scieee Science in your language
[en] (orig)
A Flexible Approach for Abstracting and Personalizing
Large Business Process Models
Jens Kolb
Ulm University,
Germany
jens[email protected]
Manfred Reichert
Ulm University,
Germany
manfred.reicher[email protected]
ABSTRACT
In process-aware information systems (PAISs), usually, dif-
ferent user groups have distinguished perspectives on the
business processes supported and on related business data.
Hence, personalized views and proper abstractions on these
business processes are needed. However, existing PAISs do
not provide adequate mechanisms for creating and visualiz-
ing process views and process model abstractions. Usually,
process models are displayed to users in exactly the same
way as originally modeled. This paper presents a flexible
approach for creating personalized views based on param-
eterizable operations. Respective view creation operations
can be flexibly composed to either hide non-relevant process
information or to abstract it. Depending on the parameter-
ization of the selected view creation operations, one obtains
process views with more or less relaxed properties, e.g., re-
garding the degree of information loss or the soundness of
the resulting model abstractions. Altogether, the realized
view concept allows for a more flexible abstraction and vi-
sualization of large business process models satisfying the
needs of different user groups.1
Categories and Subject Descriptors
D.2.2 [Design Tools and Techniques]: Computer-aided
software engineering (CASE); H.1.2 [User/Machine Sys-
tems]: Human factors
General Terms
Management, Design, Human Factors
Keywords
Process Model Abstraction, Process View, View Update,
Process Visualization, Human-oriented Business Process Man-
agement, Process Change
1. INTRODUCTION
Process-aware information systems (PAISs) provide support
for business processes at the operational level [1]. A PAIS
strictly separates process logic from application code, rely-
ing on explicit process models. This enables a separation of
concerns, which is a well established principle in computer
1Copyright is held by the authors. This work is
based on an earlier work: SAC’12 Proceedings of
the 2012 ACM Symposium on Applied Comput-
ing, Copyright 2012 ACM 978-1-4503-0857-1/12/03.
http://doi.acm.org/10.1145/2245276.2232043
science to increase maintainability and to reduce costs of
change [2]. The increasing adoption of PAISs has resulted
in large process model collections (cf. Figure 1). In turn,
each process model may refer to different domains, orga-
nizational units, and user groups, and comprise dozens or
even hundreds of activities (i.e., process steps) [3]. Usually,
different user groups need customized views on the process
models relevant for them, enabling a personalized process
model abstraction and visualization [4, 5, 6, 7]. For exam-
ple, managers rather prefer an abstract process overview,
whereas process participants need a more detailed view of
the process parts they are involved in.
PMS4:LargeProcessModel
Illustration.
One of our partners from the automotive domain has provided us with detailed insights into product
planning (PP), which constitutes a core process in vehicle development [1]. The part of the PP process
we considered, for example, comprises a large number of activities for planning production facilities
and resources. Furthermore, it defines the flow of about 50 relevant documents. When studying this
case we got access to a large model that was plotted on a 1,5 m x 5 m wallpaper - a fragment of this
process is depicted in Fig. XX1. Altogether, the PP process comprises several hundreds activities with
complex inter-dependencies. Furthermore, there exists a process handbook with detailed descriptions
of each activity. This handbook mainly serves for training purposes and provides detailed task
descriptions. – From interviews with process owners we have learned that the current model contains
several flaws, is known in its complete form to only very few experts, and is outdated in certain parts.
In particular, the model is considered as being too large and costly regarding its maintenance.
Interestingly, due to an enterprise-wide harmonization initiative the current process model needs to be
transformed into another notation as well as into a more comprehensible form.
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Fig.XX1:Fragmentoftheproductplanningprocesswithabout100activities
Reference
[1]Bobrik,Ralph(2008)ConfigurableVisualizationofComplexProcessModels.PhDThesis,
UniversityofUlm.
Figure 1. Complex Process Model (Partial View)
Hence, providing personalized process views is a much needed
PAIS feature. Several approaches for creating process model
abstractions based on process views have been proposed [8,
9]. However, none of them provide parametrizable opera-
tions to assist users in easily creating or changing process
views. Furthermore, existing approaches do not consider
another fundamental aspect of flexible PAISs: change and
evolution [1, 2]. More precisely, it is not possible to change
a large process model through editing or updating one of its
view-based abstractions.
APPLIED COMPUTING REVIEW MAR. 2013, VOL. 13, NO. 1
6
In the proView2project, we address these challenges in an
integrated and consistent way by supporting the creation
and visualization of process views as well as enabling users
to change a process model through updates of a related pro-
cess view. In this context, all other views associated with
the changed process model need to be migrated to the new
model version as well. Besides view-based abstractions and
changes, proView allows for alternative process model ap-
pearances (e.g., tree-, form-, and diagram-based represen-
tation) as well as interaction techniques (e.g., gesture- vs.
menu-based) [10, 11, 12, 13]. Note that the proView project
extends previous results from our Proviado project [14, 15]
by providing sophisticated change features and process vi-
sualizations. Our overall goal is to enable domain experts
to“understand”and“interact”with the (executable) process
models they are involved in.
Visualization Engine
Change Engine
CS2CS1 CS3
Migrate
Views
Create
Appearance
Create
Schema Refactor
Business Process 1
View 1
4
5 6 7
Execution & Monitoring
Engine
execute
visualize
change
...
Refactor
3Update
CPM
2
PAIS1
ü ü
ü
PAIS2
ü ü
ü
PAISn
ü ü
ü
CPM View 2
View 3
1
Figure 2. The proView Framework
Figure 2 gives an overview of the proView framework: A
business process is captured and represented through a Cen-
tral Process Model (CPM). In addition, for a particular CPM,
so-called creation sets (CS) are defined. Each creation set
specifies the schema and appearance of a particular process
view. For defining, visualizing, and updating process views,
the proView framework provides engines for visualization,
change, and execution & monitoring.
The visualization engine generates a process view based on
a given CPM and the information maintained in a creation
set CS, i.e., the CPM schema is transformed to the view
schema by applying the corresponding view creation oper-
ations specified in CS (Step 5
). Afterwards, the resulting
view schema is simplified by applying well-defined refactor-
ing operations (Step 6
). Finally, Step 7
customizes the
visual appearance of the view, e.g., creating a tree-, form-,
or activity-based appearance [10, 14].
When a user updates a view schema, the change engine is
triggered (Step 1
-4
), which updates the process schema of
underlying CPM and updates all associated process views.
[16] gives detailed insights into the proView architecture and
the view update operations based on which business process
models can be changed through updating process views.
