IEEE Systems, Man and Cybernetics Magazine - October 2020 - 41

of events from raw event data and, in a subsequent step,
entire processes and their activities from event data. This
is a nontrivial problem since event data belonging to different activities can be interleaving. Moreover, event information can belong to, or be relevant for, several activities so
that complex N:M relations between events and activities
have to be considered.
Once the activities have been identified, the next challenge is to discover the corresponding processes, i.e., to
correlate the activities with the corresponding process
instances. Process knowledge and BPM methodologies
(e.g., [15]) can support the discovery and identification of
underlying interactions as processes as well as the optimization to reduce wasted time and resources and
increase the safety of all involved agents. Process mining techniques provide promising ex post perspectives in
this respect but require the presence of a log that organizes the events in terms of traces representing the execution of a process instance. Similarly, but in an online
fashion, complex event processing can be used to derive
higher-level knowledge from raw events to provide an ex
nunc perspective [2]. Here, the timely provisioning of
events is crucial.
Improving Online Conformance Checking
Conformance checking is a process mining technique that
compares an existing process model with an event log of
the same process. It can be used to check if the reality of
process execution, as recorded in the log, conforms to the
model and vice versa. Online conformance checking takes
as input the context information and performs the comparison. This requires high-quality data and almost complete
information. Again, the IoT, as a data source, and data
management technologies can play a major role and
might improve the conformance checking of the actual
physical execution with the execution order as recorded
by the BPMS based on a secondary log of sensor information. Similarly, IoT data can be used for checking and
monitoring compliance rules that must be obeyed during
process execution.
Improving Resource Utilization Optimization
BPM can provide a governance structure for physical and
virtual organizations. BPM initiatives break up traditional
functional silos and introduce process managers to being
responsible for processes across departments. While complex systems and the IoT are centered around situations
that they must react to, BPM initiatives are organized
around processes. This ensures that some coordination
instance that is responsible for priorities and resource provisioning can monitor events and intervene with additional
knowledge, if necessary. In a pure IoT paradigm, there is a
danger that decisions will produce only local optima. The
coordinating unit responsible for resource provisioning
has advanced knowledge about the future behavior of
agents since the agents have to follow their process models

and thus can provide resources (e.g., computing power,
network bandwidth, and things) with greater accuracy,
reducing the processing time and increasing the throughput. This also helps to reduce communication time-outs
and rollbacks as well as abnormal process terminations
(see some initial results in [16] and [17]).
Improving Resource Monitoring and the
Quality of Task Execution
The execution of tasks in a business process consumes
resources, including IT, such as the storage capacity for
processed data; computing power for calculations in scientific workflows; artificial agents, such as robots that automatically execute manual tasks; and human beings who
enter and analyze data and perform manual tasks. Also,
machines, e.g., those for packing drugs, can be considered
as resources (e.g., predictive monitoring; i.e., when the
machine has to be maintained, taking its usage and historical data into account).
All these resources might suffer from issues that hinder
optimal working conditions, such as over- and underutilization and even damage/illness. IoT-based sensors can
pick up these issues by measuring machine behaviors and
human stress levels [18] and suggest changes to the process execution to alleviate the effects. Furthermore, the
IoT can support the execution of (knowledge-intensive)
tasks in a process through context-specific knowledge
provisioning, e.g., in terms of instructions and training
materials covering how to execute a task and regulations
that are relevant to a user's particular context. Sensor data
can be leveraged to determine the actual context and identify information needs (e.g., the detection of cognitive overload and stress).
Concluding Remarks
The IoT provides many opportunities for organizations/
companies/industries and for personal use through the
meaningful yet dynamic interaction of humans, software,
machines, and things. BPM is a well-established discipline
that deals with the discovery, analysis, (re)design, implementation, execution, monitoring, control, and evolution of
business processes. So far, both areas have been separately considered. In this article, we formulated a number of
points for the amalgamation of the IoT and BPM, which we
deem important to be tackled in the near future for the IoT
to benefit from business processes and vice versa.
When adopting the IoT and BPM in the building of complex systems, we need to carefully consider specific application scenarios; therefore, the generalizability and
adoption of practices, patterns, and modeling approaches
may be questionable. One of the challenging concepts presented in this article is that general modeling, design, and
mining approaches should be devised to enable the consideration of different applications. An interesting preliminary question is how to classify IoT applications, and
according to which dimensions, to perform such a
O c tob e r 2020

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IEEE Systems, Man and Cybernetics Magazine - October 2020

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