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Increasing the Efficiency of Rule-Based Expert Systems Applied on Heterogeneous Data Sources
Nowadays, the proliferation of heterogeneous data sources provided by different
research and innovation projects and initiatives is proliferating more and more and
presents huge opportunities. These developments create an increase in the number
of different data sources, which could be involved in the process of decisionmaking
for a specific purpose, but this huge heterogeneity makes this task difficult.
Traditionally, the expert systems try to integrate all information into a main
database, but, sometimes, this information is not easily available, or its integration
with other databases is very problematic. In this case, it is essential to establish
procedures that make a metadata distributed integration for them. This process
provides a “mapping” of available information, but it is only at logic level. Thus, on
a physical level, the data is still distributed into several resources. In this sense, this
chapter proposes a distributed rule engine extension (DREE) based on edge computing
that makes an integration of metadata provided by different heterogeneous
data sources, applying then a mathematical decomposition over the antecedent of
rules. The use of the proposed rule engine increases the efficiency and the capability
of rule-based expert systems, providing the possibility of applying these rules over
distributed and heterogeneous data sources, increasing the size of data sets that
could be involved in the decision-making process
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