IEEE Systems, Man and Cybernetics Magazine - October 2020 - 39

For example, object-aware processes describe the behavior of single objects through microprocesses, whereas the
dynamic construction of linked objects and their synchronized execution is described and enforced through macroprocesses. However, the respective approaches need to be
enhanced to integrate physical objects as well as their
behavior into the overall process.
Breaking Down End-to-End Processes
For a large class of processes (typically referred to as
dynamic or knowledge intensive [9]), the advent of an
overwhelming amount of sensor data and a tremendous
number of things (which act in an environment where
they encounter no central control and behave according
to "personal" agendas) makes it practically impossible to
define comprehensive end-to-end process models. Things
will perform their own routines, so-called repeated
behavior patterns, or habits (possibly to be mined; see
[10]). Accordingly, processes will have to be organized as
event-driven microprocesses to represent these habits.
Whereas the end-to-end business process itself may be
modeled in traditional ways, the linking of microprocess

Complex
plex
Organizational
ational
Process
cess

models is far more complex; to cope with this emerging
intricacy, the possible interactions between microprocess models must not be described at the low level of
message exchanges but through a higher semantical
level, similar to the utilization of semantic object relations for the purpose of object interactions in objectaware process management. Figure 2 gives the intuition
of such a complex interplay.
Detecting New Processes From Data
Designing a system in a bottom-up manner without prescriptive process models promises more flexible and
inclusive procedures. However, the question arises: To
what extent we can let the system just evolve and be discovered? When developing support for software-enabled
business processes based on the principles of the IoT, an
evolutionary self-organizing process will take place in
some respect. Thus, one must find the appropriate level
of structuring and prescription without harming the
capability to self-organize. There is a gap between IoT
data and concepts at a model level to enable behavior prediction and identify changes in behavior. The IoT

Instructions, Directives,
and Commands

Events
(Generated by Sensors,
Actuators, and Participants)
Personal
Process/Habit

Monitor and Support
Se
Sensors
and
A
Actuators

Follow
Process Participant

Figure 2. The interplay of personal processes/habits with respect to complex organizational processes. Process

participants follow habits and are monitored and supported by sensors and actuators. Events and instructions/
directives/commands interconnect the two layers but without any rigid prescription and possibly through
models that are to be dynamically mined.

O c tob e r 2020

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE

39



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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