Visual Decision Modeling for IoT-Aware Processes [original]

Visual Decision Modeling in IoT-Aware Processes

Yusuf Kirikkayis, Florian Gallik, and Manfred Reichert

Institute of Databases and Information Systems, Ulm University, Germany

{yusuf.kirikkayis,florian-1.gallik, manfred.reichert}@uni-ulm.de

Abstract.

The enactment of real-world-aware business processes involves

multiple interconnected devices. While the latter form the basis of the

Internet of Things (IoT) and enable the exchange and collection of

physical data via the Internet, Business Process Management (BPM)

enables analyzing, modeling, implementing, executing, and monitoring

business processes. In IoT-aware processes decision making may depend

on the data provided by multiple IoT devices, which results in decision

rules of complex structure. In this paper, we present two approaches for

the visual modeling of decisions in IoT-aware processes. The first approach

allows for the visual representation of complex decision rules by extending

Business Process Model and Notation (BPMN) 2.0. The second approach

separates decision logic from process logic using a drag&drop modeler.

With both these approaches, IoT involvement in decision making becomes

apparent and complex decisions can be represented in an intuitive and

simple manner.

Keywords: BPM ·Decision making ·IoT-Aware Process

1 Introduction

The Internet of Things (IoT) is a network of physical objects that enable the

collection and exchange of data via a network connection [

]. On one hand IoT de-

vices allow for the acquisition and collection of data by sensors. On the other they

support the response to an event by actuators. In particular, IoT enables bridging

the gap between physical and digital world [

]. In turn, BPM enables optimiz-

ing, modeling, executing, and monitoring business processes [

]. By integrating

BPM with IoT capabilities, process modeling, execution and monitoring can

be enhanced. Furthermore, this integration enables the confirmation of manual

steps through the use of IoT devices such as sensors and cameras [

]. In addition,

low-level data generated by individual IoT devices (e.g., temperature, humidity

or switch state) may be aggregated and combined, in high-level information

that can be used for decision making. Moreover, high-level information enables

BPM to understand the dynamic context of the physical world during process

execution, which makes IoT-aware processes context-aware [6].

In contemporary approaches combining BPM and IoT, devices are added to

process models as resources. Consequently, IoT data is used without linking it

to other contextual process data. The potential of gaining additional insights is

not exploited [

]. As another challenge of using IoT devices for decision making,

IoT-aware processes become decision-intensive [

]. In turn, this leads to an

increased complexity due to a high number of decisions as opposed to IoT-

unaware processes. In contemporary approaches, the involvement of IoT devices

in decision making is not explicitly addressed. In addition, complex decision-

making rules can only be represented in tables or gateways, which affects model

readability and comprehensibility.

In this paper, we present two approaches for visually representing decisions

in IoT-aware processes. One of these approaches is based on BPMN, whereas

the other separates decision from process logic. The approaches aim to explicitly

display the IoT devices involved. Furthermore, complex decision rules become

more intuitive due to the chosen visualization. The remainder of this paper is

organized as follows: Section 2 discusses related work. Section 3 presents the two

solution approaches. Finally, Section 4 provides a summary and outlook.

2 Related Work

There exists a variety of approaches [

][

] that embed IoT into BPMN-based

process models in terms of IoT tasks, physical entities, and resources. However,

none of these approaches aggregates IoT data into higher-level information, which

then can serve as input for decision making.

The Decision Model and Notation (DMN) standard provides a solution to

separate decision from process logic [

]. In [

], the combination of BPMN and

DMN is considered for IoT-aware processes. Note that the involvement of IoT

devices actually neither becomes apparent in BPMN nor in DMN.

In [

], an approach for converting DMN models into DMN decision tables is

presented. In particular, complex decision tables can be created automatically.

Still, the problem remains that complex decision tables are difficult to read and

comprehend. Furthermore, involved IoT devices are not explicitly represented or

highlighted in [16].

3 IoT-aware visual decision modeling

This section presents the approaches for visual decision modeling in IoT-aware

processes. Based on literature review and expert interviews we identified the

challenge to model complex IoT decisions. To the lack of space we can not

provide more details. First of all, a BPMN-based decision modeling approach is

introduced, followed by an approach that separates decision modeling from the

process logic using a drag&drop modeler.

3.1 Approach 1: Decision modeling in BPMN

To enable decision modeling directly in BPMN, the following extensions are

introduced in BPMN (Figure 1); IoT representative (1), IoT decision container

(2,3&4), and IoT decision table (5). Each IoT decision container contains an

IoT decision table, which can be filled using the properties panel (7). The IoT

representatives may be inserted into the IoT decision container via drag&drop.

An IoT representative visualizes IoT sensors such as limit switches, temperature

sensors, pressure sensors or light sensors.

3 4

Properties

panel

IoT

decision container

IoT

decision container

IoT

representative

Fig. 1: IoT-aware decision modeling in BPMN

Figure 1 shows the modeling of the decision as well as its logic. First, the

light scanner, start light barrier, and pressure sensor are queried (low-level

information). The query results are then used for defining conditions in the

decision table based on boolean algebra (high-level information). In turn, the

results of Robot 1 (3) and Robot 2 (4) are used to define conditions for the root

decision container Robots (2). The evaluation of this top-level decision, is then

considered for controlling the flow of the corresponding process (8). When clicking

on the IoT decision icon of the corresponding task (7), the root IoT decision

container may be expanded/collapsed. Each IoT decision container may contain

nIoT decision containers and likewise nIoT representatives, which results in a

tree data structure. The latter is read bottom-up (i.e., the results of the nodes

are passed from the bottom to the top) until the final decision is made by the

root IoT decision container.

