APPLYING ACTIVITY PATTERNS FOR DEVELOPING AN
INTELLIGENT PROCESS MODELING TOOL
Lucinéia Heloisa Thom, Manfred Reichert
Institute for Database and Information Systems, Ulm University, Oberer Eselsberg, 89069, Germany
Carolina Ming Chiao, Cirano Iochpe
Institute of Informatics, Federal University of Rio Grande do Sul, Av. Bento Goncalves 9500, 91501-970, Brazil
Keywords: Business function, process modeling, activity pattern, ontology.
Abstract: Due to their high level of abstraction and their reusability, workflow patterns are increasingly attracting the
interest of both BPM researchers and BPM tool vendors. Frequently, process models can be assembled out
of a set of recurrent business functions (e.g., task execution request, approval, notification), each of them
having generic semantics that can be described as activity pattern. To our best knowledge, so far, there has
been no extensive work implementing such activity patterns in a process modeling tool. In this paper we
present an approach for modeling business processes and workflows. It is based on a suite which, when
being implemented in a process modeling tool, allows to design business processes based on well-defined
(process) activity patterns. Our suite further provides support for analysing and verifying certain properties
of the composed process models (e.g., absence of deadlocks and livelocks). Finally, our approach considers
both business processes designed from scratch and processes extracted from legacy systems.
1 INTRODUCTION
Organizations are increasingly interested in
improving the efficiency and quality of their
business processes as well as in finding ways to
better cooperate with customers and business
partners (Weske, 2007), (Lenz, 2007). To achieve
these goals, enterprises are adopting Business
Process Management (BPM) tools as well as
emerging patterns for process modeling and change.
BPM technology (e.g., workflow systems) enable
the definition, enactment and monitoring of the
operational processes of an enterprise. Moreover,
through Web service technology, the benefits of
BPM can be created for cross-organizational
business processes involving linked organizations as
well. By automating processes BPM technology
contributes to the reduction of costs, execution
times, errors and redundancies with respect to
process performance. At the same time, it improves
control over the processes (Thom, 2006a).
Usually, business processes comprise a variety of
business functions, with a specific and well-defined
semantics. Thereby, a particular business function
may occur several times within one or multiple
process definitions (Thom, 2006b), (Thom, 2006c).
As example consider a simple approval process for
changing the layout of a product (cf. Figure 1).
Figure 1: Approval process for product layout change.
This process includes the following activities: (1) a
designer modifies the product layout according to
the requested changes; (2) the designer (optionally)
receives a notification when Activity 1 is delayed;
(3) an approval for the changed product layout is
requested; (4) the requested editor (optionally)
receives a notification when the approval is delayed.
Altogether this process comprises four (recurrent)
business functions with generic semantics that can
be described in terms of activity patterns. In detail,
112
Heloisa Thom L., Reichert M., Ming Chiao C. and Iochpe C. (2008).
APPLYING ACTIVITY PATTERNS FOR DEVELOPING AN INTELLIGENT PROCESS MODELING TOOL.
In Proceedings of the Tenth International Conference on Enterprise Information Systems - ISAS, pages 112-119
DOI: 10.5220/0001704801120119
Copyright
c
 SciTePress

the following business functions are used: task
execution request (Activity 1), notification
(Activities 2 and 4), and approval (Activity 3). We
denote these recurrent functions as workflow
activity patterns (activity patterns for short); i.e.,
activity patterns represent business functions that
occur several times within one or multiple process
models, and therfore might be reused when defining
other business processes.
Recently, workflow patterns have been
suggested capturing different process aspects:
control flow (Russell, 2006a), data flow (Russell,
2004a), resources (Russell, 2004b), exception
handling (Russell, 2006b), service interactions
(Barros, 2005), process change (Weber, 2007),
application–oriented aspects (Bancroft, 1998), and
process compliance (Namiri, 2007). Yet, all these
patterns have in common that they are relevant for
implementing BPM systems and for defining
adequate and expressive process modeling
languages.
