IEEE Systems, Man and Cybernetics Magazine - October 2020 - 42

generalization (see an initial study in [19]). Also, not all
scenarios for potential IoT applications can equally benefit from BPM; e.g., the single app-controlled Phillips Hue
lamp will not profit from BPM concepts, whereas a scenario that schedules maintenance appointments for a fleet
of cars might.
Before concluding, we would like to highlight a crossissue, i.e., dealing with security and, in particular, privacy
issues. Privacy levels that exist at the sensor layer might
be different with respect to those at the BPM one. A fulldisclosure approach should be avoided, especially in contexts where sensitive (i.e., personal) information is
collected. The most relevant challenge, in this case, is the
communication between the two worlds, each of them
with corresponding privacy/security levels and policies.
The layer in charge of integrating these two sides should
be planned according to the principles of privacy by design
[20]: "Identify and examine possible data protection problems when designing new technology and to incorporate
privacy protection into the overall design, instead of having to come up with laborious and time-consuming 'patches' later on" [21]. This issue can also be seen as a
"nonfunctional requirement," referring to the points discussed in the "Intersections/Challenges" section, but others might also be affected.
Finally, and partially related to the previous point,
there are ethical aspects of integrating the IoT and BPM:
the introduction of raw events paves the way for a whole
new set of analyses and explorations. On the one hand,
these analyses must preserve the privacy of the individual
(privacy is recognized as a fundamental right; see [27] and
[28]). At the same time, the analyses should not be unfair
and should not provide unequal treatment of people based
on membership to a category or a minority. This problem
is typically referred to as discrimination-aware data
mining [22]. More generally, the literature also talks
about "privacy-preserving data mining" [23]. There are
several points that are directly affected, such as those in
the "Intersections/Challenges" section, due to the set of
analyses that the integration of the IoT and BPM will
make possible.
About the Authors
Christian Janiesch (christian.janiesch@uni-wuerzburg
.de) is an assistant professor at the University of Würzburg.
He is a member of the Business & Information Systems
Engineering department editorial board and has authored
more than 100 scholarly publications. His research interests
lie at the intersection of business process management and
business analytics, with frequent applications in the Industrial Internet of Things.
Agnes Koschmider (ak@informatik.uni-kiel.de) is a
professor of information systems at Kiel University and
head of the Process Analytics group. Her work has been
published in more than 90 research papers and articles. Her
research interests broadly concern how to extract meaning42

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE O ctober 2020

ful event logs from Internet of Things (information) systems, with a special focus on privacy preservation.
Massimo Mecella (massimo.mecella@uniroma1.it) is an
associate professor at Sapienza Università di Roma. His
research interests focus on business process management,
cyberphysical systems and the Internet of Things, advanced
interfaces and human-computer interaction, and software
architectures and service-oriented computing, with applications in multiple fields, including digital government, smart
spaces, Industry 4.0, health care, disaster/crisis response and
management, cybersecurity, and digital humanities.
Barbara Weber (barbara.weber@unisg.ch) earned her
Ph.D. degree in business administration from the University
of Innsbruck, Austria. She is chair of software systems programming and development at, and director of, the Institute
of Computer Science at the University of St. Gallen, Switzerland. Her research interests include the development of
adaptive software systems, including flexible and adaptive
business process support and neuroadaptive information
systems as well as human and cognitive aspects of software
and information systems engineering.
Andrea Burattin (andbur@dtu.dk) earned his Ph.D.
degree from the Universities of Bologna and Padua (Italy).
He is an associate professor at the Technical University of
Denmark and, since 2019, has been a member of the IEEE
Task Force on Process Mining Steering Committee. The
IEEE Task Force on Process Mining awarded his doctoral
thesis the Best Process Mining Dissertation Award for dissertations defended in 2012-2013.
Claudio Di Ciccio (claudio.diciccio@uniroma1.it) is an
assistant professor at Sapienza Università di Roma, Italy. He
was program committee (PC) chair for the first Blockchain
Forum at the International Conference on Business Process Management 2019 and regularly serves on the PCs of
the conferences in the area. He is a member of the IEEE
Task Force on Process Mining Steering Committee. His
research interests include process mining, declarative modeling, and blockchains.
Giancarlo Fortino (giancarlo.fortino@unical.it) is a full
professor at the University of Calabria (Unical), Italy. He is
director of the Smart, Pervasive, and Mobile Systems Engineering lab at Unical as well as cochair of joint labs to study
the Internet of Things (IoT) that were established between
Unical and Wuhan University of Technology, Southern Medical University, and Huazhong Agricultural University in
China. In addition, he is cofounder and chief executive officer of SenSysCal, a Unical spinoff focused on innovative IoT
systems. His research interests include agent-based computing, wireless (body) sensor networks, and the IoT.
Avigdor Gal (avigal@ie.technion.ac.il) is a full professor of data science at Technion-Israel Institute of Technology. He has served as a program and general chair of the
International Conference on Business Process Management
and the International Conference on Distributed EventBased Systems. He is the author of Uncertain Schema
Matching. His research interests include data integration,

IEEE Systems, Man and Cybernetics Magazine - October 2020

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