A method for automated transformation and validation of online datasets
McCarthy, Suzanne, McCarren, AndrewORCID: 0000-0002-7297-0984 and Roantree, Mark
(2019)
A method for automated transformation and validation of online datasets.
In: 23rd IEEE-EDOC Conference: The Enterprise Computing Conference, 28 -31 Oct 2019, Paris, France.
While using online datasets for machine learning
is commonplace today, the quality of these datasets impacts
on the performance of prediction algorithms. One method for
improving the semantics of new data sources is to map these
sources to a common data model or ontology. While semantic
and structural heterogeneities must still be resolved, this provides
a well established approach to providing clean datasets, suitable
for machine learning and analysis. However, when there is a
requirement for a close to real time usage of online data, a
method for dynamic Extract-Transform-Load of new sources
data must be developed. In this work, we present a framework for
integrating online and enterprise data sources, in close to real
time, to provide datasets for machine learning and predictive
algorithms. An exhaustive evaluation compares a human built
data transformation process with our system’s machine generated
ETL process, with very favourable results, illustrating the value
and impact of an automated approach.