel characteristics
are modified when additives are incorporated in their formulation or changes are
made in the proportion of their basic composition. During the fuel development
phase different combinations are tested in cars where theirs emissions are
measured in controlled tests. The results were stored in a database and a
classification model was built to improve this research area.
Keywords
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