This paper focuses on the parameterizable visualization en-
gine component (cf. Figure 2), i.e., on the provision of a
flexible and parameterizable component for creating process
views and process model abstractions, respectively. Such a
component must cover a variety of use cases. For exam-
ple, it should be possible to create views only containing
activities the current user is involved in or only showing
non-completed process regions. As another example con-
sider executable process models, which often contain techni-
2http://www.dbis.info/proView
cal activities (e.g., data transformation steps) to be excluded
from visualization. Finally, selected process nodes may have
to be hidden or aggregated to meet confidentiality needs [17].
The proView framework allows creating respective process
views based on well-defined, parameterizable view opera-
tions. These rely on both graph reduction and graph ag-
gregation techniques. While the former can be used to re-
move nodes from a process model, the latter are applied to
abstract from certain process information (e.g., aggregat-
ing several activities to one abstract node). Additionally,
proView supports the flexible composition of basic view op-
erations to realize more sophisticated process model abstrac-
tions. The basic idea of creating process views has been al-
ready sketched in the context of Proviado [15, 18]. In this pa-
per, we introduce more advanced view operations and their
formal properties. Further, we outline their implementation.
Finally, we combine elementary view creation operations en-
abling parameterizable high-level view operations.
Section 2 gives background information required to under-
stand this paper. Section 3 introduces the formal founda-
tions of parameterizable process views. Section 4 gives in-
sights into practical issues and presents more complex ex-
amples for defining and creating process views. Section 5
presents the proof-of-concept prototype and a first valida-
tion we conducted. Section 6 discusses related work and
Section 7 concludes with a summary.
2. BACKGROUNDS
Each process is represented by a process schema consist-
ing of process nodes and the control flow between them (cf.
Figure 3). For control flow modeling, control gateways (e.g.,
ANDsplit, XORsplit) and control edges are used.
Definition 1 (Process Schema): Aprocess schema is defined
by a tuple P= (N, E, EC, NT, ET) where:
•Nis a set of process nodes,
•E⊂N×Nis a precedence relation
(notation: e= (nsrc, ndest)∈E),
•EC :E→Conds ∪ {True}assigns optionally transi-
tion conditions to control edges,
•NT :N→{Activity, ANDsplit, ANDjoin, ORsplit,
ORjoin, XORsplit, XORjoin}assigns to each n∈Na
node type NT (n); Nis divided into disjoint sets of ac-
tivity nodes A(NT =Activity) and gateways S(NT 6=
Activity).
•ET :E→ {ControlEdge, LoopEdge}assigns a type ET (e)
to each edge e∈E.
B
MK
G
JH
D
L
IA
C
PQ
R
s
t
FE
UT V
S
Activity
AND Split
Control Flow Edge XOR Split
XOR Join
branching condition
Activity States:
Complete
Running
Activated
Skipped
SESE block
(Single Entry Single Exit)
AND Join
x
u
z
x
Figure 3. Example of a Process Instance
Note that this definition focuses on the control flow per-
spective. In particular, it can be applied to existing activity-
oriented modeling languages (e.g., BPMN). Additional, view
APPLIED COMPUTING REVIEW MAR. 2013, VOL. 13, NO. 1
7
creation operations specifically addressing the data flow are
presented in [19]. Furthermore, the handling of loop struc-
tures is described [20].
We assume that a process schema has one start and one end
node. Further, it has to be connected; i.e., each activity
can be reached from the start node, and from each activity
the end node is reachable. Finally, branches may be arbi-
trarily nested, but must be safe (e.g., a branch following a
XORsplit must not merge with an ANDjoin).
Definition 2 (SESE): Let P= (N, E, EC, NT, ET) be a pro-
cess schema and let X⊆Nbe a subset of activity nodes.
The subgraph P0induced by Xis called SESE (Single En-
try Single Exit) fragment iff P0is connected and has exactly
one incoming and one outgoing edge connecting it with P.
If P0has no preceding (succeeding) nodes, P0has only one
outgoing (incoming) edge.
Based on a process schema P, related process instances can
be created and executed at run-time. Regarding the pro-
cess instance from Figure 3, for example, activities Aand B
are completed, Cis activated (i.e., offered as work items in
user worklists), His running, and Kis skipped (i.e., is not
executed). Generally, a large number of process instances
might run on a given process schema.
Definition 3 (Process Instance): Aprocess instance Iis de-
fined by a tuple (P, NS, H) where
•Pdenotes the process schema on which Iis running,
•NS :N→ExecutionStates := {NotActivated, Acti−
vated, Running, Skipped, Completed}describes the ex-
ecution state of each node n∈N,
• H =he1,...,enidenotes the execution history of I
where each entry ekis related either to the start or
completion of a particular process activity.
For an activity n∈Nwith NS(n)∈ {Activated, Running},
all preceding activities either must be in state Completed
or Skipped, and all succeeding activities must be in state
NotActivated. Further, there is a path πfrom the start
node to nwith NS(n0) = Completed ∀n0∈π.
B
MK
G
JH
D
L
IA
C
PQ
R
s
t
FE
HIJKLM
C
AB PQ
s
t
UT V
S
TUV
create view
Aggregate(A,B)
Aggregate(H,I,J,K,L,M)
Reduce(E,F,G)
Reduce(R,S)
Aggregate(T,U,V)
D
a) Central Process Model:
b) Related Process View:
Abstracted Nodes
Figure 4. Example of a Process View
3. FUNDAMENTALS ON VIEW CREATION
We first introduce basic view creation operations and reason
about the properties of the resulting process view schemas.
As first example consider the process schema from Figure 4a.
Assume that each of the activity sets {A, B},{H, I, J, K, L,
M}, and {T, U, V }shall be aggregated, i.e., the process frag-
ments induced by the respective activity set shall be replaced
by one abstract activity. Further, assume that activity sets
{E, F, G}and {R, S}shall be hidden from the user. Fig-
ure 4b shows a possible process view resulting from respec-
tive process model aggregations and reductions.
Generally, process views exhibit an information loss when
compared to the original process (i.e., central process model
(CPM)). As important requirement, view creation opera-
tions should have a precise semantics and be applicable to
both process schemas and instances. Further, it should be
possible to remove process nodes (i.e., reduction) or to re-
place them by abstracted ones (i.e., aggregation). When
creating process views, it is fundamental to preserve the
structure of non-affected process regions. Finally, the ef-
fects of view creation operations should be parameterizable
to meet application needs best and to be able to control the
degree of information loss in a flexible manner.
We first give an abstract definition of a process view. Note
that the concrete properties of such a view depend on the
view operations applied and their parameterization as spec-
ified in a respective creation set (CS).
