=Paper=
{{Paper
|id=Vol-2114/paper4
|storemode=property
|title=Situational Reference Model Mining
|pdfUrl=https://ceur-ws.org/Vol-2114/paper4.pdf
|volume=Vol-2114
|authors=Jana-Rebecca Rehse
}}
==Situational Reference Model Mining==
https://ceur-ws.org/Vol-2114/paper4.pdf
Situational Reference Model Mining
Jana-Rebecca Rehse
Institute for Information Systems (IWi) at the German Center for Artificial
Intelligence (DFKI GmbH) and Saarland University
Campus D3 2, Saarbruecken, Germany
Jana-Rebecca.Rehse@iwi.dfki.de
Abstract. Reference models can be considered as special conceptual
models that serve to be reused for the design of other conceptual mod-
els. Due to an ongoing need for high-quality reference models, reference
model mining, i.e. the (semi-)automatic derivation of reference models
from a set of existing process models, has recently gained the attention
of researchers. The presented dissertation project addresses the concept
of Situational Reference Model Mining, i.e. the idea that mined reference
models, although based on the same input data, are intended for different
use cases and thus have to meet different requirements. These require-
ments determine the reference model character and thus the technique
that is best suited for mining it. The dissertation’s major objective is to
design, elaborate, and validate a method for Situational Reference Model
Mining, which provides reference modelers with a clear guideline on how
to use automated reference model mining techniques to their advantage.
Keywords: Inductive reference modeling, Reference model mining, Ref-
erence model design principles, Context-aware process design
1 Motivation and Research Problem1
Reference models can be considered as special conceptual models that serve to be
reused for the design of other conceptual models [4]. They provide a template for
process models in a certain industry and thus facilitate a resource-efficient im-
plementation of the respective process and its adaption to the individual needs
of an organization. This way, companies may benefit from best practices and
industry-specific experience. The use of reference models is associated with a
higher quality of processes and process models, as it simplifies internal commu-
nications by introducing a common terminology and considerably reduces the
resources required for Business Process Management (BPM) [5].
Leveraging the multiple benefits of reference modeling in both research and
practice depends on the widespread availability of high-quality reference models.
Generally accepted and widely used reference models exist only for a few indus-
tries, such as the IT Infrastructure Library (ITIL) for IT service management or
1
This section is based on previous work presented in [1–3] and has been adapted.
the Supply Chain Operations Reference Model (SCOR) for supply chain man-
agement, eliciting an ongoing need for reference model construction methods.
Reference models may be constructed both deductively and inductively. De-
ductive methods or “top-down” approaches employ generally accepted theories
and principles. Models are constructed on a theoretical base and gradually sub-
stantiated along the way. In contrast, inductive or “bottom-up” development of
reference models makes use of real-world data such as existing process models
or execution logs. It focuses on similarities and commonalities within the input
data and abstracts from a model’s individual features. As is it possible to auto-
mate the inductive derivation of a potential reference model from a collection of
existing process models (Reference Model Mining), inductive reference modeling
may contribute to the trend of combining BPM with artificial intelligence (AI)2 .
Until a few years ago, although inductive development was generally acknowl-
edged, the literature on methods for reference model development was dominated
by deductive approaches. This is interesting, as many existing reference models
were at least partially developed inductively [3]. Recently, however, there have
been some research activities regarding inductive reference modeling. Methods
have been proposed to (semi-)automatically derive reference models from both
instance level data in the form of process logs [6] and type-level data, i.e. process
models [7]. They make use of a multitude of existing computational techniques,
such as subgraph isomorphism [3], genetic algorithms [8], or heuristic approxi-
mations of the graph edit distance [9].
When applying reference model mining (RMM), i.e. (semi-)automatic induc-
tive reference model development, it becomes evident that the reference model
content and character are significantly influenced by the choice of mining tech-
nique and its parametrization. Different mining techniques yield different models,
even when applied to the same set of input models. Meanwhile, given a reuse-
oriented understanding of reference models, the requirements to the reference
model are determined by the situational context it will be used in, similar to the
adaptation of software developments methods in Situational Method Engineering
(SME) [10]. It will hence increase an inductively developed reference model’s use-
fulness and situational suitability, if the mining technique is determined by the
circumstances of its intended reuse. We call this concept “Situational Reference
Model Mining” (S-RMM), i.e. extending RMM towards consciously considering
the situational context when designing and using a reference model (cf. Fig. 1).