However, all these patterns provide only a partial
answer to the question what business functions a
modeler wants to use repeatedly when designing
process models (Thom, 2007a), (Thom 2007b). In
practice, respective business functions (Medina-
Mora, 1992), (Flores, 1998), (Muehlen, 2002),
(Malone, 2004) are often re-defined from scratch for
almost every process definition. This, however, is
inefficient, and also undesirable from a maintenance
perspective. While some research has been reported
on how metadata can be organized to manage large-
scale modeling projects (see Thomas and Scheer
2006), we are not aware of any work evidencing the
existence of activity patterns for defining business
functions within real process models, or for
investigating their necessity and completeness with
respect to business process modeling. Besides that,
contemporary process modeling tools do not provide
functionalities that enable users to define, query, and
reuse such patterns in a proper and effective way.
Related to these problems, in earlier work we
proposed a set of seven workflow activity patterns.
Each of these activity patterns captures a recurrent
business function (such as the ones shown in Figure
1) we can find frequently in business processes.
Combined with specific control flow patterns,
respective activity patterns are suitable to design a
large variety of process models in different domains.
In this paper we briefly report on the results of an
empirical study in which we analyze the frequency
of activity patterns taking a set of 214 real-world
process models from domains like quality
management, software access control, and electronic
change management. For specific process categories,
we further discuss results of an additional analysis in
which we investigate the frequency of co-occuring
activity patterns. The result of this analysis is
utilized for developing an intelligent suite for
normalizing and modeling business processes based
on the reuse of activity patterns. Given some
information about the kind of process being
designed, the results of our analysis can be further
used by this suite to suggest a ranking of the activity
patterns suited best to follow the last pattern
modeled. With normalization we mean the
definition of a standard description form to which
the business processes are translated, i.e., a
canonical format for describing process models.
This suite, which we denote as Workflow
Modeling Tool in the following, can be added as an
extension to existing process modeling components
(e.g., Intalio (Intalio, 2006), Aris Toolset (IDS
Scheer, 2007), or ADEPT Process Composer
(Reichert, 2006)). Basically, the suite is intended to
provide a number of advanced modeling
functionalities, such as the: (1) extraction of
business processes from legacy systems and their
normalization, correctness checking and translation
into a standard notation; (2) support for designing
normalized process models by suggesting to the
designer activity patterns relevant in the given
modeling context (e.g., considering statistical co-
occurences of multiple patterns) ; (3) construction of
a knowledge base for storing and retrieving activity
patterns.
The remainder of this paper is organized as
follows: Section 2 gives an overview of the activity
patterns we identified in prior research. Exemplarily,
we present the approval and the decision patterns in
more detail. In Section 3 we discuss the results of
empirical studies we performed in different domains
in order to verify how often activity patterns are
used in the design of process models. In Section 4
we describe the suite that aims at supporting the
reuse of these activity patterns. Finally, Section 5
concludes the paper and gives an outlook on future
research.
2 ACTIVIY PATTERNS
In the context of this paper we use the term
Workflow Activity Pattern (WAP; activity pattern
for short) to refer to the description of a recurrent
business function that can be frequently found in
business processes (e.g., notification, decision,
approval). Initially, we derived seven activity
APPLYING ACTIVITY PATTERNS FOR DEVELOPING AN INTELLIGENT PROCESS MODELING TOOL
113

Table1: Overview of activity patterns representing business functions.
WAP - Name Description
WAP1:
Approval
An object (e.g., a document) has to be approved by one or more organizational roles.
WAP2:
Question-answer
WAP2 allows to formulate a question in the context of a process, to identify an organizational role who is able to answer
it, to send the question to this role, and to wait for the respective response (single-question-answer)
WAP3:
Unidirectional Performative
A sender requests the execution of a particular activity from another actor involved in the process. The sender continues
execution of his process part immediately after having sent the request for performing an activity..
WAP4:
Bi-directional Performative
A sender requests the execution of a particular activity from another actor involved in the process. The sender waits
until the receiver notifies him that the requested activity has been performed.
WAP5:
Notification
The status or result of an actvity execution is communicated to one or more process participants
WAP6:
Informative
An actor requests a certain information from a process participant. He continues process execution after having received
the requested information.
WAP7:
Decision
WAP7 allows to include a decision activity in the flow with connectors to different subsequent execution branches.
Those branches will be selected for execution whose transition conditions evaluate to true.
WAP1: APPROVAL
Description: An object (e.g. a document) has to be approved
by one or more organizational roles. Depending on the
context, the evaluation is executed only once or it is requested
multiple times (and approval is done either isequentially or in
parallel).
