ArticlePDF Available


The Internet of Things (IoT) refers to a network of connected devices collecting and exchanging data over the Internet. These things can be artificial or natural, and interact as autonomous agents forming a complex system. In turn, Business Process Management (BPM) was established to analyze, discover, design, implement, execute, monitor and evolve collaborative business processes within and across organizations. While the IoT and BPM have been regarded as separate topics in research and practice, we strongly believe that the management of IoT applications will strongly benefit from BPM concepts, methods and technologies on the one hand; on the other one, the IoT poses challenges that will require enhancements and extensions of the current state-of-the-art in the BPM field. In this paper, we question to what extent these two paradigms can be combined and we discuss the emerging challenges.
The Internet-of-Things Meets Business Process
Management: Mutual Benefits and Challenges
Christian Janiesch1, Agnes Koschmider2, Massimo Mecella3, Barbara Weber4, Andrea
Burattin4, Claudio Di Ciccio5, Avigdor Gal6, Udo Kannengiesser7, Felix Mannhardt8, Jan
, Andreas Oberweis
, Manfred Reichert
, Stefanie Rinderle-Ma
, WenZhan Song
Jianwen Su12, Victoria Torres13, Matthias Weidlich14, Mathias Weske15, Liang Zhang16
The Internet of Things (IoT) refers to a network of connected devices collecting and exchanging data over the
Internet. These things can be artificial or natural, and interact as autonomous agents forming a complex system.
In turn, Business Process Management (BPM) was established to analyze, discover, design, implement, execute,
monitor and evolve collaborative business processes within and across organizations. While the IoT and BPM
have been regarded as separate topics in research and practice, we strongly believe that the management of IoT
applications will strongly benefit from BPM concepts, methods and technologies on the one hand; on the other
one, the IoT poses challenges that will require enhancements and extensions of the current state-of-the-art in
the BPM field. In this paper, we question to what extent these two paradigms can be combined and we discuss
the emerging challenges.
IoT (Internet-of-Things) — BPM (Business Process Management) — Challenges — Manifesto
1University of W¨
urzburg, Germany christian.janiesch@uni-
2Karlsruhe Institute of Technology, Germany
3Sapienza Universit`
a di Roma, Italy
4Technical University of Denmark, Denmark
5WU Vienna, Austria
6Technion – Israel Institute of Technology, Israel
7Metasonic GmbH, Germany
8SINTEF, Trondheim, Norway
9Ulm University, Germany
10Universit ¨
at Wien, Austria
11University of Georgia, USA
12University of California at Santa Barbara, USA
13Technical University of Valencia, Spain
14Humboldt-Universit ¨
at zu Berlin, Germany
15University of Potsdam, Germany
16Fudan University, China
1Introduction 1
2Background 2
2.1 Introduction to the Internet of Things . . . . . . . . . 2
2.2 Introduction to Business Process Management . 3
3Challenges 3
3.1 Overview of Challenges ..................3
3.2 Challenges in Detail .....................4
4Concluding remarks 7
References 8
1. Introduction
Our world is increasingly linked through a large number of
connected devices, typically embedded in electrical / electron-
ical components and equipped with sensors and actuators, that
enabling sensing, (re-)acting, collecting and exchange data
via various communication networks including the Internet:
the Internet of Things (IoT). As such, it enables continuous
monitoring of phenomena based on sensing devices (wear-
able devices, beacons, smartphones, machine sensors, etc.) as
well as analytics opportunities in smart environments (smart
homes, connected cars, smart logistics, Industry 4.0, etc.) and
the possibility to actuate feedback. Therefore, the IoT con-
tributes to the recent trend known as big data, being one of
The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges — 2/9
the three main sources besides human sourced and process
mediated data.
Business processes represent a specific ordering of tasks
and activities across time and place to serve a business goal.
Process analytics, execution and monitoring based on IoT data
can enable an even more comprehensive view of processes
and realize unused potential for process optimization. As an
example, in the past process analytics and in particular pro-
cess mining has been hampered by the fact that process are
often incomplete or erroneous; with the IoT producing a large
amount of data stored in the cloud, even more data become
available for analysis, possibly resolving issues of incomplete-
ness and enabling providing error correction methods based
on multiple data items [4].
In the literature, several works are emerging on combining
Business Process Management (BPM) and IoT, e.g., utilizing
sensor data to enable the actuation of services [
] or adapting
running business processes to continuously align them with
the state of the things (e.g., assets, humans, and machines).
Still, there are many open challenges to be tackled (cf. Figure
1). Both BPM and IoT will benefit from a wider integration.
How IoT can benefit from BPM?
Let us consider a com-
plex system with multiple components interacting within a
smart environment being aware of the components’ locations,
movements, and interactions. Such a system can be a smart
factory with autonomous robots, a retirement home with con-
nected residents, or, at a larger scale, a smart city. While
the parties in the system can track the movements of each
component and also relate multiple components’ behaviors to
each other, they do not know the components’ agendas. Often
their interactions are based on habits, i.e., routine low-level
processes, which represent recurring tasks. Some of these
routines are more time and cost critical than others, some
may be dangerous or endanger others, and some may just be
inefficient or superfluous. Knowing their agendas, their goals,
and their procedures can enable a better basis for planning,
execution, and safety.
How BPM can benefit from IoT?
Let us consider a com-
plex process with multiple parties interacting in the context
of a business transaction. Such a process can be, for example,
a procurement process, where goods are ordered, delivered,
stored, and paid for. While the system can track each auto-
matically executed activity on its own, it relies on messages
from other parties and manually entered data in the case of
manual activities. If this data is not entered or entered incor-
rectly, discrepancies between the digital (i.e., computerized
representation of the) process and the real-world execution
of the process occur. Similar concerns hold if the process
participants do not obey the digital process under certain cir-
cumstances, e.g., an emergency in healthcare, or have not
entered the data yet though in the real-world process the re-
spective activity was already executed.
Such scenarios might be better manageable when closely
linking the digital process with the physical world as enabled
by the integration of IoT and BPM; e.g., the completion of
manual activities can be made observable through usage of
appropriate sensors. IoT can complete BPM with continuous
data sensing and physical actuation for improved decision
making. Decisions in processes require relevant information
as basis for making meaningful decisions. In general, it is not
sufficient to retrieve this data solely from traditional reposito-
ries (e.g., databases and data warehouse) providing historical
data, but also up-to-date data are needed. Data from the IoT,
such as events, provided through in-memory databases or
complex event processing can be useful in this context. The
IoT could reduce the need to manually signify the completion
of manual tasks since sensor data is already available, leading
to more accurate data, reduced errors, and efficiency gains.
