IEEE Systems, Man and Cybernetics Magazine - October 2020 - 35

by Christian Janiesch, Agnes Koschmider, Massimo Mecella, Barbara Weber,
Andrea Burattin, Claudio Di Ciccio, Giancarlo Fortino, Avigdor Gal, Udo Kannengiesser,
Francesco Leotta, Felix Mannhardt, Andrea Marrella, Jan Mendling, Andreas Oberweis,
Manfred Reichert, Stefanie Rinderle-Ma, Estefanía Serral, WenZhan Song, Jianwen Su,
Victoria Torres, Matthias Weidlich, Mathias Weske, and Liang Zhang
these two paradigms can be combined, and we discuss
emerging challenges and intersections from a research
and practitioner's point of view in terms of complex software systems development.
Background
Our world is increasingly linked through a large number of
connected devices that typically are embedded in electrical/electronical components and equipped with sensors
and actuators that enable sensing, acting on or reacting to,
collecting, and exchanging data via various communication networks, including the Internet, thereby forming the
IoT (see [24] and "The Internet of Things"). This enables the
continuous monitoring of phenomena based on sensing
devices (wearables, beacons, smartphones, machine sensors, and so forth), and it creates analytics opportunities in
smart environments (smart homes, connected cars, smart
logistics, Industry 4.0, and so on) as well as the possibility
to actuate feedback. Therefore, the IoT contributes to the
recent trend known as big data, and it is one of the three
main sources of information beside human-sourced and
process-mediated data.
Business processes (see "Business Process Management") represent a specific ordering of tasks and activities
across time and places to serve a goal, and they often provide the driving force for system development. Process analytics, execution, and monitoring based on IoT data can
enable an even more comprehensive view of systems and
realize unused potential for optimization. As an example,
during the past, process analytics and, in particular, process mining have been hampered by the fact that procedures are often incomplete and erroneous; with the IoT
producing a large amount of data that is stored in the cloud
[1], even more information become available for analysis,
possibly resolving issues of incompleteness and enabling
the provision of error correction methods based on multiple
data items [2]. In the literature, some works are emerging on
combining BPM and the IoT, e.g., utilizing sensor data to
enable the actuation of services [3] and adapting running
business processes to continuously align them with the
state of the things (e.g., assets, humans, and machines) [4].
Still, there are many open challenges to be tackled. Both
BPM and the IoT will benefit from a wider integration.
How Can the IoT Benefit From BPM?
Let us consider a complex system that includes multiple
components interacting within a smart environment and
that is aware of the components' locations, movements,
and interactions. Such a system can be a smart factory
with autonomous robots, a retirement home with

connected residents, and, at a larger scale, a smart city.
While the parties in the system can track the movements
of each component and relate multiple components' behaviors to each other, they do not know the components' agendas. Often, their interactions are based on habits, i.e.,
routine low-level processes, which represent recurring
tasks. Some of these routines are more time and cost critical than others, some may be dangerous or endanger others, and some may just be inefficient or superfluous.
Knowing their agendas, goals, and procedures can enable
a better basis for planning, execution, and safety.
The solution to typical challenges related to the IoT, such
as scalability (a massive number of devices, reliable coverage, and power consumption problems), energy harvesting,
and hardware/software optimization, can benefit, as well,
by the knowledge of such agendas and goals. Finally, such
knowledge can support the design tradeoffs involved in
moving cloud services to the edge of the network (so-called
fog computing, i.e., defining the right allocation of data storage and processing and offering services).
How Can BPM Benefit From the IoT?
Let us consider a complex process with multiple parties
interacting in the context of a business transaction. Such a
process can be, for example, procurement, where goods are
ordered, delivered, stored, and paid for. While the system
can track each automatically executed activity on its own,
it relies on messages from other parties and, in the case of
manual activities, physically entered information. If these
data are not entered, and if they are entered incorrectly,
discrepancies between the digital (i.e., computerized representation of the) process and the real-world execution of
the procedure occur. Similar concerns hold if the participants do not obey the digital process under certain circumstances, e.g., an emergency in health care, or have not yet
entered the data even though the respective activity was
already executed through the real-world procedure.
Such scenarios might be better manageable if they
closely linked the digital process with the physical world,
as enabled by the integration of the IoT and BPM; e.g., the
completion of manual activities can be made observable
through the use of appropriate sensors (e.g., [6]). The IoT
can complete BPM with continuous data sensing and physical actuation for improved decision making. Decisions in
processes require relevant information as the basis for
making meaningful determinations. In general, it is not
sufficient to retrieve these data solely from traditional
repositories (e.g., databases and data warehouses); up-todate information is also necessary. Information from the
IoT, such as events, that is provided through in-memory
O c tob e r 2020

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE

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IEEE Systems, Man and Cybernetics Magazine - October 2020

Table of Contents for the Digital Edition of IEEE Systems, Man and Cybernetics Magazine - October 2020

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