Definition 4 (Process View): Let P= (N, E, EC, NT, ET )
be a process schema (i.e., central process model) with ac-
tivity set A⊆N. Then: A process view on Pis a process
schema V(P) = (N0, E0, EC0, NT 0, ET0) whose activity set
A0⊆N0can be derived from Pby reducing and aggregating
activities from A⊆N. Formally:
•AU=A∩A0denotes the set of activities present in
both Pand V(P),
•AD=A\A0denotes the set of activities present in P,
but not in V(P); i.e., reduced or aggregated activities:
AD≡AggrNodes ∪RedNodes
•AN=A0\Adenotes the set of activities present in
V(P), but not in P.
Each a∈ANis an abstract activity aggregating a set
of activities from A:
1. ∃AggrNodesi, i = 1,...,n with
AggrNodes =
•
S
i=1,...,n
AggrNodesi
2. There exists a bijective function aggr with:
aggr :{AggrNodesi|i= 1,...,n} → AN
Using the notions from Definition 4 for a given central pro-
cess model Pand related view V(P), we introduce function
V Node :A→A0. This function maps each process activ-
ity c∈AU∪AggrNodes to a corresponding activity in the
respective process view:
V Node(c) =
c c ∈AU
aggr(AggrNodesi)∃i∈ {1,...,n}:
c∈AggrNodesi
undefined c /∈AU∪AggrNodes
For each view activity c0∈A0,V Node−1(c0) denotes the
corresponding activity or the set of activities aggregated by
c0in the central process model.
Finally, more complex process views are created by compos-
ing a set of view operations, which also define the semantics
of the process view (cf. Section 4).
APPLIED COMPUTING REVIEW MAR. 2013, VOL. 13, NO. 1
8
Advertisement
3.1 Creating Process Views Based on Schema
Reduction
Any view management component should be able to remove
activities in a process schema. For example, this is required
to hide irrelevant or confidential process details from a par-
ticular user group. For this purpose, proView provides an
elementary reduction operation (cf. Figure 5b). Based on it,
higher-level reduction operations for hiding a set of activities
are realized. Reduction of an activity (i.e., RedActivity)
is realized by removing its node together with its incom-
ing/outgoing edges from the process schema. Then, a new
control edge is inserted between the predecessor and succes-
sor of the removed activity (cf. Figure 5b). For reducing
activity sets, the single-aspect view operation ReduceCF is
provided. Single-aspect operations focus on elements of one
particular process aspect. Reduction is performed stepwise,
i.e., for all activities to be removed, operation RedActivity
is applied (cf. Figure 5a).
REDUCECF({B,C,D})
a)
C D EA
C D EBA
D EA
A E
RedActivity(B)
RedActivity(C)
RedActivity(D)
ADF
A D F
A
B
F
D
A
B
F
D
BA
rs
t
BA
r Ú s
t
b)
c)
d) e)
AB
C
AC
s
t
s
t
RedActivity(B)
Figure 5. Reduction and Refactoring Operations
Note, the algorithms we apply are context-free and may in-
troduce unnecessary process elements (e.g., empty branches).
Respective elements are purged afterwards by applying well-
defined, behaviour-preserving refactoring rules to the cre-
ated view schema [21]. For example, when reducing a com-
plete branch of a parallel branching, the resulting control
edge may be removed as well (cf. Figure 5d). In case of an
XOR-/OR-branching, however, the empty path (i.e., con-
trol edge) needs to be preserved. Otherwise an inconsistent
schema would result (cf. Figure 5b). Similarly, when apply-
ing simplification rules to XOR-/OR-branches, respective
transition conditions must be recalculated (cf. Figure 5c).
Note that any reduction of activities is accompanied by an
information loss, while preserving the structure of the non-
reduced schema parts, i.e., the activities present in both the
process and the process view schema. The latter can be ex-
pressed using the notion of order preservation. For this, we
introduce partial order relation (⊆N×N) on a process
schema Pwith n1n2⇔ ∃ path πin Pfrom n1to n2.
Definition 5 (Order-Preserving Views): Let P= (N, E, EC,
NT, ET) be a process schema with activity set A⊆N
and let V(P)=(N0, E0, EC0, NT 0, ET0) be a view on P
with activity set A0⊆N0. Then: V(P) is called order-
preserving iff ∀n1, n2∈A with n16=n2and n1n2:n0
1=
V Node(n1)∧n0
2=V Node(n2)⇒ ¬(n0
2n0
1).
This property expresses that the order of two activities
in a process schema must not be reversed in a correspond-
ing view. Obviously, the reduction operations depicted in
Figure 5ab are order preserving. Generally, this property is
fundamental for ensuring the integrity of process schemas
and related view schemas. A stronger notion is provided
by Definition 6. As we will see later, in comparison to Fig-
ure 5ab there are view operations which do not comply with
Definition 6.
Definition 6 (Strong Order-Preserving Views): Let P=
(N, E, EC, NT, ET) be a process schema with activity set
A⊆Nand let V(P)=(N0, E0, EC0, NT 0, ET 0) be a corre-
sponding view with A0⊆N0. Then: V(P) is strong order-
preserving iff ∀n1, n2∈A with n16=n2and n1n2:n0
1=
V Node(n1)∧n0
2=V Node(n2)⇒n0
1n0
2.
3.2 Creating Process Views Based on Schema
Aggregation
The aggregation operation allows merging a set of activities
into one abstracted activity. Depending on the structure
of the subgraph induced by the respective activities, dif-
ferent schema transformations have to be applied. In par-
ticular, the aggregation of non-connected activities necessi-
tates a more complex restructuring of the original process
schema. Figure 6 shows the elementary operations provided
for creating aggregated views. The depicted operations fol-
low the policy to substitute the activities in-place by an
abstract node (if possible), while ensuring properly order-
preservation (cf. Definition 5). Note that in-place substitu-
tion is always possible when aggregating a SESE fragment
(cf. Definition 2). If none of the operations from Figure 6ade
can be applied, in turn, AggrAddBranch (cf. Figure 6b) is
used. It identifies the nearest common ancestor and suc-
cessor of all the activities to be aggregated and adds a new
branch between them (cf. Figure 6b). Alternatively, aggre-
gation of non-connected activities can be handled by apply-
ing elementary operations of type AggrSESE (cf. Section 4).
Finally, when aggregating activities directly following a split
node, there exist two alternatives (cf. Figure 6e): the first
one aggregates activities applying AggrAddBranch, the sec-
ond one shifts activities to the position preceding the split
node (i.e., AggrShiftOut).
Except AggrAddBranch, the presented operations are strongly
order-preserving (cf. Definition 6). However, AggrAddBranch
violates this property. For example, in Figure 6b, order re-
lation DEcan not be preserved when applying this op-
eration.