To ensure that both domains can mutually benefit from
each other, still exist several challenges to be tackled. Particu-
larly, it has to be understood:
how processes can improve the IoT by (i) taking a
process-oriented perspective and considering the pro-
cess history to (ii) bridge the abstraction gap between
raw sensor data and higher level knowledge extracted
from this event data, and to (iii) optimize the decision-
making in the large;
how to exploit IoT for BPM by (i) considering sensor
data for automatically detecting the start and end of
activities, (ii) using event data for making decisions
in a pre-defined process model, and (iii) detecting dis-
crepancies between the pre-defined model and actual
enactment using event data for online process compli-
ance checking and exception management.
In the following of this paper
, taking these two general
questions as starting point, we discuss IoT and BPM back-
grounds in Section 2. Then in in Section 3, we detail the
key challenges in combining BPM and IoT and elaborate on
benefits of BPM for IoT and IoT for BPM, before offering
concluding remarks in Section 4.
2. Background
2.1 Introduction to the Internet of Things
The Internet of Things (IoT) [
] is the inter-networking of
physical objects (the things), being such things embedded sys-
tems with electronics hardware, software, sensors, actuators,
and network connectivity. Such connected things collect and
exchange data. Each thing is uniquely identifiable through
its embedded computing system and is able to interoperate
within the existing network infrastructure. While things act
local, the IoT allows things to be controlled remotely across
existing network infrastructures, including the Internet.
This paper has its roots in the Dagstuhl Seminar 16191 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, May 8–13, 2016, cf.
volltexte/2016/6696/, to which many authors participated.
The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges — 3/9
Pre‐definedModel EnactResponse
Discover PredictandAdapt
Figure 1. High-level overview showing the interaction between IoT and BPM. The numbering used in the blocks will
correspond to the numbering of the different paragraphs in the following sections.
The interconnection of these smart objects/things is ex-
pected to usher in automation in nearly all fields. This creates
opportunities for more direct integration of the physical world
into computer-based and digitized systems, and results in im-
proved efficiency, accuracy, and economic benefits besides
increased automation and reduced human intervention. Ex-
perts estimate that the IoT will consist of about 30 billion
objects by 2020 [22].
2.2 Introduction to Business Process Management
Business Process Management is a well-established disci-
pline that deals with the identification, discovery, analysis,
(re-)design, implementation, execution, monitoring, and evo-
lution of business processes [
]. A business process is a col-
lection of related events, activities, and decisions that involve
a number of actors and resources and that collectively lead to
an outcome that is of value for an organization or a customer.
]. Examples of business processes include order-to-cash,
procure-to-pay, application-to-approval, claim-to-settlement,
or fault-to-resolution. To support business processes at an
operational level, a BPM system (BPMS) can be used [
As opposed to data- or function-centered information systems,
a BPMS separates process logic from application code and,
thus, provides an additional architectural layer. Typically, a
BPMS provides generic services necessary for operational,
software-enabled business process support, i.e., for process
modeling, process execution, process monitoring, and user
interaction (e.g., worklist management). When using a BPMS,
software-enabled business processes are designed in a top-
down manner, i.e., process logic is explicitly described in
terms of a process model providing the schema for process
execution. The BPMS is responsible for instantiating new
process instances, for controlling their execution based on the
process model, and for completing them. The progress of a
process instance is typically monitored and traces of execution
are stored in an event log and can be used for process mining
], e.g., the discovery of a process model from the event log
or for checking the compliance of the log with a given process
So far, the predominant paradigm to develop operational
support for business processes has been based on the Model-
Enact paradigm, where the business process has been depicted
as a (graphical) process model, which then could be executed
by a BPMS. This largely follows a top-down approach and is
based on the idea of a central orchestrator that controls the exe-
cution of the business process, its data, and its resources. With
the emergence of IoT, the existing Model-Enact paradigm is
challenged by the Discover-Predict paradigm; it can be charac-
terized as a bottom-up approach where data is generated from
physical devices sensing their environment and producing raw
events. Sensor data then must be aggregated and interpreted in
order to detect activities that can be used as input for process
mining algorithms supporting decision-making [32].
3. Challenges
This section describes the challenges of interaction between
the IoT and the BPM paradigm.
3.1 Overview of Challenges
The IoT has to deal with a number of challenges; this includes,
for example, technological barriers such as computational
limitations of embedded systems or the connectivity to back-
end systems, security-related issues, a lack of standards, data
The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges — 4/9
privacy issues, untapped potential in data analytics, efficient
methods for the organization of IoT systems, etc. [
]. The
principal characteristic of the IoT is the communication be-
tween loosely-coupled objects, which mostly is accomplished
asynchronously and ad-hoc.
BPM deals with the discovery of models, the analysis
of pre-defined models, the adaptation of models, and the
enactment of business processes. We consider processes as
explicit process representations (pre-defined models), which
later are enacted. Abstract processes can also be discovered
from log files and suitable implementations for instantiation
can be predicted.
Accordingly, sensing and perception via sensors and deci-
sion based on sensors as well as decision based on actuation
according to individual goals/strategies constitute fundamen-
tal tasks of the IoT. Thereby, sensing constitutes the input
and actuation the output of any IoT-BPM interaction (see also
Figure 1). In between, raw event data is processed by event-
based systems, transforming the input events to higher-level
knowledge. In turn, the latter may be utilized by BPM con-
cepts, methods or technologies to deal with the discovery of a
model, the analysis of a pre-defined model, the adaptation of
a model and the enactment of business processes.
While the IoT generally focuses on communication and
data flow, BPM approaches consider control flow, big mono-
lithic process models, and synchronous interactions. In addi-
tion, most BPM approaches have trouble dealing with non-
routine, non-deterministic processes, whereas IoT applica-
tions typically involve these kind of interactions.
Against these considerations, plenty of challenges arise,
which need be considered when improving business inter-
actions as well as alignment between BPM and IoT. In the
following we introduce each challenge in detail.
3.2 Challenges in Detail
C 1 – Placing sensors in a process-aware way.
In order
to collect all relevant data, sensors need to be carefully placed.
