Despite there exist di erent proposals for reusing existing well-designed artifacts for process modelling and capturing variability in business process models, most of them su er from three major shortcomings: lack of automation support in (i) maintaining the con gurable process model, (ii) modelling sub-processes as stand alone entities and (iii) user-centricity and decision support in choosing the suitable alternatives. In this context, we propose a framework that allows for reusing process models. We present in this paper our ongoing research in de ning this framework that contains a data structure and its construction principles as well as rst thoughts about maintaining it when adding process variants.
Process Aware Information Systems (PAISs) [
A business process model is a graphically designed network based on symbols
that represent procedures or activities [
Business process modelling phase, requires intensive knowledge related to
both (i) the business domain and (ii) the modelling language. In fact, the business
modeler has to de ne exactly what tasks must be captured, their execution logic
as well as how to specify them through a modelling language. Designing high
quality process models from scratch is often time consuming, error-prone and
costly [
The advent of Reuse-Oriented Development (ROD) in BPM brings a number of
reference models and repositories of process templates. They are used to design
business process models exploiting proven practices [
In addition, con gurable process models enable to capture variants of large process models. However, they do not allow for modelling sub-processes within these large models. A customizable process model that manages both processes and sub-processes is needed.
At a rst step of this research, we have identi ed the following requirements for reusing business process models: 1. Resource e ciency : First, there is a need to have an approach that enables managing process variants in an e cient way. By e cient way we mean overtaking both resource redundancy and inconsistency problems. 2. Maintainability : Second, an approach should support automatic changes. By support automatic changes we refer to adding or removing a variant from the con gurable process model with an automation support. 3. Modularity : Third, an approach should allow for modelling both processes and their sub-processes. 4. User-centricity : Fourth, an approach should not require much modelling skills from the end users. 1.2
The research goal we are trying to reach in this thesis is summarized as follows: Develop an e cient approach that allows for reusing existing process models without (or with little) modelling experience. Our approach should o er a con gurable model, provide an automation support for its maintainability and allow for modelling both processes and sub-processes.
To manage con gurable process modelling in an e cient way, we suggest providing a framework that captures the di erent process variants. A process variant, as it is understood here, is a single process model that achieves a given goal. Such a framework should be easily updated via an automation support. It should also enable business process modelers to de ne the most suitable (sub)process variant that suits their requirements.
The rest of this paper is divided into three sections. Section 2 shows how current solutions handle business process model reuse. Section 3 provides our preliminary work in de ning a framework to represent a con gurable process model. And nally section 4 concludes the paper and gives brief overview of our future work. 2
Several solutions have been investigated to support the reuse of business process models. We classify these solutions into two categories: (i) discovery of existing models and (ii) adaptation of a structure that captures possible model's variants. 2.1
Under this rst category, we distinguish two possible approaches. The rst one consists of discovering an entire business process model by querying a repository and selecting the most suitable one [8{10]. This method su ers from resource redundancy because it does not consider common parts of process model's variants and consequently they are duplicated in each entry of the repository. In addition, if any changes must be applied on the common parts of process variants, all the entries must be updated and from this arises the inconsistency problem.
The second approach overcomes the problem of resource redundancy and
inconsistency by considering business process building blocks instead of entire
models. This approach consists of discovering these building blocks and
aggregating them in order to construct a business process model [
Here we consider solutions that aim at merging di erent variants of a business process model into a single model. This is suitable for avoiding both resource redundancy and inconsistency problems. Under this category, we distinguish two possible approaches.
The rst one provides a process model which contains placeholders that need
to be detailed during the modelling phase [
The second approach manages con gurable business process models [
This approach has been extended by \Questionnaire-driven Con guration of
Reference Process Models" [
However, a process modeler has to de ne the con gurable process model and sit with the domain expert in order to de ne domain constraints and their mapping to the con gurable model. Thus, adding a new variant in this framework, requires another meeting between the modeler and the domain expert in order to add a variant to the process model, de ne its mapping with the domain constraints and update the questionnaire model. Even if this solution is suitable for a user-centric modelling, it needs to be improved to support automation maintainability of both con gurable process model and questionnaire model.
In addition, con gurable process models presented in [
A Framework for Managing Process Variants To cope with the aforementioned research problem, we introduce a framework to represent con gurable process models. This framework consists of a data structure, a set of structuring principles and maintaining operations. At this stage we show how to maintain this data structure only when adding a new variant. In this rst version of our study, we consider a business process model as a sequence of business goals. 3.1
Our framework de nes a data structure as a tuple CPM=f , , g where: { { {
represents the set of business goals involved in the whole con gurable business process model,
represents the set of abstract business goals (will be introduced later in this paper) and
represents the possible variants of each business goal that can be a sequence of business goals. Entries of are presented as: BusinessGoal1 : BusinessGoal2( BusinessGoalN ) such that fBusinessGoal1; BusinessGoal2; :::BusinessGoalN g [ and \BusinessGoal2 BusinessGoal3" is the sequence between the two business goals (i.e., BusinessGoal2 and BusinessGoal3).