Definition 7 (Dependency Set): Let P= (N, E, EC, NT, ET )
be a process schema with activity set A⊆N. Then: DP=
{(n1, n2)∈A×A|n1n2}is denoted as dependency set
reflecting all direct and transitive control flow dependencies
between any two activities.
We are interested in the relation between the dependency set
of a process schema and a related view schema. For this pur-
pose, let DPbe the dependency set of Pand DV(P)be the de-
APPLIED COMPUTING REVIEW MAR. 2013, VOL. 13, NO. 1
9
C DBA E
DA
BCE
AggrAddBranch
ZA
A Z
SESE
N
AggrSESE b)a)
AB
ED
C
A
E
C
Alternative 1: Alternative 2:
BD
AggrAddBranch AggrShiftOut
AE
C
BD
A
B
GF
C
ED
r
s
t
AGF
r Ú s
t
AggrComplBranches
BCDE
d)
AB C
ED
r
s
AB
E
r
s
r Ú s CD
AggrAddBranch
c)
e)
Figure 6. Elementary Aggregation Operations
pendency set of view V(P). We further introduce a projec-
tion of the dependencies from V(P) on Pdenoted as D0
V(P).
The latter can be derived by substituting the dependencies
of the abstract activities by the ones of the original activities.
As example consider AggrShiftOut in Figure 6e. We ob-
tain DP={(A, B),(B, C),(C, F),(A, D),(D, E),(E, F)}and
DV(P)={(A, BD),(BD, C),(BD, E),(C, F),(E, F)}. Fur-
ther D0
V(P)={(A, B),(B, C),(D, C),(C, F),(A, D),
(B, E),(D, E),(E, F )}holds. As one can see, D0
V(P)con-
tains additional dependencies. We denote this property as
dependency-generating.
Calculation of D0
V(P):For n1∈AN,n2∈A0: remove
all (n1, n2)∈DV(P); insert {(n, n2)|n∈aggr−1(n1)}in-
stead (analogously for n2∈AN); finally insert the depen-
dencies between AggrNodes, i.e., DP[AggrNodes] = {d=
(n1, n2)∈DP|n1∈AggrNodes ∧n2∈AggrNodes}
Now we can classify effects on the dependencies between
activities when building a view.
Definition 8 (Dependency Relations): Let P= (N, E, EC,
NT, ET) be a process schema and V(P) be a corresponding
view schema. Let further DPand D0
V(P)be the dependency
sets as defined above. Then:
•V(P) is denoted as dependency-erasing iff there are
dependency relations in DPnot existing in D0
V(P)any-
more.
•V(P) is denoted as dependency-generating iff D0
V(P)
contains dependency relations not existing in DP.
•V(P) is denoted as dependency-preserving iff it is
neither dependency-erasing nor dependency-generating.
Generally, reduction operations are dependency-erasing. When
aggregating activities, however, there exist elementary op-
erations of all three types. In Figure 6, for example, Ag-
grSESE is dependency-preserving, while AggrAddBranch is
dependency-erasing since (B, C)/∈D0
V(P)holds. Finally,
AggrShiftOut is dependency-generating: (B, E)∈D0
V(P).
Theorem 1 expresses the relation between dependency prop-
erties (cf. Definition 8) and order-preservation property (cf.
Definition 5).
Theorem 1: Let P= (N, E, EC, NT, ET ) be a process schema
and let V(P) be a corresponding view. Then:
i) V(P) is dependency-erasing ⇒
V(P) is not strong order-preserving
ii) V(P) is dependency-preserving ⇒
V(P) is strong order-preserving
The proof of Theorem 1 is based on the definition of the
properties and dependency sets.
Proof : Let P= (N, E, EC, NT, ET ) be a process schema
and V(P) be a corresponding process view. Then:
i) V(P) is dependency-erasing and relation d= (a, b)∈
DPexists with d6∈ D0
V(P)(w.l.o.g., a∈AggrNodes
and b6∈ AggrNodes). Hence, a6 b. Let a0=V Node(a)
and b0=V Node(b), i.e., a∈A0
Nand b∈A0
U. Then
a06 b0based on the calculation of D0
V(P). If V(P) is
strong order-preserving, dependency a0b0relation
has to exist.
ii) V(P) is dependency-preserving, i.e., for all dependency
relations d= (a, b)∈DP:ab. The calculation
of D0
V(P)results in a0b0with a0=V Node(a) and
b0=V Node(b). The proof of Theorem 1 is based on
the transitivity of the relation .
To conclude, we have presented a set of elementary aggrega-
tion operations. Each of them fits to a specific ordering of
the activities to be aggregated. In Section 4.1 we combine
these operations into more complex ones utilizing the dis-
cussed properties. Furthermore, this section has focused on
the control flow schema of a process view. Generally, addi-
tional process aspects must be covered, including data ele-
ments, data flow, and process attributes (cf. Section 3.4) as
defined for the different process elements (e.g., activities or
data elements). When creating a process view, proView con-
siders these aspects as well. Regarding data elements, for in-
stance, Figure 7a shows an example of a simple aggregation;
in this example, data edges connecting activities with data
elements are re-linked when aggregating {B, C, D, E}in or-
der to preserve a valid model. More details about view oper-
ations covering the data flow perspective and corresponding
correctness issues are discussed in [19].
B
E
D
A
C
BCDEA
data
element
data flow
edge optional data flow
AggrShiftOut
D3
D1D2
D3
D1
D2
Figure 7. Aggregation of Data Elements
APPLIED COMPUTING REVIEW MAR. 2013, VOL. 13, NO. 1
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3.3 Applying Views to Process Instances
So far, we have only considered views on process schemas.
This section additionally introduces views on process in-
stances (cf. Definition 3). When creating respective instance
views their execution state (i.e., states of concrete and ab-
stract activities) must be determined and the trace relating
to the instance view logically needs to be adapted. Exam-
ples are depicted in Figure 8a (reduction) and Figure 8b
(aggregation). Function V NS then calculates the state of
an abstract activity based on the states of the aggregated
activities.
V NS(X) =
NotActivated ∀x∈X:NS(x)/∈ {Activated, Run−
ning, Completed} ∧
∃x∈X:NS(x) = NotActivated
Activated ∃x∈X:NS(x) = Activated ∧ ∀x∈X:
NS(x)/∈ {Running, Completed}
Running ∃x∈X:NS(x) = Running ∨
∃x1, x2∈X:NS(x1) = Completed ∧
(NS(x2)=NotActivated ∨NS(x2)
=Activated)
Completed ∀x∈X:NS(x)/∈ {NotActivated,
Running, Activated}∧
∃x∈X:NS(x) = Completed
Skipped ∀x∈X:NS(x) = Skipped
Definition 9 (View on Process Instance): Let I= (P, NS, H)
be an instance of schema P= (N, E, EC, NT, ET) and
V(P)=(N0, E0, EC0, NT 0, ET0) be a view on Pwith cor-
responding aggregation and reduction sets AggrNodes and
RedNodes (cf. Definition 4). Then: V(I) on Iis a tuple
(V(P), NS0,H0) with:
•NS0:N0→ExecutionStates with NS0(n0) =
V NS(V Node−1(n0)) assigns to each view activity a
corresponding execution state.