It constitutes already a challenge to construct sensors and
place them on agents (human or artificial) or in a smart spaces,
such that they are non-intrusive but still efficient: sensors
can be battery-less tags such as RFID, battery and renewable
energy powered, or outlet-powered; and the communication
methods can be wired or wireless. It is even more challenging
to decide on the type of sensor and its placement with regard
to its function in respect to the interaction between agents [
A business process (model) may guide this placement since
it offers knowledge about resources, locations and variants
of behavior (enact), that need to be covered. As well, the
trade-off between the cost of introducing additional sensing
points and the expected increase in monitoring accuracy may
be approached based on process knowledge.
C 2 – Visualization support for managing manually ex-
ecuted, physical processes.
In many settings, BPM ap-
proaches are used to automate processes through the support
of a BPMS, in which some activities require the interplay
between human operators and software/hardware modules.
Notable examples, in addition to logistic and industrial pro-
cesses, include disaster management (e.g., a BPMS in the field
to coordinate emergency teams [
]) and healthcare processes
(e.g., [
], or [
] – a BPMS coordinating doctors and nurses
with vocal interfaces for human tasks). In many of these sce-
nario, there is an increasing use of mobile devices fostering
the delivery of work items to the right users [26].
In these settings, workers do not necessarily have to in-
teract with the BPMS while carrying out physical tasks (e.g.,
moving boxes in a warehouse): sensors, which are connected
to the BPMS, monitor whether or not such a task has started
or ended. However, appropriate mapping from process activi-
ties to the GUI and usable visualizations are needed allowing
actors (process participants) to perform their work in a natural
way, without requiring non-value adding management tasks
such as clicking on confirmation buttons.
C 3 – Connection of analytical processes with IoT.
ing process execution, a variety of information is required to
make meaningful decisions. In turn, this information often
needs to be available not only from traditional databases/data
warehouses providing historical data, but it needs to be up-to-
date and current. In order to design systems providing such
up-to-date information, and to judge the quality of the data
analysis results from such applications, it needs to be clear
where the data stems from and where it has been used (data
provenance), as well as the overall quality of the data at-hand
needs to be ensured. This is particularly critical at the presence
of big data [
]. Generally, it becomes necessary to find a way
to annotate the data’s origin and use this (meta-)information
in process models. So far, there is no universal method to
connect the analytic processes of observation, analysis, and
decision-making to business processes in a standardized way,
]; recent attempts include the Decision Model and No-
tation (DMN)
standard. Its focus, however, is on decision
requirements, but less on the origin and use of decision data.
Hence, it still needs to be investigated how to model quality
and provenance in order to be exploitable at the process model
Erroneous sensors, not working at all or delivering erro-
neous data, need to be discovered and excluded from any
analysis. In turn, this necessitates a reasonable judgment on
which sensor data might be erroneous. Here, the process con-
text in which these data occur might be helpful to identify
erroneous sensors as well as to cope with them.
C 4 – Integrating the IoT with process correctness checks.
Well-known techniques for analyzing the quality of process
models can contribute to improve the design of interactions
in IoT, by finding deadlocks, livelocks, or dead activities in
respect to the behavior or interactions of objects [
]. Dead-
locks and livelocks are reasons why some processes may not
terminate in the assumed time frame or not at all. It can either
occur because certain actions are waiting for each other or
when actions change states but the process does not progress.
While a rollback is a typical service in data management, it
The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges — 5/9
becomes much more costly and complicated when managing
processes and thus should be avoided. Dead activities do not
harm a processes execution (unless they are supposed to be
mandatory) since they will never be triggered. Yet, they rep-
resent a waste of resources as either or both, physical and/or
virtual resources may have been reserved for this activity.
Therefore, designing correct process models which specif-
ically consider the IoT nature of some components becomes
crucial, as well as the verification of important properties.
C 5 – Dealing with unstructured environments.
BPM of-
fers a way to structure businesses. As such, it often assumes
a controlled environment with a managed repository of ver-
sioned processes that can be orchestrated for the purpose of a
single enterprise or be choreographed between parties in case
of cross-organizational collaborations. Orchestration denomi-
nates the execution order of the interactions from the perspec-
tive and under control of a single party, whereas choreography
describes public, i.e., globally visible, message exchanges,
interaction rules and agreements made among multiple parties.
Both concepts presume knowledge about the structure and/or
interactions of each participating process. It is questionable
whether orchestration and choreography still suffice as orga-
nizational concepts in an IoT world, which is much more ad
hoc and situative (e.g., devices involved in the interaction
might fail, deliver erroneous data, new devices may have to
be flexibly added, etc.).
C 6 – Managing the links between micro processes.
approach to bridge the gap between IoT data and processes,
would be to break end-to-end process models into micro pro-
cesses representing habits and arrange them in a less pre-
scriptive (control-flow) way. Modeling a small and possibly
autonomous micro process does not necessarily require new
modeling constructs or methods. Yet, the organization of hun-
dreds/thousands of loosely coupled small processes is a task
that cannot be left to the forces of natural evolution in a busi-
ness environment. It may require new modeling constructs
and methods to structure and represent their non-hierarchical
interaction in human-readable form [21].
Data-centric process paradigms offer promising perspec-
tives in this context. For example, object-aware processes
] describe the behavior of single objects through micro
processes, whereas the dynamic construction of linked objects
as well as the their synchronized execution is described and
enforced through macro processes. However, respective ap-
proaches need to be enhanced to integrate physical objects as
well as their behavior in the overall process.
C 7 – Breaking down end-to-end processes.
For a large
class of processes (typically referred to as dynamic or
knowledge-intensive), the advent of overwhelming sensor
data and things acting in the environment without central con-
trol but according to “personal” agendas, makes it practically
impossible to define comprehensive end-to-end process mod-
els. Things will perform their own routines, so called repeated
behaviour patterns or habits [
]. Accordingly, processes
will have to be organized as event-driven micro processes to
represent these habits. Whereas the overall end-to-end busi-
ness process itself may be modeled in traditional ways, the
linking of micro-process models is far more complex; to cope
with this emerging complexity, the possible interactions be-
tween micro-process models must not be described at the low
level of message exchanges, but be put at a higher semantical
level, similar to the utilization of semantic object relations
for the purpose of object interactions in object-aware process
C 8 – Detecting new processes from data.
