IEEE Systems, Man and Cybernetics Magazine - October 2020 - 37

discrepancies between the predefined model and the
actual enactment that harnesses event information for
online process compliance checking and exception
management.
In the remainder of this article, taking these two general questions as starting point, we detail the key points
related to combining BPM and the IoT and elaborate on the
benefits of BPM for the IoT and vice versa. (This article
has its roots in Dagstuhl Seminar 16 191, "Fresh Approaches to Business Process Modeling," organized by Richard
Hull, Agnes Koschmider, Hajo A. Reijers, and William
Wong at the Leibniz Center for Informatics in Germany,
8-13 May 2016 [25], in which many of the authors participated. Moreover, a preliminary version was published at
the Computing Research Repository [26].)
Intersections/Challenges
The IoT has to deal with a number of challenges; these
include, for example, technological barriers, such as the
computational limitations of embedded systems and
connectivity to back-end systems; security-related concerns; standardization and interoperability matters;
data privacy; the untapped potential in data analytics;
efficient methods for system organization; and so on [5].
The principal characteristic of the IoT is the communication between loosely coupled objects, which is mostly
accomplished asynchronously and ad hoc. BPM deals

with the discovery of models, the analysis of predefined
models, the adaptation of models, and the enactment of
business processes through software applications and
systems. Abstract processes can also be discovered
from log files, and suitable implementations for instantiation can be predicted.
Accordingly, sensing and perception via sensors and
decisions based on sensors as well as determinations
based on actuation according to individual goals/strategies constitute fundamental tasks of the IoT. Thereby,
sensing constitutes the input and output actuation of any
IoT-BPM interaction (see Figure 1). In between, raw event
data are processed by event-based systems, transforming
the input events to higher-level knowledge. In turn, the latter may be utilized by BPM concepts, methods, and technologies to deal with the discovery of a (process) model,
the analysis of a predefined model, the adaptation of a
model, and the enactment of a model (a business process).
While the IoT generally focuses on communication and
data flow, BPM approaches consider control flow, process
models (large and "in the large"), and synchronous interactions. In addition, most current BPM approaches have difficulties in dealing with nonroutine, nondeterministic
processes, whereas IoT applications typically involve
these kinds of interactions. Plenty of intersections, which
pose new challenges for researchers and practitioners,
arise, as detailed in the following.

Specifying the
Autonomy Level of Things

Detecting New Process
From Data
Discover

Predict and Adapt

Breaking Down
End-to-End Processes

Concretizing Abstract
Process Models

Managing the Link
Between Microprocesses
Dealing With
Unstructured Environments
Integrating the IoT
With Process Correctness
Checks
Connection of Analytical
Processes With the IoT
Support for Managing
Manually Executed
Physical Processes
Placing Sensors in a
Process-Aware Way

Specifying the "Social"
Role of Agents

Predefined Model

Enact Response

Dealing With New
Situations
Bridging the Gap
Between Event-Based and
Process-Based Systems

Higher-Level Knowledge
Event Processing + Learning

Improving Online
Conformance Checking

Raw Event Data
Sensing
(Physical Objects,
Systems, and Humans)

Actuation
(Physical Objects,
Systems, and Humans)

Improving Resource
Monitoring and the Quality
of Task Execution
Improving Resource
Utilization Optimization

Figure 1. A high-level overview showing the interaction between the IoT and BPM.

O c tob e r 2020

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE

37



IEEE Systems, Man and Cybernetics Magazine - October 2020

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