• H0is the reduced/aggregated history of the instance.
It is derived from Hby (1) removing all entries ei
related to activities in RedNodes and (2) for all j:
replacing the first (last) occurrence of a start event
(end event) of activities in AggrNodesjand remove
the remaining start (end) events.
BC
C DBA
DA
C DBA
DA
AggrSESERedSESE b)a)
H: ‹S(A),E(A),S(B),E(B),S(C)›
H’: ‹S(A),E(A)›
H: ‹S(A),E(A),S(B),E(B),S(C)›
H’: ‹S(A),E(A),S(BC)›
c)
BD
A
E
C
AB
ED
C
Alternative 1:
AggrAddBranch
BDA E
C
Alternative 2:
AggrShiftOut
Figure 8. View Operations for Process Instances
Examples are depicted in Figure 8. Figure 8c shows the
scenario from Figure 6e with execution states added. Note
that applying AggrShiftOut yields an inconsistent state as
two subsequent activities are in state Running.
Definition 10 (State Consistency): Let I= (P, NS, H) be a
process instance and let V(I) be a corresponding view on I.
Then:
•V(I) is strong state-consistent iff for all paths π
(cf. Section 2) in V(I) from start to end, and not con-
taining activities in state Skipped, there exists exactly
one activity in state Activated or Running.
•V(I) is state-consistent iff for all paths πin V(I)
from start to end and not containing activities in state
Skipped, there does not exist more than one activity
in state Activated or Running.
As indicated in Figure 8a, reducing activities from a process
instance may result in a “gap” during instance execution, if
no activity is in state Running or Activated. Hence, reduc-
tion is not strong state-consistent. Theorem 2 shows how
state inconsistency is correlated with dependency relations
(cf. Definition 8):
Theorem 2: Let Ibe a process instance and V(I) a corre-
sponding view. V(I) is dependency-generating ⇒V(I) is
not state-consistent.
Proof: Let I= (P, NS, H) be a process instance of pro-
cess P= (N, E, EC, NT, ET) and V(I)=(V(P), NS0,H0)
be a view on Iwith V(P)=(N0, E0, EC0, NT 0, ET0) being
dependency-generating. Then, there exists n0
1, n0
2∈N0in
Vwhich generates a dependency, i.e., n0
1n0
2. Let n1=
V Node−1(n0
1)∈Nand n2=V Node−1(n0
2)∈N. Then:
n1n2. Further there are two paths in Pfrom start to end
containing n1and n2. Therefore, there exists a state of in-
stance Iwith NS(n1) = Running and NS(n2) = Running.
Thus, NS(n0
1) = Running and NS(n0
2) = Running. Since
there is a path from start to end in V(P), containing both
n0
1and n0
2, the claim follows.
Theorem 2 shows that AggrShiftOut always causes an in-
consistent execution state, whereas the application of oper-
ation AggrAddBranch maintains a consistent state.
Concerning the process instances, proView allows aggregat-
ing collections of them; i.e., multiple instances of the same
process schema may be condensed to an aggregated one
in order to provide abstracted information regarding their
progress or key performance data.
3.4 View Operations Affecting Attributes of
Process Elements
Process nodes are not elementary, but constitute complex
objects comprising various process attributes (e.g., attributes
of an activity may be cost, start time, and end time). In
Figure 9, activities A, B, and Care aggregated into an ab-
stracted activity ABC. When applying this aggregation,
related attributes must be aggregated as well. For this
purpose, proView provides transformation functions, which
may be applied to aggregate attributes, e.g., function CON-
CAT allows concatenating the name of aggregated activities.
APPLIED COMPUTING REVIEW MAR. 2013, VOL. 13, NO. 1
Table 1. Properties of View Creation Operations
Properties
Operation
str. order preserving
order preserving
str. state consistent
state consistent
depend. preserv.
depend. erasing
depend. generat.
RedActivity + + - + - + -
AggrSESE + + + + + - -
AggrComplBranches + + + + + - -
AggrShiftOut + + - - - - +
AggrAddBranch - + + + - + -
AggrAttr ++ ++ +++
BA C
name: A
start: 01.08.12
end: 05.08.12
cost: 2500
name: B
start: 07.08.12
end: 14.08.12
cost: 4100
name: C
start: 06.08.12
end: 07.08.12
cost: 600
ABC
name: ABC
start: 01.08.12
end: 14.08.12
cost: 7200
AggrSESE
name = concat(ni.name)
start = min(ni.start)
end = max(ni.end)
cost = sum(ni.cost)
Trans. Functions
Figure 9. Aggregation of Attributes
Generally, there exist two application scenarios in which
transformation functions are applied to process attributes:
•AS1 (Integrated): Transformation functions are inte-
grated with view creation operations (as indicated in
Figure 9). This scenario is particularly relevant for
aggregation operations.
•AS2 (Stand-Alone): Transformation functions are ap-
plied to aggregate or reduce selected process attributes
in the respective process view.
To discuss the application of transformation functions, Def-
inition 1 has to be enriched with attributes.
Definition 11 (Process Schema with Attributes): Aprocess
schema is defined by a tuple P= (N, E, EC, NT, ET, attr, val)
with:
•N, E, EC, NT, ET as defined in Definition 1 and Athe
set of supported attributes.
•attr :N∪E→AS assigns to each process element a
corresponding attribute set AS ⊆ A.
•val : (N∪E)×AS →valueDomain(AS) assigns to
an attribute a∈AS of process element n∈(N∪E) a
respective value:
val(n, a) = (value of a, a ∈attr(n)
null, a 6∈ attr(n)
Accordingly, a process view with attributes is denoted as
V(P)=(N0, E0, EC0, NT 0, ET0, attr0, val0). Further, A.x
denotes process attribute xof process node A.
In the context of a process schema, attributes may be grouped
into four categories:
•C1 (Activity State): This category includes process
attribute VNS representing the execution state of a
process node (cf. Section 3.3).
•C2 (Default Attributes): This category represents de-
fault attributes that are common to all process nodes.
For example, each process node comprises attributes
describing its name,start, and end time.