Designing a method for S-RMM and understanding the influencing factors
behind it is the main objective of the presented dissertation project titled “Sit-
uational Reference Model Mining — Conceptual Design and Selected Applica-
tions”, which is conducted at the Institute of Information Systems at Saarland
University, under the supervision of Prof. Dr. Peter Loos. It started in October
2015 and is planned to be completed in 2020. Some preliminary results and cited
publications were already achieved during the author’s time as research assistant
at the same institute (April 2011 until October 2015).
2
Cf. the keynote speech “Intelligent Continuous Improvement, When BPM Meets
AI” by Miguel Valdés at BPM 2017
29
Reference
Model
Design
Require-
Choice of Design
ments
Technique Principle
Individual Target
Models Models
Fig. 1. Main idea behind Situational Reference Model Mining
2 Research Method and Research Questions
As the main objective of the project is the design of an artifact (a method
for S-RMM), it follows the design science research paradigm (DSR), originally
coined by Hevner et al. [11]. More specifically, it applies the design science
methodology by Wieringa [12], which describes design science as the “design
and investigation of artifacts in context”. According to its template for design
questions [12, p.16], the overall goal of the dissertation is the following:
Improve availability and suitability of reference models
by introducing a method for reference model mining
that considers the situational context
in order to facilitate reference model development and usage.
This goal shall be reached by answering the following four research questions.
The first two questions are knowledge questions, determined to gather knowl-
edge on the reference modeling process. The second two questions are design
questions, asking for new artifacts to improve the reference modeling process.
1. Which contextual factors influence reference model design?
2. Which techniques exist for reference model mining and which contextual
factors influence their choice and applicability?
3. How can reference model mining techniques be matched with situational
contexts to produce applicable reference models?
4. How can reference model mining be applied in a given application case,
considering situational contextual factors?
The first question addresses the contextual factors that influence reference
modeling. It is an open descriptive empirical research question, meant to ob-
serve reference modeling in real-world scenarios, gathering knowledge about the
context of the artifact to be designed without the need for explanations. In con-
trast, the second question is an open explanatory empirical knowledge question
with the goal to identify existing RMM techniques and to determine their poten-
tials and limitations. We want to know under which situational circumstances a
certain technique may and may not produce an applicable and well-suited ref-
erence model. Because these circumstances may apply to multiple techniques,
they should be conclusively explained by the technique’s properties.
30
Treatment Implementation Problem Investigation
What must be improved? Why?
Treatment Validation Treatment Design
How does this artifact contri- Which artifact could solve the
bute to stakeholder goals? problem?
Fig. 2. The design cycle to be followed [12]
The third question joins the existing artifacts (i.e. the RMM techniques)
with the context by designing a method to match an existing technique with a
situational context, such that the produced reference models are applicable and
useful for their intended purpose. Therefore, the results from the previous two
questions need to be related to each other through some intermediary concept.
This concept and the matching method are then used in the fourth question,
which finally addresses the overall goal of the dissertation project, as sketched
by the template above. It is set out to design a comprehensive method for S-
RMM, ranging from the identification of a need for a new reference model to its
application and validation in a concrete use case.
Knowledge questions and design questions are answered by means of different
research methods, following Wieringa’s design cycle [12, p.27ff.] in Fig. 2 and
Frank’s pluralistic conception of research methods in IS research [13]. Prob-
lem investigation concerns learning more about the problem context, including
stakeholder goals, conceptual problem frameworks, and related phenomena. In
our case, this means an in-depth analysis of the context factors that determine
a reference model’s applicability and usefulness. Therefore, we will perform lit-
erature reviews and conduct interviews with expert modelers. Treatment design
consists of defining requirements for the artifact and designing it accordingly.
Therefore, we will translate the knowledge from the first stage into concrete re-
quirements, before the main artifact (the method) is designed. In the treatment
validation stage, the artifact is tested in its context. In our case, this validation
will be conducted by applying the method for inductively developing a reference
model for a to-be-defined use case, using observational case studies as well as
single-case mechanism experiments along with qualitative research methods.