Example: In a change management process, for example, a
particular change request may have to be concurrently
approved by all organizational roles concerned by the change.
If one of these roles rejects the change request, it will be not
approved.
Problem: During the execution of a business process, object
approval by one or multiple organizational roles is required
before proceeding with the flow of control.
Issues:
a) The approval activity is executed only once and by one
organizational role.
b) The single approval is executed multiple times in processes
being executed in flat and descentralized organizations (or
specific organizational units).
c) Final decision can be made manually (i.e., by a user) or
automatically according to some rules.
Solution: The bellow process fragment illustrates a single
approval using the BPMN notation; here an organizational role
reviewer performs a document review either resulting in
approval or disapproval.
Figure 2: Approval activity pattern.
patterns based on an extensive literature study:
A
PPROVAL, QUESTION-ANSWER, UNI-
DIRECTIONAL PERFORMATIVE, BI-DIRECTIONAL
PERFORMATIVE, INFORMATIVE, NOTIFICATION
and DECISION. Table 1 shows an overview of the 7
WAP7: DECISION
Description: In a process the execution of one or multiple
activities is requested. Depending on the results the process
continues execution with one or multiple branches.
Example: To get feedback from a user concerning a particular
service, he shall indicate his satisfaction degree by giving
grades from 0 to 10. Depending on the specified grade process
execution continues with one or multiple branches depending
on their conditions (e.g., grade between 0 and 4).
Problem: In a process an explicit decision step has to be
included. The final decision is made based on the execution
result(s) of requested activities.
Issues:
a. The decision pattern is usually combined with a performative
bi-directional pattern .
b. Based on the response one or several subsequent branches are
selected for execution..
c. The final decision is usually made automatically based on the
execution result(s) of previous activities.
Solution: The bellow process fragment illustrates a single
decision pattern using the BPMN notation.
Figure 3: Decision activity pattern.
activity patterns we identified. Each of them is
specified taking the following attributes:
Description, Example, Problem and Issues. (We
have considered additional attributes as well like
Design Choices (pattern variants), Related Patterns
and Pattern Implementation. However, these
attributes are outside the scope of this paper. Bellow
we give two examples of pattern descriptions: the
A
PPROVAL activity pattern for single approval (i.e.,
the approval pattern is executed only once) and the
D
ECISION activity pattern.
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3 ANALYZING THE
FREQUENCY OF ACTIVITY
PATTERNS IN REAL PROCESS
MODELS
With the goal to check whether the identified
activity patterns are present in real applications as
well, we analyzed 214 process models. These
process models have been modeled either with the
Oracle Builder tool or anUML modeler. Altogether,
the analyzed process models stem from 13 different
organizations and are related to different
applications like Total Quality Management (TQM),
software access control, document management,
help desk services, user feedback, document
approval and electronic change management.
Amongst others, we have obtained the following
results from our empirical studies (i.e. from the
analysis of the 214 process models):
1. evidence with high probability that the activity
patterns exist in real process models;
2. evidence that the set of activity patterns is
both necessary and sufficient to model all 214
process models analyzed; and
3. identification of common ocurrences of
activity patterns based on a classification of
the respective processes into Human-Intensive
and System-Intensive (Le Clair, 2007). For
example, if the Decision activity pattern
occurs in an intensive-system process it is
most of the times followed by a Notification
activity pattern.
For each activity pattern we calculate its support
value. In the given context support corresponds to
the number of occurrences of each activity pattern
when looking at the total set of 214 process models.
For those models comprising more than one
occurrence of the same pattern we consider just one
of these occurences.
First, we identify and annotate activity patterns
within all analyzed process models. Second, for all
process models we count the number of occurrences
of each pattern. To get relative values, the obtained
result is divided by the total number of analyzed
process models (i.e. 214 in our study).
3.1 Frequency of Activity Patterns in
Process Models
The following five activity patterns are not
dependent on specific application domains or
organizational structure aspects (e.g., the degree of
centralization in decision making, standardization of
work abilities):
UNIDIRECTIONAL and BI-
DIRECTIONAL PERFORMATIVE, DECISION,
NOTIFICATION
and INFORMATIVE . This fact mainly
explains why these five patterns have been identified
with high frequency in almost all analyzed process
models. The same applies to the
APPROVAL pattern.