•C3 (Type-specific Attributes): This category comprises
all process attributes, which are only available for a
specific type of process node (e.g., activity or data el-
ement). Attribute cost, for example, is only available
for activities, but is undefined for gateways.
•C4 (Other Attributes): This category comprises all
process attributes not available for all occurrences of
a specific type of process node. For example, process
attribute personnel cost may be only available for ac-
tivities executed by a human resource.
In the following, view creation operations considering pro-
cess attributes are presented:
Attribute Reduction Operation: View creation opera-
tion ReduceAttr(A.x) removes attribute A.x in the corre-
sponding process view. For example, in Figure 10 attribute
A.z is reduced when creating the respective process view.
Generally, function ReduceAttr(A.x) may be applied to hide
specific attributes from a user for privacy reasons [17].
BA C BC
AggrSESE({B,C})
ReduceAttr(A.z)
AggrAttr(
{A.x,A.y},SUM)
A
A.x
A.y
A.z
B.x
B.y
B.z
C.x
C.y
C.z
A.xy BC.x
BC.y
BC.z
Figure 10. Combination of Attribute Operations
Attribute Aggregation Operation: View creation op-
eration AggrAttr(AS’, func) combines a set of process at-
tributes AS’ to an attribute using transformation function
func.
Definition 12 (Transformation Function): Based on a set of
attribute values, transformation function func calculates a
new attribute value. Let Wiwith i= 1,...,nand Wbe the
value domains of selected attributes as well as the abstracted
attribute
func :W1. . . Wn→W.
Depending on the type of attributes to be aggregated, var-
ious transformation functions can be applied. For exam-
ple, a transformation function aggregating activity execu-
tion states (i.e., VNS) has been introduced in Section 3.3.
Table 2 summarizes transformation functions supported by
proView. For example, function SUM adds numerical values
APPLIED COMPUTING REVIEW MAR. 2013, VOL. 13, NO. 1
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of attributes returning their sum (cf. Figure 10).
Control Flow Aggregation Operations: As aforemen-
tioned, when aggregating activities (e.g., using AggrSESE),
their attributes need to be aggregated as well (cf. Figure 10).
For this purpose, transformation functions, as introduced in
Definition 12, are applied. To automatically aggregate the
attributes of different activities, proView provides default
transformation functions. For example, attribute start (end)
time may be aggregated using the MIN (MAX) transforma-
tion function (cf. Table 2). For aggregating activity names,
function CONCAT can be applied. Generally, it is possible
to override such standard behaviour in a given context.
Finally, when aggregating XOR/OR branches, we must take
into account that not all branches will be executed in all
cases. For example, summing up all available attribute val-
ues in such a situation might result in erroneous attribute
aggregation. Regarding numerical transformation functions,
execution probabilities of the different branches are used to
calculate the attribute values expected. If such values are
not available, one might initially assume that all branches
are selected with the same probability. Note that for textual
transformation functions, this is not required.
Table 2. Trans. Functions for Attribute Values
Transformation Functions for Numeric Values
SUM sum of attribute values
AVG average of attribute values
MIN minimum of attribute values
MAX maximum of attribute values
COUNT number of attributes
Transformation Functions for Textual Values
CONCAT concatenation of attribute values
FIRST first value of attribute list
LAST last value of attribute list
MAXFREQ most frequently used attribute value
MINFREQ less frequent used attribute value
RANDOM random attribute value
4. ADVANCED VIEW CREATION
CONCEPTS
To enable more complex view creation operations, the ele-
mentary operations presented in Section 3 may be combined.
Table 1 summarizes the view properties we can guarantee
for the elementary operations described. Based on this, we
may reason about the properties of the views resulting from
the combined use of elementary operations. For any pro-
cess visualization component, however, manually selection
of the elementary operations applied in the given context
is inconvenient for users. Note that this would require in-
depth knowledge of the different operations and their seman-
tics. To tackle this challenge, proView additionally provides
single-aspect view operations on top of elementary opera-
tions. In turn, this allows us to cope with more complex use
cases as well. Single-aspect operations analyze the context
of the activities to be reduced or aggregated in a process
schema, and they automatically determine the appropriate
elementary operations required to build the view with the
desired properties.
4.1 Parameterizable View Creation Operations
One major use case of our process view framework is pro-
cess visualization. However, other use cases (e.g., process
modeling) are covered as well. Thus, different requirements
regarding the properties of the resulting view schema exist.
In one of our case studies, in the automotive domain, for
example, we have shown that for the visualization of large
process models minor inconsistencies or information loss will
be tolerated by the users as long as an appropriate visual-
ization can be obtained for them. Opposed to this, incon-
sistencies will not be accepted if process updates based on
views shall be enabled [16].
To deal with these varying requirements, proView expands
single-aspect operations with a parameter set. This allows
specifying the properties of the resulting view schema. The
parameters of operation AggregateCF, for example, are
summarized in Table 3; e.g., when aggregating activities di-
rectly succeeding an ANDsplit (cf. Figure 6e) and requiring
state-consistency of the resulting view AggrAddBranch has
to be chosen (cf. Figure 11a).
In certain cases, the specified parameters might be too strict;
i.e., no elementary view operations exist to realize the de-
sired properties. We provide two strategies for addressing
such scenarios. The first one subdivides the set of activities
until elementary operations can be applied and the desired
properties be ensured. The second strategy expands the
activity set to achieve this. Regarding reduction, in turn,
view generation is always possible due to the way activity
sets are split into single activities and the application of
RedActivity.
Figure 11 illustrates the use of the single-aspect operation
AggregateCF. It depicts a process schema together with
the set of activities to be aggregated. The view operation
analyzes the structure of the activities and determines which
elementary operation shall be applied. If the application of
these operations results in a view schema complying with the
properties defined by the desired parameters (dependencies,
execution states ), it is applied as shown in Figure 11a. If
parameter strategy forces us to process the set of activities
as it is, and an appropriate operation cannot be found, view
generation is aborted with an error message. Figure 11b
shows the result we obtain when expanding the activity set
to be aggregated to the minimum SESE-block that contains
all activities to be aggregated. Note that this strategy has
Table 3. Overview of Parameters for AggregateCF
Parameter Values1Description
dependencies preserving,
non-erasing,
non-generating,
any
The view operation applied
should be dependency-
preserving, not dependency-
erasing, or not dependency-
generating. Otherwise
no restrictions regarding
dependencies are considered.
exec. states inconsistent,
consistent
The view operation applied
should be state consistent or
may be state inconsistent.
strategy as-is,subdivide,
expand
Activity set should be aggre-
gated as-is, may be subdi-
vided or expanded.
