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tion between these two spheres of control mutate the
functionality of the mentioned five perspectives.
Since the functional perspective is just the skele-
ton of a process step, it will not be changed notice-
ably - but more indirectly via the other perspectives
- by the IoT communication. However, all other per-
spectives will undergo positive modifications by this
interplay. The data perspective will heavily profit by
the enlarged database provided by IoT. Not just the
range of data can drastically be enlarged, but also the
accuracy and currentness of data will be improved
broadly (cf. Data intensive). This can lead to a new
quality of process execution and new ways of func-
tion provision. The latter leads to a direct improve-
ment of the operational perspective of a process step.
In addition, the organizational perspective of a pro-
cess profits from data provided by IoT. For example,
the actual geographical position of agents are reported
by wearables such that agent assignment can consider
this information and can better assign people to out-
standing tasks (cf. Ubiquitous). The behavioral per-
spective mostly benefits from the active communica-
tion between IoT and a BPM system. For example,
an IoT signal can promptly trigger starting an activ-
ity directly what can improve performance of process
execution significantly (cf. Triggering).
2.2 How Can BPM Benefit from IoT?
This discussion of the interplay depicts how posi-
tively an IoT integration into a BPM system improves
and enlarges its functionality. From a BPM system,
the IoT world acts as an extrinsic factor that actively
and/or passively affects the BPM system and enables
new or enhanced functionality. In this section, we
highlight the main benefits that IoT provides for BPM
applications and classify them w.r.t. the different
phases of the BPM lifecycle (Dumas et al., 2013).
Within the modelling phase of the BPM lifecycle,
the incorporation of IoT technology can reduce the
complexity of process models, i.e., it is possible to re-
place elements and patterns due to the opportunities
of IoT. In particular, certain activities, e.g., manual
control activities, can be avoided and thus removed
from existing models. Furthermore, through the pro-
vision of a broad and highly up-to-date database, pro-
cess models can be logically extended and enriched
with new dimensions and new perspectives. For ex-
ample, pure control-flow oriented models can be ex-
tended with data dependencies stemming from IoT
devices. The location awareness of IoT devices like
wearable computers creates the opportunity to intro-
duce a locational perspective into process models and,
for instance, assign activities to the staff members
based on distance measures. These new features lead
to more fine-grained and more specific process defi-
nitions that better reflect operational reality. Also the
execution phase can benefit from the integration with
IoT. Here, real time interaction, mobile and wear-
able interfaces for process control, new signaling as
well as activity indication technology (e.g., haptic and
acustic signals of smartwatches) can lead to signif-
icant latency and activity runtime reduction that ul-
timately leads to an improved overall case perfor-
mance. Incorporating IoT technology fosters process
monitoring as well. Here, data provided and provi-
sioned by IoT sensors increases process transparency,
e.g., the remaining time until next activities as well
as certain important environmental data. Last but not
least, IoT enhances the analysis phase by increas-
ing the quality and evidence of process event logs by
recording rich process data in form of IoT sensor and
device values. This big amount of data enables and
fosters multi-perspective process mining technology
(e.g., (Sturm et al., 2017)) which automatically pro-
duces data enriched process models.
2.3 How Must BPM Be Adapted?
The discussion will show how a BPM system must be
customized to be enabled for the interplay with IoT.
We will again use the BPM lifecycle for classifying
the adjustment tasks. We distinguish between neces-
sary conceptual and technological adaptations. The
different aspects are summarized in Table 1.
First, we focus on the modelling phase of the life-
cycle where certain conceptual adaptations are neces-
sary to incorporate IoT devices and sensors into pro-
cess models. Here, it is potentially necessary to re-
engineer existing process models, i.e., activities need
to be added, removed or rearranged, data objects re-
flecting IoT values need to be added and organisa-
tional dependencies redefined. These adaptations re-
flect the inclusion of new entities stemming from IoT
technology. This re-engineering can even go to such
lengths that the used modelling language is extended
with new modelling elements to ensure understand-
ability of models. In this paper, we will tackle this is-
sue in Sec. 4. Furthermore, it is important to mention
that IoT technology introduces new dimensions into
BPM scenarios that have not been covered by existing
languages yet and thus require semantical enrichment
of languages. For instance, the assignment of activi-
ties to the closest staff member, i.e., a location depen-
dent assignment condition can easily be implemented
with IoT devices, however, is not covered in standard
languages like BPMN. The execution phase requires
several adjustments, both on a conceptual as well as
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