This can be explained by the high degree of
centralization on decision-making existing in the
organizational units for which we analyzed their
process models. This high centralization implies the
use of approval activities. By contrast, most of the
process models analyzed do not comprise
QUESTION-
ANSWERING activities. Figure 4 graphically
illustrates the frequency of each activity pattern with
respect to the set of process models analyzed.
57%
2%
74%
64%
53%
14%
60%
0%
10%
20%
30%
40%
50%
60%
70%
80%
AWP1 AWP2 AW P3 AWP4 AWP5 AWP6 AWP7
AWP1 (
Appr oval
), AW P2 (
Question-answering
), AW P3 (
Performative Unidirectional
),
AWP4 (P
erformative Bi-directional
), AW P5 (
Notification
), AW P6 (
Informative
), AW P7
Figure 4: Frequency of activity patterns in real process.
3.2 Identifying Common Ocurrences of
Activity Patterns in Process Models
One of the use cases for the knowledge base of our
suite (cf. Section 4) is based on a mechanism that
gives design time recommendations with respect to
the most suited activity patterns to be combined with
an already used pattern. This mechanism utilizes
statistical data we gathered during our empirical
study, which we also summarize in this section. To
obtain the frequencies for pattern co-occurences, we
analyze the sequences of the occuring activity
patterns in 154 of the 214 process models studied.
In earlier work, we have shown that if we
classify the process models into human–oriented
(i.e., with human intervention during execution) and
fully automated (i.e., with no human intervention
during execution) we can identify certain activity
patterns more often in one of the two categories. We
tried to classify the processes based on common
characteristics (e.g., application domain), also
considering classifications from the literature in this
context. However, most of the studied classifications
(Dowson, 1987), (Harrington, 1991) and (Leymann,
1999) are based on specific application domains of
the related process models. Accordingly, those
APPLYING ACTIVITY PATTERNS FOR DEVELOPING AN INTELLIGENT PROCESS MODELING TOOL
115

approaches are not applicable to our analysis
because the set of the process models we have been
investigating does not cover all the categories
covered by these approaches.
We decided then to use the approach of Le Chair
who classifies business processes into system-
intensive and human-intensive (Le Clair, 2007). The
system-intensive processes are characterized by
being handled on straight-through basis, this means
that there is minimal or no human intervation and
few exceptions migh occur. The human-intensive
processes require people to get work done by relying
on business applications, databases, documents as
well as other people and interacting extensively with
them. This type of process requires human intuition
or judgment for decision-making during individual
steps.
By classifying our set of process models in those
two categories, we obtain 123 human-intensive
process models and 31 system-intensive process
models respectively. Remember that in this analysis
we consider 154 of the 214 process models. The
next step was to evidence the occurrence of the
activity patterns in the two categories of process
models. Figure 5 shows the frequency of the
workflow activity patterns in the system-intensive
process models and the human-intensive. Note that
some patterns (i.e. approval, informative, question-
answer) do not appear in the system-intensive
process models at all. These patterns are frequently
related to human activities, i.e. are executed by an
organiyational role.
75%
2%
73%
63%
71%
27%
73%
0% 0%
68% 68%
65%
0%
87%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
WAP1 WA P2 WAP3 WA P4 WAP5 WA P6 WAP7
Human-Intensive System-Intensive
Figure 5: Frequency of Activity Patterns in Human-
intensive and System-intensive Process Models.
In other analysis we search for frequent and
recurrent occurences of activity patterns in the
process models. Relying on these common
ocurrences of activity patterns the knowledge base
must show to users a ranking of the most frequent
subsequent activity patterns which follow the
activity pattern the user has recently modeled in the
process.
Figure 6 shows how often the
DECISION
PATTERN
is used immediately after the other
workflow activity patterns in the set of process
models we analyzed. Note that for each kind of
process (i.e. human intensive and system intensive),
specific pairs of process have more probability to
occur. Per example the pair
DECISION Æ
NOTIFICATION
(AWP5) is more often in system-
intensive process models. On the other hand, the pair
DECISION Æ APPROVAL is more often in the human-
intensive process models
Decision Pattern Subsequent Patterns
31%
0%
16%
12%
21%
5%
15%
0% 0%
5%
15%
50%
0%
30%
0%
10%
20%
30%
40%
50%
60%
WAP1 WAP2 WAP3 WAP4 WAP5 WAP6 WAP7
Human-Intensive System-Intensive
Figure 6: Subsequent Activity Patterns of the Decision
Pattern (regarding both system- and human-intensive
processes).