3 State of the Art and Related Solutions3
The concept of situational reference model construction based on design princi-
ples is not new, but has yet only been elaborated for deductive reference model
development [4]. Inductive reference model development has only recently gained
attention in research, so there is little methodological seminal work. We presented
first ideas towards S-RMM in [2], where the choice of an appropriate mining tech-
nique is discussed. In [14], we illustrate concrete challenges of inductive refer-
3
This section is based on previous work presented in [1] and has been extended.
31
ence modeling according to a seven-stage framework. A number of contributions
describe concrete RMM techniques, but do not take on a methodological per-
spective, reflecting on the ways of model construction and the requirements of
specific use cases. A first overview on existing techniques is given in [14]. Since
then, we have identified a few additional the contributions, such as [15].
The term “context” can be defined as “any information that can be used to
characterize the situation of an entity” [16]. The idea of adapting an artifact, such
as a method or a model, to fit the specific context in which it is used, originated
in the software engineering domain, where a rigid one-size-fits-all methodology
for software development is not only unattainable but also inefficient [10, p.5].
Their explanations and formalizations of the terms “situation” and “context”
can be adapted to the BPM domain [17]. Regarding business processes, the con-
text describes the “environment in which a business process artefact is used”
[18], formalized in terms of a “minimum set of variables containing all relevant
information that impact the design and execution of a business process” [19].
Similar to reference modeling, context is also considered in process mining re-
search to achieve better and more specific results (e.g. [20]).
S-RMM is innovative in the way that it combines the domain of reference
modeling with the idea to adapt an artifact to the context in which it is used, as
coined by SME, and techniques to derive a process model from collected real-life
data, as in process mining. Giving a manual procedure model for inductive refer-
ence modeling based on RMM techniques, S-RMM goes beyond SME, because it
does not directly influence the artifact itself (the model), but instead adapts the
method used for deriving it. It differs from process mining, as both process mod-
els and execution logs are considered potential data sources. To our knowledge,
no comparable approaches or solutions exist in state-of-the-art literature.
4 Contributions4
Whereas most of the author’s contributions have so far addressed research ques-
tion 2 (e.g [2, 21, 14]), the main contribution to S-RMM is made in [1], where
a first answer is given to questions 3 and 4. With a reuse-oriented conceptual-
ization of reference models, their main purpose is to serve as an orientation in
the design of new business process models. In this context, we decipher two gen-
eral design processes [4]. Deriving an individual model from a reference model
is known as “Design With Reuse” (DWR), i.e. an existing model is used as a
blueprint offering guidance to the process model designer by giving suggestions
for both content and design of the individual model. On the other hand, “Design
For Reuse” (DFR) describes the process of constructing a (reference) model for
the purpose of being reused, i.e. composing model parts and domain knowledge,
such that they achieve a certain degree of universality.
Considering a model construction process, there exist several different tech-
niques for deriving a conceptual model from another one. These so-called design
4
This section is based on previous work presented in [1] and has been adapted.
32
Determine Target Determine Determine
Determine
Model Design Reference Model Reference Model
Situational Context
Principle Requirements Design Principle
Choose Mining Choose Mine
Technique Input Models Reference Model
Design For Reuse
Design With Reuse
Adapt Design Evaluate
Reference Model Target Models Target Models
Fig. 3. Procedure model for Situational Reference Model Mining [1]
principles describe how the content of the original model is adopted, adapted,
and extended in order to create a new model. Five design principles are described
in the literature [4]. Each configuration, instantiation, specialization, aggrega-
tion, and analogy may be used in the context of reference modeling and applied
to both DFR and DWR. However, not every design principle may be applied to
every reference model, nor may every intended target model be derived by any
design principle. Instead, the choice of model design principle depends on the
situational circumstances, i.e. the requirements posed to the target model and
the construction process itself. These factors also determine the character and
the choice of an appropriate reference model for a certain application context.