This means that possible variants of BusinessGoal1 are presented as a sequence of other business goals. We call BusinessGoal1 a variation point and BusinessGoal2( BusinessGoalN ) a variant.
The framework should respect a set of constraints/principles to assure that the data structure will remain valid and well-formed after an update operation. 1. Minimality : Each element of has to be de ned only once and should not be derived from other elements of . 2. Coverage: By necessity and nature, the framework must cover all de ned variants. 3. Consistency: Only de ned variants should be deduced from the framework. 3.2
When building the data structure, we start from a set of variants and we add them, one at a time, assuring that the principles de ned previously are not violated. When adding a new variant of a goal, the idea is to check, using matching detection, whether the variant (or parts of it) already exists in the data structure. There are three situations that may occur according to the matching degree between business goals of the new variant and those in the con gurable process model. 1. Perfect match: In this case the current variant to be inserted is entirely found in the current data structure. In such situation there is no action to be taken and the data structure remains as it. 2. No matching: In this case the current variant to be inserted is not found in the current data structure, not even partially. The variant is inserted as follows: all business goals composing the variant are added to and the variant description is added to .
Example: We want to insert the variant A:G-H-I in CP M1 = f 1, 1, 1g where: { 1 = fA, B, C, D, E, Fg { 1 =f g { 1 = fA:B-C, A:F-E, C:D-Eg The updated con gurable process model is then CP M1=f 1, 1, 1g where: { 1 = fA, B, C, D, E, F, G,H,Ig { 1 =f g { 1 = fA:B-C, A:F-E, C:D-E, A:G-H-Ig Using a tree representation, the variant is inserted as an alternative of the variation point as shown in Fig. 11. 3. Partial match: An intermediary situation is when a partial match occurs between the process variant to be inserted and the current data structure. In this case we distinguish two possible situations: { The rst situation occurs when the new variant has common parts with another variant of the same business goal. A typical example is depicted in Fig. 2. This example shows adding the variant A:B-H-I in CP M1 = f 1, 1, 1g where:
1 = fA, B, C, D, E, F, Gg 1 All gures in the paper are following the BPM notation: www.bpmn.org
A B
C
F
E D
E -a
Add A:G-H-I no match
A B
C
F
E
G
H
I D
E -b1 =f g 1 = fA:B-C, A:F-G, C:D-Eg The new variant A:B-H-I has B in common with A:B-C. To add this variant, an abstract business goal is introduced to replace the di erent parts of these variants. The updated con gurable process model is then CP M1=f 1, 1, 1g where: 1 = fA, B, C, D, E, F, G, H, Ig 1 =f g 1 = fA:B- , :C, :H-I,A:F-G, C:D-Eg { The second situation occurs when the new variant has common parts with another variant but not of the same business goal. A typical example is depicted in Fig. 3. This example shows adding the variant A:D-F in CP M1 = f 1, 1, 1g where: 1 = fA, B, C, D, Eg 1 =f g 1 = fA:B-C, B:D-Eg This situation is similar to the second case (no match) and the updated con gurable process model is then CP M1=f 1, 1, 1g where: 1 = fA, B, C, D, E, Fg
C D
E -a
Add A:D-F partial match
A B
C
D
F D
E -bReusing process models is an important concept for Business Process Management because it can decrease the modelling time and reduce the business user's work and risk to make errors.
Several solutions have been provided for process model reuse. Despite their bene ts, they have not yet been adopted to capture con gurable process models. This is mainly due to the issues such as the lack of automation support in handling changes (i.e., adding or removing process variants).
In this thesis we propose a framework for managing con gurable process models. It de nes a data structure that captures process model variability at the business goal level. We presented a set of principles that the proposed data structure has to comply with and we show how it is maintained when adding a new variant.
Our work is still in an early stage and continuous improvements are planned as a future work: { In the near future, we plan to formally de ne construction principles. Further construction principles could be considered as well. { A number of maintaining operations have not yet been explored or are still under de nition, for example the deletion of a variant. { We intend to investigate and extend this framework in order to consider other block patterns (we have presented only sequence pattern in this paper). { From exploitation point of view, we intend to capture con guration parameters at each variation point. These parameters will help for generating a question ow to assist users in de ning their process models. { Our framework is not exclusively designed for managing process variability.
We plan to experiment our approach in the context of Mashups applications development.
This thesis is supervised by Dr. Sami Bhiri from the Service Oriented Architecture Unit, National University of Ireland Galway, Digital Enterprise Research Institute. This work is funded by the Lion II project supported by Science Foundation Ireland under grant number 08/CE/I1380.