Multi-level Risk Modelling for Interoperability of Risk Information

Yuhong Fu

1,2 a

, Georg Grossmann

1,2 b

, Karamjit Kaur

1,2 c

, Matt Selway

1,2 d

The digital transformation driven by the rise of Industry 4.0 leads to an increase use of data standards and

information systems for management and decision making. With the emerging of new software ecosystems,

industries are facing heterogeneous systems and a lack of interoperability for information including risk infor-

mation. The lack of interoperability in risk management leads again to a slow and often incorrect information

transfer, which affects the timely response to risks. In this paper, we propose a new risk modelling approach

that combines existing multi-level modelling and multi-view modelling approaches to structure and hence

simplify interoperability.

1 INTRODUCTION

Industry 4.0 pushes digital transformation of indus-

try and is changing the way people live and industry

interoperability pose a challenge affecting the abil-

ity of organizations to gain insights from risk anal-

ysis. Therefore, it is significant to have a comprehen-

sive understanding of risks at a system and system-of-

systems level and provide a holistic view.

Within the FEnEx CRC

we look at different sce-

narios for risk modelling in asset management. The

assets of concern are usually large physical assets that

are composed of a number of complex components,

https://orcid.org/0000-0003-4415-2228

c

https://orcid.org/0000-0003-0255-1060

d

https://orcid.org/0000-0001-6220-6352

e

https://orcid.org/0000-0002-7125-3289

1

https://www.fenex.org.au

sumed that they are fitted with sensors and maybe

even come with analytical capabilities in a software

ponent level you may deal with different data for-

mats and how data is captured and structured. This

becomes increasingly challenging with new business

models of manufactures where data might be pro-

vided only through online services for which a sub-

scription has to be paid. (2) On the horizontal di-

mension, we are dealing with the communication be-

tween systems, departments and may be event exter-

nal partners or government each working with a dif-

ferent software ecosystems. The exchange of infor-

departments and holistically across the whole organi-

zation spanning multiple risk models. One risk event

affects other departments and sectors such as agricul-

well as the cascading effects of the inter-connection

risks. To handle such risks, an enhanced risk mod-

elling approach is required which covers the cross-

departmental risk models in the horizontal direction

as well as risk models across multiple level in the ver-

tical direction. This intersection of modelling both

horizontal and vertical angle will provide risk man-

agers a clear picture of the risks across multiple levels

and views.

The research goal of this paper is to propose a

2 MOTIVATION AND CASE

Risk is a measure of the probability and severity of

adverse effects. The main factors in assessing risk in-

clude the potential loss (consequence) and the prob-

ability of occurrence (Haimes, 2005). In public util-

ities, such as the energy industry, risk assessment is

important and necessary. This helps in the repair and

maintenance of infrastructure, as well as the timely

ment. Through risk modelling, risk-related metrics

can be represented in a model diagram which can be

used for risk simulation and analysis, and documen-

tation and code generation. Stakeholders can under-

stand the probability of a particular risk occurring and

the severity of the consequences, and develop solu-

tions to avoid or reduce the adverse impact of the risk.

We investigate risk modelling from a model-

driven engineering (MDE) perspective. MDE has the

advantage of abstracting from the real-world through

increase the automation for interoperability. A tra-

sual modelling language, this makes it more intuitive

for model developers to build models and easier to

check for model deficiencies. It provides a clearer un-

derstanding of the model architecture. Based on the

fact that UML is a universally accepted and agreed

modelling language, the information contained in the

model can be understood by different stakeholders of

the model. UML can be effectively used to model

the flow of risk information. Through risk modelling,

potential risks can also be clearly represented for all

ergy industry are large and complex, and the risks

associated with business processes are also often di-

verse and complex. This poses a challenge for risk

modelling. For example, there are interoperability is-

sues between large heterogeneous ecosystems. Tra-

ditional modelling approaches, such as UML, models

within two categorization levels, the model and meta-

model levels (Igamberdiev et al., 2016). At the same

time, UML instantiation models do not clearly scale

2001) provides a better way to model multi-level risk

models, since it supports unlimited number of levels

while modelling, as contrast to the limit of two levels

in the UML model. The natural propagation of con-

straints on multi-level instantiation that a multilevel

modelling approach can bring is necessary (Selway

et al., 2017). Through this approach, the detailed in-

formation available at lower level will be aggregated

to the higher level, which will enable decision mak-

ers to gain a more comprehensive and specific under-

its own non-standard language extension mechanism

(Atkinson et al., 2015). In the energy industry, such

ure to transmit anomaly information from sensors to

risk control department in a timely manner, or the in-

formation is not properly recognized when transmit-

ted across systems. This can lead to serious problems,

such as the power interruptions.

There is also the issue that within the energy in-

dustry, as well as other large companies, contains

Categorisation, Extension and Refinement) (Selway

et al., 2017) which has been chosen as the basis for

risk modelling and a multi-view modelling approach

called e-BPRIM (e-Business Process-Risk Manage-

ment – Integrated Method) (Lamine et al., 2022) in

our work. In the next section we will describe the

multi-level modelling approach and the multi-view

modelling approach separately on a case study in the

energy sector.

on the bottom left and contains procurement risk and

to the top view, the enterprise-level risk management

system.

level element and cannot be utilised for subse-

quent instantiations;

• Classes cannot affect entities formed via addi-

tional instantiation and they can only determine

the semantics of their immediate instances.