The main idea behind our method for S-RMM is to match RMM techniques
with applicable contexts by means of selecting appropriate design principles for
the construction of both target and reference models. Depending on the situa-
tional circumstances and the target model requirements, a certain design prin-
ciple is applied to derive the target model from the reference model. This design
principle poses certain restrictions and requirements to the reference model de-
sign, which is mainly influenced by the choice of technique that was used to
mine the reference model. Hence, the choice of mining technique is ultimately
determined by the situational context of the reference model application.
Research question 4 is addressed by the first conceptual design for a pro-
cedure model for S-RMM, shown in Fig. 3. It constitutes the main artifact to
be delivered in the dissertation. It is designed around the conceptualization of
S-RMM in Fig. 1 and describes a generic execution process of an S-RMM appli-
cation. Of the ten steps, seven belong to DFR and focus on the reference model
construction (i.e. the actual mining), whereas three belong to DWR and are con-
cerned with the target model construction (i.e. the reference model application).
The procedure model starts with defining the situational context, which de-
termines the target model design principle in the second step. As stated below,
this step is not yet conclusively elaborated and requires a lot of additional re-
search. In the third step, the reference model requirements are defined, which
are partly determined by the used principle and partly by the context itself. The
reference model design principle is derived from these requirements, with the
help of additional methods and artifacts, as explained below. For certain combi-
33
nations of target model and reference model principle, several mining techniques
are available; their choice may depend on additional factors. The input mod-
els can only be finally selected after the mining technique, as some techniques
pose additional requirements to their input data. Afterwards, the model can be
mined, concluding the DFR process. In the DWR process, the model is manually
or automatically adapted, before the target models can be derived and evaluated
regarding their purpose.
To provide a guideline for applying the procedure model and to answer re-
search question 3, we analyzed existing mining techniques regarding their un-
derlying principles and requirements (cf. Table 1). For each target model design
principle, we suggest corresponding reference model design principles and, for
each pair, a example of a suitable mining technique. A more detailed version of
this table, classifying many state-of-the-art techniques can be found in [1].
Table 1. Analysis of matching principles and according mining techniques
Target Model Reference Model RMM Technique
Design Principle Design Principle (Example)
Aggregation [2]
Configuration
Analogy [7]
Instantiation Aggregation [21]
Aggregation [2]
Specialization
Analogy [7]
Aggregation – –
Configuration [6]
Analogy Aggregation [2]
Analogy [8]
5 Intended Future Work
Although the to-be-designed artifacts are roughly sketched in the previous sec-
tions, there are numerous steps to be conducted in order to complete the research
activities that are required to satisfactorily answer the posed research questions.
Readers will notice that the above procedure model was designed without per-
forming an in-depth requirements analysis first. This analysis will be built on
an identification of relevant context factors, which will be identified by means
of a literature analysis of inductive reference modeling case studies and seminal
works on reference modeling. The goal is an integrated and conclusive model of
influential context factors for reference model development. This model will then
be validated and enhanced with expert interviews and provide a comprehensive
answer to research question 1. From that, we can derive requirements, which can
be used to adapt and refine the procedure model.
To conclusively answer research question 2, it is necessary to update the
literature analysis from [14], in order to capture the newest developments in the
34
field of RMM techniques. In addition, there needs to be an extensive literature
review for the identification of techniques that are not explicitly set out for
RMM, but could be used for that purpose, e.g. from the field of process mining.
Our analysis of matching existing RMM techniques with accepted design
principles for reference models in Tab. 1 is a preliminary sketch of a conclu-
sive answer to research question 3. It needs to be complemented by additional
principles such as modification, elimination, or union, potentially by means of a
structuring framework. The RMM techniques identified in the previous research
question need to be matched the the individual principles. Most importantly,
there needs to be some matching procedure between the context factors from
the first question to the design principles, and then, the RMM techniques.
The procedure model in Fig. 3 is the framework for an answer to research
question 4. Not only does it require an in-depth analysis of posed requirements
and an according adaptation and refinement, it also has to be substantiated by
concrete operationalizations of the individual stages. At this point, it is unclear
how the stages should be executed. To complete the design cycle, the procedure
model is supposed to be evaluated in practical case studies, where it is used to
inductively develop a reference model for a certain context. These case studies
have yet to be determined, planned, and executed.
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