1 research outputs found
How Far are we from Data Mining Democratisation? A Systematic Review
Context: Data mining techniques have demonstrated to be a powerful technique
for discovering insights hidden in data from a domain. However, these
techniques demand very specialised skills. People willing to analyse data often
lack these skills, so they must rely on data scientists, which hinders data
mining democratisation. Different approaches have appeared in the last years to
address this issue.
Objective: Analyse the state of the art to know how far are we from an
effective data mining democratisation, what has already been accomplished, and
what should be done in the upcoming years.
Method: We performed a state-of-the-art review following a systematic and
objective procedure, which included works both from the academia and the
industry. The reviewed works were grouped in four categories. Each category was
then evaluated in detail using a well-defined evaluation criteria to identify
its strengths and weaknesses.
Results: Around 700 works were initially considered, from which 43 were
finally selected for a more in-depth analysis. Only two out of the four
identified categories provide effective solutions to data mining
democratisation. From these two categories, one always requires a minimum
intervention of a data scientist, whereas the other one does not provide
support for all the stages of the data mining process, and might exhibit
accuracy problems in some contexts.
Conclusion: In all analysed approaches, a data scientist is still required to
perform some steps of the analysis process. Moreover, automated approaches that
do not require data scientists for some steps expose some problems in other
quality attributes, such as accuracy. Therefore, although existent work shows
some promising initial steps, we are still far from data mining
democratisation