DI adopts the element called clabject and the con-

cept called potency to solve the above problems. A

class and object. Potency represents the number of

concepts pf multi-level modelling.

nents of the conceptual model, which entails hiding

the components by folding them into a single object at

the highest level (Selway et al., 2017). We agree with

Matt et al. that the DI approach is ineffective, partic-

ularly for scenarios involving system interoperability,

because it obscures significant distinctions that exist

in the domain being represented, resulting in a one-

to-many relationship between model elements and the

3.1.2 SLICER

In 2017, Matt et al. proposed the SLICER ap-

proach for solving interoperability problems between

the following features.

• This framework is based on a flexible horizontal

• This framework adds detail or specification

• This framework explicitly identifies the features

elements are flexibly instantiated or specialized. We

believe that the risk model built based on this frame-

work can clearly display and flexibly combine the risk

concept and the entity concept. The definition of the

In this subsection, we present a new Multi-View mod-

elling approach called e-BPRIM.

3.2.1 e-BPRIM

BPRIM was first proposed as a risk management

approach by Sienou et al. in 2009 (Sienou et al.,

2009). This approach is a risk-driven process en-

AdoBPRIM based on AdoXX(Lamine et al., 2019).

e-BPRIM, which is based on the agile development

methodology, offers insight and value-driven models

to assist risk and process managers in carrying out

their duties (Lamine et al., 2022). This method is

founded on the black box, which seeks to build re-

lationships between the inputs and outputs of various

phases that make up the two cycles (Lamine et al.,

2022). In AdoBPRIM, the authors designed naviga-

tion techniques to ensure consistency between these

given view and produce a new one;

• Reuse: Reuse some syntax and/or semantics from

some existing views and produce a new one;

• Merge: Merge some syntax concepts from some

existing views and produce a new one;

• Compositing: Collect information from some ex-

isting views and produce a new one;

• Synchronization: Execute modifications of over-

lapping concepts synchronously in all other views

through the algorithm.

We believe that multi-view modelling approach

is well suited for applying in large ecosystems. It

allows the risk model in large ecosystems to be di-

vided into several sub-views and ensures consistency

between these sub-views, which facilitates interoper-

ability between large ecosystems and allows informa-

ble risks to make risk management plan and respond

this multi-view modelling approach into the SLICER

multi-level modelling approach to further improve the

interoperability and efficiency between systems and

reduce the complexity of risk models.

• Integrate all other forms of;

• Strict formalization allows for additional au-

terms in large heterogeneous ecosystems to enhance

interoperability. In the future research, we will extend

a suitable existing risk ontology to implement in the

current project.

4 A MULTI-LEVEL AND

As mentioned above, in our approach, we combine

a multi-level modelling and a multi-view modelling

approach in order to create a new risk management

model. The model has the advantage of low complex-

ity and ease of readability, while achieving a sepa-

ration of concerns. Furthermore, we adopts syntac-

tic overlap relationship and extend relationship from

multi-view modelling approach to achieve consis-

tency among different views.

• InstX: InstX is a special instantiation relationship.

It can add new attributes or relationships compar-

ing with the traditional instantiation;

• InstN: InstN is the traditional instantiation rela-

tionship. It acquires the attributes and relation-

ships from the element being instantiated and can-

not be further instantiated;

• SbS: SbS is used to specialise the parent class.

The specialised subclass is at the same level as

the parent class, which can refer the properties of

black box to the white box.

Clearly, through these relationships, elements in

the model can be flexibly extended or instantiated.

UOM is an abbreviation for Unit of Measurement,

which represents a data format with unit of measure-

ment.

In Figure 2, each risk and asset type is considered

as a black box, which is in the upper part. By us-

ing the concept of syntactic overlap, when elements

in any one view change, the corresponding overlap-

ping syntax in other views is updated in time. This

concept from multi-view modelling approach ensures

consistency among views and enables the transfer of

see the risk view that belongs to their department.

Also, with the consistency between views, the values

of the attributes in the views are updated in time. Ob-

viously, it is easier to focus on and get information

from just one of the views than to get information in

the whole figure. This is particularly the case in the

larger, more complex industrial ecosystems. With the

concept from multi-view approach, the risks and the

entities corresponding to the risks are distinguished in

each risk view, which further increases the readability

of the model and reduces complexity.

In each sub-view, risk consequence has its own

quantity and delay time, formula is as follow.

RQ =

LQ

T Q

ble 1 shows the assessment criteria of risk conse-

quence for production risk.

both two risk views can be derived automatically.

Table 1: Assessment criteria of risk consequence for pro-

duction risk and finance risk.

specification-for-analytics-interoperability-20-rp3-0048/

heterogeneous systems. By taking advantages from

the multi-level modelling approach, the complexity of

the model is reduced and the model is made clearer.

While the benefits from the multi-view modelling ap-

proach ensures that interoperability is enhanced and

makes it easier for risk managers to focus on relevant

information.

In future research, we will continue to refine

this risk modelling approach by further incorporating

the multi-view modeling approach at the meta-model

ically in a risk matrix. Moreover, for most risks, we

will link each risk with a corresponding management

measure to achieve timely response and timely treat-

ment. Furthermore, we will expand the current risk

ontology to achieve system integration via semantic

mapping, which can avoid the restrictions imposed by

UML.

Level Modeling Approaches and Tools. In MULTI@