Designing a
system in a bottom-up manner without prescriptive process
models promises more flexible and inclusive processes. How-
ever, 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-organising process will take
place in some respect. Thus, one must find the appropriate
level of structuring and prescription without harming the ca-
pability to self-organize. There is a gap between IoT data
and concepts at a model level to enable behavior prediction
and to identify changes in behavior. The IoT allows deriv-
ing 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 (e.g., 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 en-
tities, and simulated. Additionally, the output (goal) must
be known (e.g., save time, save costs, improve health) and
its derivation as well as the reconciliation of private goals
must be mapped with organizational goals, which in turn is a
challenge of the IoT. An alignment between event-based and
process-oriented systems indispensable in this context
starting point could be to define goal-based deviation patterns
and to provide modeling techniques considering sensor-data
and event data.
C 9 – Specifying the autonomy level of IoT things.
jects in the IoT are able to react to events by executing tasks or
entire processes. The execution of the latter is typically asyn-
chronous and sometimes not explicitly started from a central
coordinator. The execution of tasks or processes may further
trigger certain reactions, for example the start of another pro-
cess to correct deviating behavior. Yet, it is unfeasible to grant
things full autonomy to decide everything without supervi-
sion. Hence, there has to be a concept of autonomy levels that
dictate if things need supervision and may be vetoed, be it
an individual or a group. Currently, there is no universal way
to talk about these levels of autonomy or to resolve conflicts
originating from this distinction [
]. While different con-
ceptualizations of individual and group autonomy exist, they
have not been transferred to BPM yet. Moreover, there is a
lack of understanding on how to express them in a business
Alignments between both system types were discussed at a Dagstuhl
The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges — 6/9
process model, e.g. using patterns or further attributes.
C 10 – Specifying the “social” roles of agents.
tions aim to optimize their business processes based on orga-
nizational (i.e., group) goals. However, process participants
often follow personal, i.e., individual processes or agendas
with individual goals. The challenge is to synchronize/ recon-
cile different, possibly conflicting goals. These agendas are
typically mitigated through governance processes prescribing
desired behavior. The individual goals of a thing are typically
not precisely known or explicitly given. Furthermore, these
processes may be less prescriptive micro processes or habits.
Hence, holistic and prescritive governance may not be possi-
ble. Hence, it is an option to define and specify social behavior
of things (such as self-interest, helpful, cooperative [
]) to
better coordinate and govern their behavior. This becomes
even more challenging, when also considering robotics, i.e.,
the integration of human actors as well as robots in processes
(raising issues like exchangeability, co-existence of different
kinds of resources etc.).
C 11 – Concretizing abstract process models.
process models are sometimes used to model processes at
design time without providing the details necessary for exe-
cution. 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
model later before being executable, for example by discover-
ing available services as well as the conditions in which these
services may be used. Context also includes physical data
about users, e.g., location, devices the user carries with him
(e.g., smartphone), etc. For the discovery phase (see Figure 1),
the semantics related to the services (i.e., what functionality
can the service offer specially within the context of the pro-
cess) should be available and it should be possible to reason
over 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 initially planned in the
process or the addition of new fragments (e.g., combining two
or more services either in sequence or parallel to achieve the
task goal). Despite all flexibility, this phase of instantiation
presumes a given structure in form of the abstract process
C 12 – Dealing with new situations
Individual ad hoc de-
cisions may resolve a current situation from an individual’s
or a small group’s point of view towards favorable results
for them. In a complex business environment, foresightful
and structured decision making cannot only achieve similar
results but also save costs and time, and possibly improve the
total 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 new
responses constitute major technological challenges whose
tackling can benefit from the combination with BPM.
BPM methodologies and technologies can support the
identification and selection of appropriate responses by rec-
ommending tasks, triggering tasks or whole processes, and
automating as well as monitoring their execution. These re-
actions can be pre-defined using existing BPM technologies
and learning can be based on the analysis of historic traces
to identify beneficial habits from a higher level perspective.
Furthermore, reference models can help to 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.
C 13 – Bridging the gap between event-based and pro-
cess-based systems.
A challenge is to bridge the gap be-
tween clouds of sensor data and event logs for process mining.
Events captured by sensors are available in high volume, ve-
locity, and variety. They are often affected by noise and errors.
Process knowledge can be employed to support the identi-
fication of events from raw event data and in a subsequent
step entire processes including their activities from event data.
This is a non-trivial problem since event data belonging to
different activities can be interleaving. Moreover, event data
can belong to or be relevant for several activities, so that com-
plex n:m relations between events and activities have to be
considered. The question of mapping start and end of event
(streams) to the start and end of a process is closely related.
Once the activities have been discovered, 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 can support the discovery
of these habits, the identification of their underlying interac-
tions as processes as well as the optimization of these habits to
reduce the waste of 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 an event log that organizes the events in terms of
traces representing the execution of a process instance [
Similarly, but in an on-line fashion, complex event process-
ing can be used to derive higher level knowledge from raw
events to provide an ex nunc perspective [
]. Here, the timely
provisioning of events is crucial.
C 14 – Improving online conformance checking.
As de-
tailed earlier, conformance checking is a process mining tech-
nique that compares an existing process model with an event
log of the same process. It can be used to check if the re-
ality of process execution, as recorded in the log, conforms
to the model and vice versa. Online conformance checking
takes as input the context data and performs the comparison
online. This requires high quality data and almost complete
information. Again, the IoT as a data source and data man-
agement technology 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 data. Similarly, the checking and
monitoring of compliance rules to be obeyed during process
execution might benefit from IoT data sources and data man-
agement technologies.
The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges — 7/9
C 15 – Improving resource utilization optimization.
can provide a governance structure for an organization, be it
physical or virtual. BPM initiatives break up traditional func-
tional silos and introduce process managers being responsible
for processes across departments. While complex systems
and the IoT is centered around situations to react to, BPM ini-
tiatives are organized around processes. This entails that some
coordination instance responsible for priorities and resource
provisioning can monitor and intervene with additional knowl-
edge if necessary. In a pure IoT paradigm, there is the danger
that decision will only produce local optima. Research has
shown, for example, that situational decisions about resource
provisioning as it is common in virtualized environments,
e.g., CPU usage, can be optimized from a process perspective
through BPM knowledge when executing known procedures
or behavior patterns [
]. Vice versa, research has also
shown that process knowledge can optimize event processing
]. The coordinating unit responsible for resource provi-
sioning has advanced knowledge about the future behavior
of agents since they have to follow their process models and,
thus, can provide resources (e.g., computing power, network
bandwidth, or things) with greater accuracy reducing pro-
cessing time and thus increasing the throughput of a process.
