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multi-dimensional, current research studies have
mainly focused on few of these attributes. We
contend that elevating our understanding of factors
and attributes relevant for industrial practitioners will
help companies, researchers and tool vendors to meet
the specific information needs.
In future work we plan to quantitatively
evaluate the effect size of important attributes
towards ML adoption decision using large scale
survey of companies that have already adopted ML
techniques and ones that are yet to embrace them.
Research with regard to which factors are important
for industry and evaluative studies of ML based
techniques/tools on these factors can complement the
existing and on-going work on establishing the
characteristics of ML techniques and thus contribute
toward their adoption in industry and society.
ACKNOWLEDGEMENTS
The research presented here is done under the VISEE
project which is funded by Vinnova and Volvo Cars
jointly under the FFI programme (VISEE, Project
No: DIARIENR: 2011-04438).
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