Models for the digital transformation
Models for the digital transformation
Jeff Gray 0 1
Bernhard Rumpe 0 1
0 RWTH Aachen University , Aachen , Germany
1 University of Alabama , Tuscaloosa, AL , USA
?Digital transformation? is currently an important trend
that penetrates many industrial and societal domains. The
phrase is also emerging as a buzzword that allows
different stakeholders to inject various forms of innovation into
their respective company, business, government, academic
institution, or other public services. The nuances of digital
transformation are broad and have not yet been defined
precisely, but even job advertisements often contain the phrase.
Deconstructing the term from its two primary words,
we identify two well-known concepts. ?Transformation?
describes a general process that starts with some initial
situation that moves toward a changed, and supposedly better
situation. May be that in this case the word transformation
is not the best word choice because the underlying
transformation may never meet a stable end, but rather undergo a
continual set of evolutionary optimizations related to new
forms of business, production, logistics, medicine or other
changes within the targeted domain. ?Digital? suggests that
many changes in society, business and industry will be driven
by information technologies that allow data to be processed
in real-time and even used to intelligently derive information
to finally to provide stakeholders with improved knowledge
about their processes and products. Downstream digitization
would also allow optimization, automation activities and
production techniques of various forms.
Of course, within the context of SoSyM, the key question
is the extent to which models can aid the emerging digital
As SoSyM readers may observe, models have much
potential toward achieving the goals of digital transformation.
Below are a few possible contexts for application, among
others that we are sure could be suggested by the SoSyM
(1) One of our Editors, Ulrich Frank, recently wrote that
models can be used beneficially to mitigate the
differences and challenges that emerge between different
worlds that speak very unique languages. This becomes
obvious when considering the various stakeholders that
come into contact with Digital Products or services.
Each stakeholder may have individual domain-specific
terms to describe his or her needs, capabilities, and
unique information resources.
(2) Digital transformation often co-exists with large data
sets that are associated with some processing need of
the transformation context. Data has structure. For an
explicit, well-founded handling of this data, models are
necessary to describe the data structure, but also how
to manipulate the data and retrieve it efficiently.
Transformation models describe how to slice, select, join, or
aggregate data to retrieve useful information. Beyond
manipulation of data, there is much software that is
necessary to handle, manage and visualize this data. In the
future, traditional software engineering techniques may
use models to design such systems, or models at runtime
will describe specific techniques within a rather generic
software package (e.g., database, statistics packages or
(3) The design of digital products and the development of
product lines using digital technologies will lead to a
very challenging integration problem for the physical
components of a system, as well as the development
methodologies and their tools. Many of these tools use
very specific forms of models, written in proprietary or
semi-standardized modeling languages, that will need
a syntactic, semantic and tool-based integration.
(4) While traditional engineering uses human-generated
models to prescribe aspects of the system under
development, machine learning and data mining techniques
have the potential to reverse this relation by extracting
models from sets of running data. It will be interesting to
see how prescriptive and extracted models fit together,
if at all.
There is a deep list of research topics that need to be explored
in order to derive the understanding that will bring a pure
data-driven world together with prescriptive design models.
Particularly, the application of machine learning currently too
often relearns already well-known models, because
prescriptive models and machine learning are not well integrated.
Colleagues recently applied big data analytics in a larger
industrial project that at first and foremost re-uncovered basic
physical laws from a set of production data. While this result
may be interesting from a machine learning perspective, the
end result was not very helpful given the physical laws were
In the current discussion in the research literature about
digital transformation, models do not play a prominent role.
This may be because many people researching and
applying digital transformation are not aware of the possibilities
and capabilities of using models of appropriate languages.
It may also be the case that models and modeling languages
have become mainstream in many domains, such that it is not
regarded as a research topic anymore, but more as a helpful
tool that is commonly available and can be used out of the
box. While the latter points to a desirable level of maturity
for models, there is still plenty of research and application
opportunity for modeling, model languages, as well as
generative and analytical tooling, that can be applied to the ongoing
pursuits of digital transformation.
Content of this Issue
This issue contains the Special Issue on ?Modeling?
Foundations and Applications?, with Ana Moreira, Bernhard
Sch?tz, Peter Clarke, and Antonio Vallecillo as Guest
Editors. The included papers are described in the Guest Editorial.
This issue also contains five Regular Papers:
?Aspectual Templates in UML? by Gilles Vanwormhoudt,
Olivier Caron, and Bernard Carr?
?Proactive Modeling: A New Model Intelligence
Technique? by James Hill, Tanumoy Pati, and Sowmya Kolli
?Using Contexts to Extract Models from Code? by Lucio
Duarte, Jeff Kramer, and Sebastian Uchitel
?Model-based Tool Support for Tactical Data Links: An
Experience Report from the Defence Domain? by
Dimitrios Kolovos, Suraj Ajit, Chris Holmes, Julian Johnson,
and Richard Paige
?Contract-based Modeling and Verification of Timed
Safety Requirements within SysML? by Iulia Dragomir,
Iulian Ober, and Christian Percebois.