It also helps to reduce communication time-outs and thus,
rollbacks, or abnormal process terminations.
C 16 – Improving resource monitoring and quality of task
The execution of tasks in a business process con-
sumes resources. These can be IT, such as storage capacity for
process data, computing power for calculations in scientific
workflows, artificial agents, such as as robots automatically
executing manual tasks, or human beings entering or analyz-
ing data or performing manual tasks. Also machines, e.g.,
packing drugs, can be considered as resources (e.g., predictive
monitoring, i.e., when does the machine have to be maintained
taking its usage as well as historical data into account).
All these resources migth suffer from issues, which hinder
optimal working conditions such as over- or under-utilization
or even damage/ illness. IoT-based sensors can pick up these
issues by measuring machine-behavior or human stress lev-
els [
] and suggest changes to process execution to alleviate
these 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
or training materials on how to execute the task, or regula-
tions that are relevant for the user’s particular context. Sensor
data can be leveraged to determine the actual context [
and to identify information needs (e.g., detection of cognitive
overload or stress).
4. Concluding remarks
The IoT provides many opportunities for industry as well as
for personal use through the meaningful, yet dynamic inter-
action of humans, software, machines, and things. BPM is
a well established discipline that deals with the discovery,
analysis, (re-)design, implementation, execution, monitoring,
controlling and evolution of business processes.
So far, both areas have been considered separately. In this
we have formulated a number of challenges for the
amalgamation of the IoT and BPM, which we deem important
to be tackled in the near future in order for the IoT to benefit
from business processes and vice-versa.
Before concluding, we would like to highlight a cross-
issue, i.e., dealing with security and, in particular, privacy
issues. For example, privacy levels that exist at the sensors
level might be different with respect to those at the BPM
side. A full-disclosure approach should be avoided, espe-
cially in contexts where sensitive (i.e., personal) information
is collected. The most relevant challenge, in this case, is
the communication between the two worlds, each of them
with corresponding privacy/security levels and policies. The
layer in charge of integrating these two sides should be de-
signed according to the principles of privacy by design [
“identify and examine possible data protection problems when
designing new technology and to incorporate privacy protec-
tion into the overall design, instead of having to come up
with laborious and time-consuming “patches” later on” [
This issues can also be seen as a “non-functional requirement”
referring to C1, C3, C4, C6, C8, C13, and C14, but also other
challenges might be affected. Fibally, partially related to the
previous point, are ethical aspects of the integration of IoT
and BPM: the introduction of raw events paves the way to
a whole new set of analyses and explorations. On the one
hand, these analyses must preserve the privacy of the individ-
ual (privacy is recognized as a fundamental right
.). At the
same time, the analyses should not be unfair and should not
provide unequal treatment of people based on membership to
a category or a minority. This problem is typically referred to
as “discrimination-aware data mining” [
]. More generally,
the literature also talks about “privacy-preserving data mining”
]. There are several challenges that are directly affected
by that such as C2-C6 and C13-C15. This is due to the set
of analyses that the integration of IoT and BPM will make
This paper is a living document. All persons willing to provide feedbacks
and improvements are welcome to contact the authors.
Cf. Article 8 of “European Convention on Human Rights”
and Article 12 of the “Universal Declaration of Human Rights”
The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges — 8/9
M. T. P. Adam, H. Gimpel, A. Maedche, and R. Riedl.
Design blueprint for stress-sensitive adaptive enterprise
systems. Business & Information Systems Engineering,
pages 1–15, 2016.
R. Agrawal and R. Srikant. Privacy-preserving data min-
ing. SIGMOD Rec., 29(2):439–450, May 2000.
K. Ashton. That ‘Internet of Things’ Thing. RFiD
C. Batini and M. Scannapieco. Data and Information
Quality - Dimensions, Principles and Techniques. Data-
Centric Systems and Applications. Springer, 2016.
T. Catarci, M. de Leoni, A. Marrella, M. Mecella, B. Sal-
vatore, G. Vetere, S. Dustdar, L. Juszczyk, A. Manzoor,
and H. L. Truong. Pervasive software environments for
supporting disaster responses. IEEE Internet Computing,
12(1):26–37, 2008.
F. Cossu, A. Marrella, M. Mecella, A. Russo, S. Kimani,
G. Bertazzoni, A. Colabianchi, A. Corona, A. De Luise,
F. Grasso, and M. Suppa. Supporting doctors through
mobile multimodal interaction and process-aware exe-
cution of clinical guidelines. In 7th IEEE International
Conference on Service-Oriented Computing and Appli-
cations, SOCA 2014, Matsue, Japan, November 17-19,
2014, pages 183–190. IEEE Computer Society, 2014.
M. Dumas, M. La Rosa, J. Mendling, and H. A. Rei-
jers. Fundamentals of Business Process Management.
Springer Publishing Company, Incorporated, 2013.
S. Euting, C. Janiesch, R. Fischer, S. Tai, and I. Weber.
Scalable business process execution in the cloud. In 2nd
IEEE Conference on Cloud Engineering (IC2E), pages
175–184. IEEE, 2014.
D. Firmani, M. Mecella, M. Scannapieco, and C. Ba-
tini. On the meaningfulness of “big data quality”. Data
Science and Engineering, 1(1):6–20, 2016.
G. Grambow, R. Oberhauser, and M. Reichert. Context-
aware and process-centric knowledge provisioning: An
example from the software development domain. In
L. Razmerita, G. E. Phillips-Wren, and L. C. Jain, editors,
Innovations in Knowledge Management - The Impact of
Social Media, Semantic Web and Cloud Computing, vol-
ume 95 of Intelligent Systems Reference Library, pages
179–209. Springer, 2016.
J. Gubbia, R. Buyyab, S. Marusica, and M. Palaniswamia.
Internet of things (iot): A vision, architectural elements,
and future directions. Future Generation Computer Sys-
tems, 29(7):1645–1660, 2013.
J. H
oller, V. Tsiatsis, C. Mulligan, S. Avesand, and
D. Boyle. From Machine-to-Machine to the Internet
of Things - Introduction to a New Age of Intelligence.
Academic Press, 2014.
R. Hull, A. Koschmider, H. A. Reijers, and W. Wong.
Fresh Approaches to Business Process Modeling
(Dagstuhl Seminar 16191). Dagstuhl Reports, 6(5):1–
30, 2016.
