IEEE Systems, Man and Cybernetics Magazine - October 2020 - 40

facilitates deriving situational knowledge when tracking
and evaluating data streams. Situational knowledge, in
turn, is input to analyze prospective knowledge, which
constitutes a dynamic task. Prospective knowledge
addresses long-tail information about resources (How
well is the person/thing doing? Are there any behavior
changes expected?). Moreover, data streams from sensors
need to be tracked, mapped to information entities, and
simulated. Additionally, the output (goal) must be known
(e.g., save time, save costs, and improve health), and its
derivation and the reconciliation of private goals must be
mapped with organizational targets, which is a challenge
of the IoT. An alignment between event-based and process-oriented systems is indispensable in this context. A
starting point could be to define goal-based deviation patterns and provide modeling techniques that consider sensor and event data.
Specifying the Autonomy Level of Things
Objects in the IoT are able to react to events by executing tasks and entire processes. The execution of the latter procedures is typically asynchronous and sometimes
not explicitly begun from a central coordinator. The execution of tasks and processes may further trigger certain
reactions, for example, the start of another process to
correct deviating behavior. Yet, it is unfeasible to grant
things full autonomy to decide everything without supervision. Hence, there has to be a concept of autonomy levels that dictates whether things, be they individuals or in
a group, need supervision and whether the things' decisions may be vetoed. Currently, there is no universal way
to represent these levels of autonomy and resolve conflicts originating from this distinction [11], [12]. While
different conceptualizations of individual and group
autonomy exist, they have not yet been transferred to
BPM and the IoT.
Specifying the "Social" Role of Agents
Institutions aim to optimize their business processes
based on organizational (i.e., group) goals. However, process participants often follow personal, i.e., individual,
processes and agendas. The challenge is to synchronize/
reconcile different, possibly conflicting goals. These agendas are typically mitigated through governance processes
that prescribe desired behaviors. The individual goals of a
thing are typically not precisely known and explicitly
given. Furthermore, these processes may be less prescriptive microprocesses and habits. Hence, holistic and prescriptive governance may not be possible. Thus, it is an
option to define and specify the "social" behavior of things
(such as self-interest, helping, and being cooperative [13])
to better coordinate and govern their behaviors. This
becomes even more challenging with the integration of
human actors as well as robots in processes (raising issues
such as exchangeability, the coexistence of different kinds
of resources, and so on).
40

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE O ctober 2020

Concretizing Abstract Process Models
Abstract models are sometimes used to replicate processes at design time without providing the details that are
necessary for execution. This is a sensible approach when
dealing with very dynamic scenarios. In these cases, it is
possible to define the process, but the abstract model has
to be turned into a concrete version before it is executable,
for example, by discovering available services as well as
the conditions in which the services may be used. The context also includes physical data about users, e.g., location
and the devices a user carries with him or her (such as
smartphones). For the discovery phase, the semantics
related to the services (i.e., what functionality the service
can offer, especially within the context of the process)
should be available, and it should be possible to reason
through this for matchmaking purposes. In addition, the
services' discovery phase may lead to changes in the schema of the original abstract process. Examples of corresponding changes include the skipping of certain tasks
that were initially planned for a process and the addition
of new fragments (e.g., combining two or more services
either in sequence or in parallel to achieve the task goal).
Dealing With New Situations
Individual ad hoc decisions may favorably resolve a current situation from an individual's or a small group's point
of view. In a complex business environment, decision making conducted with foresight and structure achieves similar results and saves money and time, possibly improving
overall quality. Deterministic event detection and correlation can be very well modeled and executed with event
processing languages in complex event processing
engines. However, the flexible discovery of new situations
and the derivation of original responses constitute major
technological challenges whose resolutions can benefit
from the combination with BPM.
BPM methodologies and technologies can support the
identification and selection of appropriate responses by
recommending tasks, triggering tasks and whole processes, and automating as well as monitoring task execution.
These reactions can be predefined using existing BPM
technologies, and learning can be based on the analysis of
historic traces to identify beneficial habits from a higherlevel perspective. Furthermore, reference models [14] can
help identify state-of-the-art industry blueprints, which can
be contextualized and instantiated to find a proper reaction for the context and the history of the situation. The
capability of IoT sensing can be of additional benefit here.
Bridging the Gap Between Event-Based and
Process-Based Systems
A challenge is to bridge the gap between clouds of sensor
data and event logs for process mining. Events captured by
sensors are available in high volumes, velocities, and varieties. They are often affected by noise and errors. Process
knowledge can be employed to support the identification



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