1default values are printed in bold face
APPLIED COMPUTING REVIEW MAR. 2013, VOL. 13, NO. 1
TA BCDEFGHIJKLMNOPQRS
AggrAddBranch
({B,F,G,J,M,N,P,Q,R})
AggrSESE({B,C,D,E,F,G,H,
I,J,K,L,M,N,O,P,Q,R,S})
AGGREGATE({B,F,G,J,M,N,P,Q,R})
(Single-Aspect Operation)
AggrAddBranch({B,J,M,N})
AggrSESE({F,G})
AggrSESE({P,Q,R})
AggrShiftOut({B,J,M,N})
AggrSESE({F,G})
AggrSESE({P,Q,R})
AggrSESE({B})
AggrSESE({J})
AggrSESE({M,N})
AggrSESE({F,G})
AggrSESE({P,Q,R})
B
OM S
LJ
D
N
KA
C
P
F
H
s
t
R
E
Q
G
IT
B
OM S
LJ
D
N
KA
C
P
F
H
s
t
R
E
Q
G
IT
O
L
D
K
AS
H
s
t
EIT
BFGJMNPQR
C
O
L
D
K
AS
H
s
t
EIT
BJMN
CFG
PQR
O
L
D
KA S
H
s
t
EITBJMN
CFG
PQR
S
H
s
tIT
MN
FG
PQR
B
O
LJ
D
KA
C E
(Elementary Operation)
(Elementary Operation)
a
b
d
c
Central Process Model
strategy = as-is
states = default
dependencies = default
View Parameterization:
strategy = expand
states = consistent
dependencies = preserving
View Parameterization:
strategy = subdivide
states = default
dependencies = default
View Parameterization:
strategy = subdivide
states = consistent
dependencies = default
View Parameterization:
strategy = subdivide
states = consistent
dependencies = preserving
View Parameterization:
B
OM S
LJ
D
N
KA
C
P
F
H
s
t
R
E
Q
G
IT
B
OM S
LJ
D
N
KA
C
P
F
H
s
t
R
E
Q
G
IT
B
OM S
LJ
D
N
KA
C
P
F
H
s
t
R
E
Q
G
IT
Figure 11. Views Depending on Quality Parameters
been proposed in literature as well [22, 23]. Generally, for vi-
sualization purposes it is not always acceptable to aggregate
activities originally not contained in the aggregation set.
Figure 11cd can be derived by subdividing the set of ac-
tivities to be aggregated. This is done stepwise: First, all
connected fragments are identified (cf. Figure 11c). If the
aggregation of these fragments does not meet the required
properties, the fragments are further subdivided until each
subset constitutes a SESE (cf. Figure 11d).
Altogether, parameterization of view operations significantly
increases the flexibility of our view creation approach. Fur-
ther, it allows defining exactly the view properties to be
preserved. Considering reduction, a parameterization at the
level of single-aspect operations is not useful since reduc-
tion of a complex set of activities can be realized by calling
RedActivity repeatedly as explained in Section 3.1.
4.2 A Leveled Operational Approach for Re-
alizing Views
So far, we have presented a set of elementary and single-
aspect view creation operations. Additionally, proView of-
fers high-level operations hiding as much complexity from
end-users as possible. As configuration parameter, the single-
aspect view operations take the sets of activities to be re-
duced or aggregated, and then determine appropriate ele-
mentary operations. What is still needed are view opera-
tions allowing for a predicate-based specification of the re-
spective activity sets. Besides, operations with built-in in-
telligence are useful, e.g. ”show only the activities of a par-
ticular user role”.
To meet these requirements, proView organizes view oper-
ations into four layers (cf. Figure 12). Thereby, high-level
operations may access lower-level ones. For defining a view,
operations from all layers can be used.
Attribute Op.
High-level Operations
Data Flow Op.
Multi-Aspect Operations
Single-Aspect Operations
Elementary Operations
AGGREGATECF REDUCECF
Aggregate Reduce
ShowMyActivities ...AggrExecutedPart
Control Flow Attributes Application Data
Process
AggrSESE
AggrShiftOut
AggrAddBranch
RedActivity
AggrComplBranch
...
...
...
Figure 12. Multi-Layer View Operations
Elementary operations are designed for a specific ordering of
the selected activity set within the process schema (cf. Sec-
tion 3). Single-aspect operations receive a set of activities
as input to be processed. They analyze the structure of the
activities in the process schema and select the appropriate el-
ementary operations based on the chosen parameterization.
In turn, multi-aspect operations consider elements of differ-
ent type (e.g., activities, data elements) and delegate their
processing to single-aspect operations. High-level operations
abstract from the aggregations or reductions neccessary to
build a particular view: AggrExecutedPart only shows those
parts of the process schema, that still may be executed, and
aggregates already finished activities. Figure 13 shows an
example illustrating the way high-level operation AggrExe-
cutedPart is translated into a combination of single-aspect
and elementary operations, respectively. In a first step, all
activities with completed and skipped execution states are
determined (i.e., activites A, B, C, E, H, and I). Based on
this activity set AggregateCF with the following parameter
settings is applied: strategy=as-is, states=default, and
dependencies=default (cf. Section 4.1). Finally, in the
resulting process view the execution states are set.
AggrExecutedPart
High-level Operation:
Aggregate(A,B,C,E,H,I)
Multi-Aspect Operation:
AGGREGATECF(
A,B,C,E,H,I
)
Single-Aspect Operation:
AggrShiftOut
(A,B,C,E,H,I)
Elementary Operation:
Initial Process
Result
1.
2.
3.
4.
J
GF K
D
ABCEHI
A
JH
GE
C
I
F
B
K
D
Figure 13. Example of AggrExecutedPart
Another high-level operation, ShowMyActivities, extracts ex-
actly those parts of a process model the user is involved in.
APPLIED COMPUTING REVIEW MAR. 2013, VOL. 13, NO. 1
Advertisement
Figure 14 shows an example of applying this operation to
create a personalized process view for user U1. Operation
ShowMyActivities first determines all activities the selected
user U1 is not involved in and reduces them. Finally, refac-
toring operations are applied to simplify the control-flow.
Initial Process
High-level Operation:
Reduce({C,D,E,F,I,J,K,
N,P,J})
Multi-Aspect Operation:
Initial Process 1.
2.
A
C
B
D
J
F
I J K
GH
E
M
O P
U1
U1
U1
U1
U1
U1
N
U2
U2
U2
U3 U3
U3
U3
U4 U4
U3
ShowMyActivities(U1)
Determine all activities user U1
is not involved in:
S={C,D,E,F,I,J,K,N,P,J}
ReduceCF({C,D,E,F,I,J,
K,N,P,J})
Single-Aspect Operation:
3.
RedActivity(x)
for all x in {C,D,E,F,I,J,
K,N,P,J}
Elementary Operation:
4.