C. Janiesch and J. Diebold. Conceptual modeling of event
processing networks. In 24th European Conference on
Information Systems (ECIS), pages 1–15. AIS, 2016.
C. Janiesch, M. Matzner, and O. M
uller. Beyond process
monitoring: A proof-of-concept of event-driven business
activity management. Business Process Management
Journal, 18(4):625–643, 2012.
C. Janiesch, I. Weber, M. Menzel, and J. Kuhlenkamp.
Optimizing the performance of automated business pro-
cesses executed on virtualized infrastructure. In 47th
Hawaii International Conference on System Sciences
(HICSS), pages 3818–3826. IEEE, 2014.
S. Kalenka and N. Jennings. Socially responsible decision
making by autonomous agents. In 5th International Col-
loquium on Cognitive Science, pages 135–149. Springer,
A. Kokkonen and W. Bandara. Expertise in business pro-
cess management. In J. vom Brocke and M. Rosemann,
editors, Handbook on Business Process Management,
volume 2, pages 517–546. Springer, Berlin, 2010.
V. K
unzle and M. Reichert. Philharmonicflows: towards
a framework for object-aware process management. Jour-
nal of Software Maintenance, 23(4):205–244, 2011.
M. Langheinrich. Privacy by design - principles of
privacy-aware ubiquitous systems. In Proceedings of
the 3rd International Conference on Ubiquitous Com-
puting, UbiComp ’01, pages 273–291. Springer-Verlag,
M. Lincoln and A. Gal. Searching business process repos-
itories using operational similarity. In On the Move to
Meaningful Internet Systems: OTM 2011 - Confeder-
ated International Conferences: CoopIS, DOA-SVI, and
ODBASE 2011, Hersonissos, Crete, Greece, October 17-
21, 2011, Proceedings, Part I, pages 2–19, 2011.
A. Nordrum. Popular Internet of Things Fore-
cast of 50 Billion Devices by 2020 Is Outdated.
Spectrum Tech Talk, 18 August 2016.
R. Parasuraman, T. Sheridan, and C. Wickens. A model
for types and levels of human interaction with automation.
IEEE Transactions on Systems, Man and Cybernetics -
Part A: Systems and Humans, 30(3):286–297, 2000.
D. Pedreshi, S. Ruggieri, and F. Turini. Discrimination-
aware data mining. In Proceedings of the 14th ACM
SIGKDD International Conference on Knowledge Discov-
ery and Data Mining, KDD ’08, pages 560–568. ACM,
The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges — 9/9
R. Pryss, N. Mundbrod, D. Langer, and M. Reichert.
Supporting medical ward rounds through mobile task
and process management. Information Systems and e-
Business Management, 13(1):107–146, 2015.
R. Pryss and M. Reichert. Robust execution of mobile
activities in process-aware information systems. IJISMD,
7(4):50–82, 2016.
M. Reichert and B. Weber. Enabling Flexibility in
Process-Aware Information Systems - Challenges, Meth-
ods, Technologies. Springer, 2012.
P. Schaar. Privacy by design. Identity in the Information
Society, 3(2):267–274, Aug 2010.
M. Schillo and K. Fischer. A taxonomy of autonomy in
multiagent organisation. In Agents and Computational
Autonomy. LNCS, volume 2969, pages 68–82. Springer,
New York, NY, 2004.
F. Stertz, J. Mangler, and S. Rinderle-Ma. NFC-Based
Task Enactment for Automatic Documentation of Treat-
ment Processes. In Enterprise, Business-Process and
Information Systems Modeling - 18th International Con-
ference, BPMDS 2017, 22nd International Conference,
EMMSAD 2017, Essen, Germany, June 12-13, 2017, Pro-
ceedings, pages 34–48, 2017.
J. Vaidya, Y. Zhu, and C. W. Clifton. Privacy Preserving
Data Mining, volume 19 of Advances in Information
Security. Springer, 2006.
W. M. P. van der Aalst. Process Mining: Discovery,
Conformance and Enhancement of Business Processes.
Springer Publishing Company, Incorporated, 1st edition,
M. Weidlich, H. Ziekow, A. Gal, J. Mendling, and
M. Weske. Optimizing event pattern matching using
business process models. IEEE Trans. Knowl. Data Eng.,
26(11):2759–2773, 2014.
... Moreover, the generation and use of comprehensive process data and the connection of process entities can be used to improve all types of business processes and thus optimize value creation (Del Giudice, 2016). Therefore, the integration of IoT technology into existing business processes can lead to beneficial Business Process Improvements (BPI) that are highly relevant for process-oriented organizations (Janiesch, 2020). For instance, equipping in-stock products with simple radio-frequency identification (RFID) tags can fundamentally enhance the traceability of warehouse processes and enable manifold further opportunities for improving downstream operations. ...
... Despite IoT's capabilities to enhance BPI and therefore sustainably optimize the organization's overall performance, there is a lack of research regarding IoT-based BPI. Among the limited number of contributions, Janiesch al. (2020) created an overview of existing research and remaining challenges. Here, especially the need for further research on how to benefit from the integration of IoT into business processes has been highlighted. ...
Conference Paper
The number of Internet of Things (IoT) devices is constantly growing across all areas of private and professional life. Especially industrial organizations are increasingly recognizing the IoT's disruptive capabilities and potential benefits for business processes along all value chain activities. In this regard, the integration of IoT technology into existing business processes enables valuable Business Process Improvements (BPI). However, it often remains unclear which BPIs can be expected by organizations and how the anticipated BPIs are realized in detail. Furthermore, the integration of IoT technology into existing business processes constitutes a major challenge caused by a lack of supporting methods, models, or guidelines. The paper at hand addresses this research gap by providing a metamodel that enables the illustration of generic IoT-based BPI patterns. It contains all relevant elements that are comprised by IoT applications with BPI propositions and can be used by industrial organizations as blueprints for conducting IoT projects. The metamodel development follows fundamental principles of design science research (DSR) and is extensively evaluated by deriving a first set of patterns from real-life IoT applications of three market-leading corporations. In addition, an expert survey is conducted to assess the metamodel's usefulness.
... huge amounts of real-time data can benefit from the advantages of BPM solutions [7]. One such key advantage is the ability to react to changes in the environment flexibly [11] to satisfy process performance indicators or to improve the processes. ...
