  The significance and abundance of data are increasing due to the growing
digital data generated from social media, sensors, scholarly literature,
patents, different forms of documents published online, databases, product
manuals, etc. Various data sources can be used to generate ideas, yet, in
addition to bias, the size of the available digital data is a major challenge
when it comes to manual analysis. Hence, human-machine interaction is essential
for generating valuable ideas where machine learning and data-driven techniques
generate patterns from data and serve human sense-making. However, the use of
machine learning and data-driven approaches to generate ideas is a relatively
new area. Moreover, it is also possible to stimulate innovation using
contest-driven idea generation and evaluation. The results and contributions of
this thesis can be viewed as a toolbox of idea-generation techniques, including
a list of data-driven and machine learning techniques with corresponding data
sources and models to support idea generation. In addition, the results include
two models, one method and one framework, to better support data-driven and
contest- driven idea generation. The beneficiaries of these artefacts are
practitioners in data and knowledge engineering, data mining project managers,
and innovation agents. Innovation agents include incubators, contest
organizers, consultants, innovation accelerators, and industries. Since the
proposed artefacts consist of process models augmented with AI techniques,
human-centred AI is a promising area of research that can contribute to the
artefacts' further development and promote creativity.