3.2 Approach 2: Separate Decision modeling

To separate the decision from the process logic, a drag&drop modeler is used.

This allows the insertion and movement of elements on a modeling area (Figure 2).

For modeling the decision logic the following elements are available; IoT decision

container (1), IoT representative and attached comparison (2), logical gates (4&5)

and result module (6).

First, the IoT representative queries the physical sensor. Then, the attached

condition is checked. Depending on the result, the IoT representative returns

a corresponding boolean value. Each IoT decision container may contain any

number of IoT representatives,comparison modules, and decision containers (1).

In turn, IoT representatives are connected to logical gates, which process multiple

boolean input signals into a boolean output signal based on logical operators,

such as conjunction (AND gate 4), disjunction (OR gate 5), or negation (3).

The output signals of the logical blocks may either lead to a final result or be

nested with other logical blocks. The final result may be of any data type and be

represented by a result module (6). The transfer of the final decision from the

drag&drop modeler to the BPMN model is done via an IoT decision task.

COPD severeness

Heart rhythm < 120

Temperature = "cold"

Respiration > 16

AND & = "none"

Heart rhythm = [60...90]

Temperature ≤ 36

Respiration ≤ 16

AND & = "mild"

Heart rhythm ≥ 160

Temperature = ]37...40[

Respiration ≤ 16

OR ≥ 1 = "severe"

COPD severeness 1

Result

module

Logical

gate

IoT

decision

container

IoT

representative

Fig. 2: IoT-aware decision modeling separated from BPMN

4 Summary and Outlook

We presented two approaches for visually modeling decisions in IoT-aware pro-

cesses. The first one enables explicit decision modeling in BPMN using an IoT de-

cision container with a corresponding IoT decision table. The IoT representatives

are dragged and dropped into the IoT decision container. These representatives

query the physical sensors and store the retrieved values in the decision table.

As each IoT decision container has its own decision table, complexity is reduced.

Each IoT decision container passes its final decision upwards until reaching the

root IoT decision container. The latter makes a final decision, which then flows

into the process and can be used in BPMN. As opposed to Approach 1, Approach

2 separates decision modeling from process modeling.

In future work, we will implement the two approaches and evaluate their

usability and benefits in a case study. Moreover, we will develop an engine, for

processing decision rules in IoT-aware processes. Finally, we will model IoT-aware

processes from different domains to further verify the approaches.

Acknowledgments This work is part of the ZAFH Intralogistik, funded by the

European Regional Development Fund and the Ministry of Science, Research

and Arts of Baden-W¨urttemberg, Germany (F.No. 32-7545.24-17/12/1)

References

Janiesch, C.: The Internet of Things Meets Business Process Management: A Mani-

festo. In: IEEE Systems, Man, and Cybernetics Magazine 6(4), pp. 34–44. (2020).

Suri, K., Gaaloul, W., Cuccuru, A., Gerard, S.: Semantic Framework for Internet of

Things-Aware Business Process Development. In: 26th International Conference on

Enabling Technologies: Infrastructure for Collaborative Enterprises, (2017).

Bazhenova, E., Zerbato, F., Oliboni, B., Wekse, M.: From BPMN process models to

DMN decision models. In: Information Systems 83(12), (2019).

Cheng et al.: Modeling and Deploying IoT-Aware Business Process Applications in

Sensor Networks ,(2019).

Combi, C., Oliboni, B., Zardiniy, A., Zerbato, F.: Seamless Design of Decision-

Intensive Care Pathways. In: International Conference on Healthcare Informatics,

(2016).

Song, R., Vanthienen, J., Cui, W., Wang, Y., Huang, L.:Context-Aware BPM Using

IoT-Integrated Context Ontologies and IoT-Enhanced Decision Models. In: IEEE

Conference on Commerce and Enterprise Computing, (2019).

Meyer, S., Ruppe, A., Hilty, L.: The Things of the Internet of Things in BPMN. In:

Conference in Advanced Information Systems Engineering Workshops, (2015).

Hasi´c, F., De Smedt, J., Vanthienen, J.: Augmenting Processes with Decision Intelli-

gence: Principles for Integrated Modelling. In: Decision Support Systems, (2018).

Dumas, M., La Rosa, M., Mendling, J., Reijers, H.: Fundamentals of Business Process

Management 2nd edn. Springer-Verlag GmbH, Germany (2018).

10.

Hasi´c, F., De Smedt, J., Vanthienen, J.: Developing a Modelling and Mining

Framework for Integrated Processes and Decisions. In: On the Move to Meaningful

Internet Systems, (2018).

11.

Gardaˇsevi´c, G., Veleti´c, M., Maleti´c, N. et al.: The IoT Architectural Framework,

Design Issues and Application Domains. In: Wireless Pers Commun 92, 127–148

(2017).

12. Object Management Group (OMG): Decision Model and Notation 1.3. (2021).

13.

Koschmider, A., Mannhardt, F., Heuser, T.: On the Contextualization of Event-

Activity Mapping. In: Business Process Management Workshops, (2018).

14.

Hasi´c, F., Snoeck, M., Serral, E.: Comparing BPMN to BPMN + DMN for IoT

process modelling: a case-based inquiry. In: 35th ACM/SIGAPP Symposium on

Applied Computing, (2020).

15.

Hasi´c, F., Asensio, E.: Executing IoT Processes in BPMN 2.0: Current Support and

Remaining Challenges. In: 13th International Conference on Research Challenges in

Information Science (RCIS), (2019).

16.

Etinger, D., Simi´c, D., Buljubaˇsi´c L.: Automated decision-making with DMN: from

decision trees to decision tables. In: 42nd International Convention on Information

and Communication Technology, Electronics and Microelectronics (MIPRO), (2019).