Full-text available
Monitoring the state of currently running processes and reacting to deviations during runtime is a key challenge in Business Process Management (BPM). The MAPE-K control loop describes four phases for approaching this challenge: Monitor, Analyze, Plan, Execute. In this paper, we present the ProGAN framework, an idea of an approach for implementing the monitor, analyze, and plan phases of MAPE-K. For this purpose, we leverage a deep learning architecture that builds upon Generative Adversarial Networks (GANs): The discriminator is used for monitoring the process in its environment by using sensor data and for detecting deviations w.r.t. the desired process state (monitor phase). The generator is used afterwards for analyzing the detected deviation and its symptoms as well as for adapting the current process to resolve the deviation and to restore the desired state. Both components are trained together by utilizing each other’s feedback in a self-supervised way. We demonstrate the application of our approach for an exemplary scenario in the manufacturing domain.
... In this context, digital transformation is characterized by enabling connectivity, collecting data, and therefore using digital technology to redefine a value proposition and to change the identity of the organization [2]. As IoT offers the capabilities to enhance connectivity and collect data, it is a main technology to enable digital transformation [3]. One major lever to transform the organization is IoT-based Business Process Improvement (BPI) which changes the way, companies are doing their businesses [4]. ...
Conference Paper
Full-text available
Companies of all industrial sectors are increasingly integrating Internet of Things (IoT) technology into their processes to realize a data-driven transformation of their businesses. The generation and use of comprehensive process data in real-time and the connection of process entities enables an improvement and beneficial redesign of business processes of all kinds. However, a goal-oriented exploitation of IoT technology for digital transformation and Business Process Improvements (BPI) is challenging due to the complexity of integrating IoT into existing processes. Companies require appropriate guidance to evaluate and scope their initiatives regarding IoT-based BPI. We therefore propose a holistic IoT-based BPI Maturity Model that assists organizations to determine their current state and get assistance to optimize or develop specific capabilities. This paper provides an overview about the structured development process of the maturity model comprising an extensive literature review and a six-round Delphi study.
Full-text available
Being the blockchain and distributed ledger technologies particularly suitable to create trusted environments where participants do not trust each other, business process management represents a proper setting in which these technologies can be adopted. In this direction, current research work primarily focuses on blockchain-oriented business process design, or on execution engines able to enact processes through smart contracts. Conversely, less attention has been paid to study if and how blockchains can be beneficial to business process monitoring. This work aims to fill this gap by (1) providing a reference architecture for enabling the adoption of blockchain technologies in business process monitoring solutions, (2) defining a set of relevant research challenges derived from this adoption, and (3) discussing the current approaches to address the aforementioned challenges.
We envisage that BPM and IoT Big Data will be the two pillars of next-generation Process-Aware Information Systems (PAIS). While IoT enables BPM to perceive and react to realtime events in the physical world, BPM can equip IoT with a well-developed modelling and implementation platform. However, the integration of BPM and IoT is facing paradigm misalignment challenges including mismatch of programming mechanisms, mismatch of resource management mechanisms, and mismatch of adaptation mechanisms. In this paper, we present the vision and architectural solution of the recently funded NSFC-DFG cooperation research project BRIBOT, which aims to develop novel service-based approaches and techniques for these challenges. The paper presents the BRIBOT methodology that comprises four parts: abstraction and servitization of IoT data, resource space that handles service and data assets, modelling and transformation of IoT and business events, and IoT-event-driven process awareness and adaptation.
The Internet of Things enables to connect the physical world to digital business processes (BP) and allows a BP to (1) consider real-world data to take more informed business decisions, (2) automate and/or improve BP tasks, and (3) adapt itself to the physical execution environment. We refer to these processes as IoT-enhanced BPs. Although numerous researchers have studied this subject, there are still some challenges to be faced. For instance, the need of a modelling solution that does not increase the notation complexity to facilitate further analysis and engineering decision making, or an execution approach that provides a high degree of independence between the process and the underlying IoT device technology. The objective of this work is defining an approach that (1) considers important intrinsic characteristics of IoT-enhanced BPs at modelling level without growing the complexity of the modelling language, and (2) facilitates the execution of the IoT-enhanced BPs represented in models independently from IoT devices’ technology. To do so, we present a modelling approach that uses standard BPMN concepts to model IoT-enhanced BPs without modifying its metamodel. It applies the Separation of Concern (SoC) design principle: BPMN is used to describe IoT-enhanced BPs while low-level real-world data is captured in an ontology. Finally, a microservice architecture is proposed to execute BPMN models and facilitate its integration with the physical world. This architecture provides high flexibility to support multiples IoT device technologies as well as their evolution and maintenance. The evaluation done allows us to conclude that the application of the SoC principle using BPMN and ontologies facilitates the definition of intrinsic characteristics of IoT-enhanced BPs without increasing the complexity of the BPMN metamodel. Besides, the proposed microservice architecture provides a high degree of decoupling between the created models and the underlying IoT technology.
Full-text available
The integration of high frequency event data from Internet of Things (IoT) devices into existing complex and mature Business Process Management Systems (BPMS) constitutes a major hurdle for many organizations. Event-Driven Business Process Management (EDBPM) is a paradigm to tackle this hurdle and to lever the enhancement of industrial IoT applications. Existing literature regarding EDBPM and its underlying technologies and methods form a heterogenous set of approaches, frameworks and applications that lacks standardization and maturity. In this context, the literature review of the work at hand conducts a survey about EDBPM focusing on its capabilities to be a lever for the scale of IoT applications. First, we perform an extensive literature research on EDBPM and related topics. Second, a literature analysis and synthesis are presented by summarizing and clustering the discovered publications. Furthermore, a future research agenda is formulated that addresses the main existing research gaps and challenges of EDBPM.
Conference Paper
Full-text available
Process discovery from event logs as well as process prediction using process models at runtime are increasingly important aspects to improve the operation of digital twins of complex systems. The integration of process mining functionalities with model-driven digital twin architectures raises the question which models are important for the model-driven engineering of digital twins at designtime and at runtime. Currently, research on process mining and model-driven digital twins is conducted in different research communities. Within this position paper, we motivate the need for the holistic combination of both research directions to facilitate harnessing the data and the models of the systems of the future at runtime. The presented position is based upon continuous discussions, workshops, and joint research between process mining experts and software engineering experts in the Internet of Production excellence cluster. We aim to motivate further joint research into the combination of process mining techniques with model-driven digital twins to efficiently combine data and models at runtime.