Initial Process 5.
A B G H
M
O
U1
U1
U1
U1
U1
U1
Refactoring Operation
Removing unnecessary
control-flow structures
Initial Process
A
G H
O
U1
U1
U1
U1
M
U1
B
U1
6.Final Process View for
User U1
A
C
B
D
J
F
I J K
GH
E
M
O P
U1
U1
U1
U1
U1
U1
N
U2
U2
U2
U3 U3
U3
U3
U4 U4
U3
Figure 14. Example of ShowMyActivities
Note that proView supports additional operations at the
different levels to cover data flow and attributes as well; e.g.,
to handle adjacent data elements when aggregating activities
(remove, aggregate, or maintain) [16, 20].
5. EVALUATION
The proView framework presented in this paper has been im-
plemented as a proof-of-concept prototype in a client-server
application. This prototype enables users to simultaneously
create and change process models based on process views
[24, 25]. Overall, the proView prototype demonstrates the
applicability of our framework (cf. Figure 15).
Figure 15. Proof-of-Concept Prototype
We further applied this prototype in an industry project,
e.g., to visualize the order handling process of a mid-sized
company for different user groups. This process consists
of 56 activities and involves six different user roles. In
the top right, Figure 15 shows this process; on the bot-
tom right, an automatically generated view of an involved
electrical-electronic engineer is displayed. This view is gen-
erated through high-level operation ShowMyActivities.
We automatically created views for each involved user mea-
suring the complexity of the resulting view schemas based on
well-known process metrics, i.e., number of activities, num-
ber of gateways, and McCabe metric measuring the com-
plexity of the control flow schema [26, 27]. The results are
depicted in Table 4. The first row shows the metrics for
the initial process model Order Process. In the rows below,
calculated metrics of the automatically generated views for
user roles clerk, accountant, electrical-electronic engineer,
mechanical engineer, and project manager are listed.
Table 4. Abstraction through High-Level Operation
Process #Activities #Gateways McCabe
CPM: Order Process 56 14 8
V1: Clerk 2 0 0
V2: Accountant 17 2 1
V3: EE Engineer 17 2 2
V4: Mech. Engineer 11 0 0
V5: Proj. Mgmr. 9 2 1
As it can be seen, when providing personalized process views
to users, process complexity can be reduced for them. In the
given scenario, the number of activities is reduced to 2-17
depending on the respective view, i.e., to 3%-30% of the ini-
tial process model. Furthermore, the number of gateways
decreases from initially 14 to 0-2 gateways, i.e., the result-
ing personalized process views have at most one branching
(i.e., one split and join gateway). Finally, McCabe metric
for control flow complexity is between 0 and 2 in the pro-
cess views, which is a significant decrease. The calculated
metrics show that process model size as well as complexity
is decreased in the process views.
Overall, this evaluation has shown promising results. In
particular, it becomes easier for process participants to un-
derstand those process aspects relevant for them.
6. RELATED WORK
IEEE 1471 recommends user-specific viewpoints for software
architectures [28]. These viewpoints are templates from
which individual views are created for a concrete software
architecture. Since this standard does not define any meth-
ods, tools or processes, proView could provide a powerful
framework in the context of PAIS. [29] introduces a meta
model for views and shows a general overview of process
view patterns. However, no implementation is provided.
Some view creation approaches deal with inter-organizational
processes and apply views to create abstractions of private
processes hiding sensitive process parts [30, 9, 31, 32]. In
particular, views are specified by the designer.
[8] presents an approach with predefined view types (i.e. hu-
man tasks, collaboration view). As opposed to proView, it
is limited to the specified view types and it is not possible
to define user-specific views. [33] applies graph reduction to
verify structural properties of process schemas. proView ac-
APPLIED COMPUTING REVIEW MAR. 2013, VOL. 13, NO. 1
complishes this via aggregation and respective high-level op-
erations. [34] uses SPQR-tree decomposition for abstracting
process models. This approach neither provides high-level
abstractions nor does it take other process aspects (e.g. data
flow) into account.
[35] determines semantic similarity between activities by
analysing the structural information of a process model. The
discovered similarity is used to abstract the given process
model. However, the approach neither distinguish between
different user perspectives on a process model nor does it
provide concepts to manually create process views.
An approach for creating aggregated views is presented in
[36]. It proposes a two-phase procedure for aggregating parts
of a process model that must not be shown to public. How-
ever, this approach focuses on block-structured graphs and
neither considers data flow nor attributes are considered.
Implementations of process views focusing on process mon-
itoring are presented in [37, 38]. These approaches focus
on the mapping of run-time information to process views.
Respective views have to be pre-specified manually by the
designer.
Several approaches exist that align business process mod-
els with technical workflow models [39, 40, 41, 42] and to
synchronize them with changes. However, neither an auto-
mated synchronization of changes nor high-level operations
are provided. In this context, proView supports high-level
operations to automatically create and update both business
and technical process models [25].
An approach enabling abstractions of large, object-centric
process structures is presented in [43]. In particular, state
abstractions and coordination components are used to visu-
alize (and execute) process structures.
The proView project provides a holistic framework for user-
centric view creation based on elementary as well as high-
level operations. Thereby, the behaviour and information
perspectives are taken into account. Additionally, it consid-
ers run-time information. None of the existing approaches
covers all these aspects. Furthermore, existing approaches
for creating views are based on rigid constraints not tak-
ing practical requirements into account. For example, our
first design of a visualization-oriented view mechanism was
based on reduction and aggregation techniques for block-
structured process graphs [20]. Presenting this solution to
business users, however, we figured out that block-structured
aggregation does not always meet the practical requirements
coming with the visualization of large processes. For this
reason, proView allows to flexibly specify the acceptable de-
gree of imprecision. A validation of proView was conducted,
where users are confronted with with complex, long-running
development processes [20].
7. SUMMARY AND OUTLOOK
We introduced the proView framework and its formal foun-
dation. Further, high-level view creation operations provide
the required flexibility since process schemas can be adapted
to specific user groups. Reduction operations provide tech-
niques hiding irrelevant parts of the process, whereas aggre-
gation operations allow abstracting from process details by
aggregating arbitrary sets of activities in one node. Finally,
parameterization of the respective operations allows specify-
ing the quality level the resulting view schema must comply
with. This enables adaptable process visualization not fea-
sible with existing approaches. We have implemented large
parts of the described view mechanism in a prototype and
evaluated it in the context of an industry project. For usabil-
ity reasons, it is important to provide appropriate methods
for defining and maintaining process views. Therefore, we
have designed and implemented a comprehensive set of user-
oriented, high-level operations as well as on a view definition
language. Both will be evaluated in a user experiment.
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