Full-text available
Business Process Management (BPM) has significantly advanced and gained high popularity in industry. However, it remains an open issue why tools frequently are used for business process modeling that are not mainly implemented for this purpose. Often, macros for Microsoft Visio or Microsoft Excel form the first choice to capture the flow of business activities. One reason why these tools might be used is the low training effort and the fast creation of a quick model, which can be generated with these tools. Another reason for the “lower” preference of BPM software tools might be their inability to respond to changes in technology and working styles, e.g. the shift towards "agile" processes and the "flattening" of workforce hierarchies that bring more stakeholders into contact with a much broader array of processing steps than before. A central question is whether the BPM community should create an entirely new paradigm for process modeling. One can think of more intuitive drawing conventions that laymen would use, and of models of an entirely different kind (i.e. not process-centric and not data- or case-centric) that still bear the possibility to support modern and future business process. The purpose of this seminar was to bring together a cross-disciplinary group of academic and industrial researchers to foster a better understanding of how to ease the access to, and applicability of, business process modeling. We discussed business process modeling approaches against emerging trends such as Internet of Things, the need for incremental and agile creation of new processes, and the need for workers to understand and participate in multiple contextual levels (e.g. transactional, business goals, strategic directions) while performing processes. The seminar also considered how new technologies, such as modern tools for UI design (e.g. D3, node.js) could be applied to support fundamentally shifts in how processes are modeled and how humans are involved with their execution.
Full-text available
Stress is a major problem in the human society, impairing the well-being, health, performance, and productivity of many people worldwide. Most notably, people increasingly experience stress during human-computer interactions because of the ubiquity of and permanent connection to information and communication technologies. This phenomenon is referred to as technostress. Enterprise systems, designed to improve the productivity of organizations, frequently contribute to this technostress and thereby counteract their objective. Based on theoretical foundations and input from exploratory interviews and focus group discussions, the paper presents a design blueprint for stress-sensitive adaptive enterprise systems (SSAESes). A major characteristic of SSAESes is that bio-signals (e.g., heart rate or skin conductance) are integrated as real-time stress measures, with the goal that systems automatically adapt to the users’ stress levels, thereby improving human-computer interactions. Various design interventions on the individual, technological, and organizational levels promise to directly affect stressors or moderate the impact of stressors on important negative effects (e.g., health or performance). However, designing and deploying SSAESes pose significant challenges with respect to technical feasibility, social and ethical acceptability, as well as adoption and use. Considering these challenges, the paper proposes a 4-stage step-by-step implementation approach. With this Research Note on technostress in organizations, the authors seek to stimulate the discussion about a timely and important phenomenon, particularly from a design science research perspective.
Full-text available
In this paper, we discuss the application of concept of data quality to big data by highlighting how much complex is to define it in a general way. Already data quality is a multidimensional concept, difficult to characterize in precise definitions even in the case of well-structured data. Big data add two further dimensions of complexity: (i) being “very” source specific, and for this we adopt the interesting UNECE classification, and (ii) being highly unstructured and schema-less, often without golden standards to refer to or very difficult to access. After providing a tutorial on data quality in traditional contexts, we analyze big data by providing insights into the UNECE classification, and then, for each type of data source, we choose a specific instance of such a type (notably deep Web data, sensor-generated data, and Twitters/short texts) and discuss how quality dimensions can be defined in these cases. The overall aim of the paper is therefore to identify further research directions in the area of big data quality, by providing at the same time an up-to-date state of the art on data quality.
Conference Paper
In nursing homes documentation is a mandatory yet time consuming task: typically, nurses document their work after performing the treatments at the end of their shifts which might lead to a decline in the quality of the documentation. The utilization of process-oriented technology in the care domain has already been shown to have high potential in support for documentation of treatment tasks. We want to further this idea, by transforming physical objects into smart objects through equipping them with NFC tags. They can then be used to automatically register their usage with NFC readers specific to care residents. Our analysis shows that many treatment tasks are using care utilities and are candidates for automatic task documentation. We present three scenarios for automatic documentation in nursing homes, an implementation through a proof-of-concept prototype, and an evaluation through expert interviews in the care domain. The interviews indicate an average decrease in documentation time per shift of more than 60%.
Process management technology constitutes a crucial component of service-oriented environments as it facilitates the composition of services at design time and their orchestration at run time. In this context, high flexibility is required as business functions must be quickly adaptable to cope with dynamic changes in the business. The tremendous proliferation of smart mobile devices over the last years has fostered their prevalence in knowledge-intensive areas. As a result, it is frequently demanded to enhance process-aware information systems with mobile activity support. The latter constitutes process activities (i.e., single process steps) to be executed on smart mobile devices. In general, the technical integration of this activity type with existing process management technology is challenging. If a mobile context shall be additionally considered when executing the activities, the integration gets even more complex. However, the use of such a mobile context offers several advantages. For example, (mobile) activity execution time can be significantly decreased if mobile activities are only assigned to those users whose location is close to the one of the mobile activity. Existing research approaches mainly focus on the partitioning of processes and the distributed execution of the resulting fragments on smart mobile devices. Opposed to this fragmentation concept, this paper proposes an approach to enable the robust and flexible execution of single process activities on smart mobile devices.
Conference Paper
Most autonomous agents are situated in a social context and need to interact with other agents (both human and artificial) to complete their problem solving objectives. Such agents are usually capable of performing a wide range of actions and engaging in a variety of social interactions. Faced with this variety of options, an agent must decide what to do. There are many potential decision making functions which could be employed to make the choice. Each such function will have a different effect on the success of the individual agent and of the overall system in which it is situated. Therefore, this paper examines agents' decision making functions to ascertain their likely properties and attributes. A framework for characterising social decision making is presented and a socially responsible decision making principle is proposed which enables both the agent and the overall system to perform well. This principle is illustrated, and empirically evaluated, in a multi-agent system for unloading lorries at a warehouse.
Conference Paper
Providing operational support to clinicians during their daily activities in hospital wards is a challenge for information technologies. In particular, any possible solution should provide usable user interfaces, possibly deployed on mobile devices, and should be able to enact and monitor the execution of clinical guidelines. To tackle this issue, in this paper we present a medical system that supports clinicians in the management of clinical guidelines. The system exploits concepts from Business Process Management (BPM) and Service Oriented Computing (SOC) on how to organize clinical guidelines in the healthcare context and how to support the automation of their execution. As a viable solution for clinicians' interaction with the system, we investigated the use of vocal and touch interfaces. Usability evaluation results indicate the feasibility of the approach.