In Figure 7, we present the result of the analysis
of the
PERFORMATIVE UNIDIRECTIONAL subsequent
patterns. In system-intensive process models, the
most frequent pair of activity patterns is
UNIDIRECTIONAL Æ UNIDIRECTIONAL. A
considerable amount of the studied system-intensive
process models presented a sequence of 2 or more
UNIDIRECTIONAL PERFORMATIVE patterns as a way
of modularizing distinct software functions. In
human-intensive process models, the frequency of
the pairs was very similar, except for the less
frequent activity patterns.
Performative Unidirectional Subsequent
Patterns
14%
0%
25%
19%
17%
4%
21%
0% 0%
60%
36%
0% 0%
4%
0%
10%
20%
30%
40%
50%
60%
70%
WAP1 WAP2 WAP3 WA P4 WA P5 WAP6 WAP7
Human-Intensive System-Intensive
Figure 7: Frequency of performative unidirectional
subsequent patterns on system-intensive and human-
intensive processes.
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116

As example consider a system-intensive process
to delete an existent document (cf. Figure 8). This
process includes the following activities: (1) the data
used by an external program (Bussines Event
System) to delete a document are set; (2) the
properties of an event to delete the document are
defined; (3) an event to delete the document is
executed; (4) an event informs whether the
document is deleted or an error ocurred; (5) in case
of an error the main author of the document is
notified. Activity 4 is then repeated. Note that the
process include a sequence of 4
UNIDIRECTIONAL
PERFORMATIVE
patterns (activities 1, 2, 3 and 4
respectively).
Figure 8: Example of system-intensive process.
4 TOWARDS AN INTELLIGENT
BPM TOOL
We now present an intelligent workflow designer
tool (intelligent suite for short) for normalizing and
modeling business processes based on the reuse of
activity patterns. This suite can be added as an
extension to existing process modeling components
(e.g., Intalio (2006), Aris Toolset (IDS, 2007), or
ADEPT Process Composer (Reichert, 2006)).
Core functionalities of the intelligent suite are:
1. Extraction of business processes from legacy
systems and their normalization: Comprises
the extraction of business rules from the
analysis of source code (e.g. COBOL, clipper,
access, visual basic, C++) of legacy systems
and subsequent generation of business
processes in high-level notation (such as the
BPMN). The process is then, validated by
matching it with existent activity patterns
stored in a knowledge database. The challenge
here is to identify all embody activity patterns
comprised by the process. As a result the
process is translated into one or more activity
patterns. Such procedure must benefit the
translation of the processes to some execution
language (e.g., BPEL4WS). Furthermore, with
the scope of business process extraction, a
model checking is performed, in order to test
the correctness (accuracy) of the process
model.
2. Support to process design
: a user process is
received by the intelligent workflow designer
as an input. The process is then, matched with
activity patterns stored in the knowledge
database in order to identify the partial order
of activity patterns it comprises. Having this
information, the intelligent suite will
recommend the most suitable activity patterns
to be used together with the activity pattern
designed before. In addition, it will inform
how frequent each pair of activity patterns was
used in earlier modeling. This module will be
developed based on the analysis result we
presented in Section 3.2.
3. Construction of a knowledge database of
activity patterns: The activity patterns
repository will store not only the activity
patterns but also the frequency with each
activity pattern is combined with an already
used pattern. Through the analysis of new
process models (e.g., from automotive as well
as health care domain) we aim at increasing
the support value of the sequences of activity
patterns (cf. Section 3.2). Thus, in design time
the accuracy, concerning the frequency
associated with each recommendation of pair
of pattern be correct may increase. Figure 9
illustrates the intelligent suite.
Figure 9: Intelligent Suite.
Core components of the intelligent suite are:
• Legacy Program Flow Extractor (LPFE):
component responsible by the extraction of
business process rules from the source code of
legacy systems. In addition, generation of
corresponding process in high-level language
(such as BPMN).
• Business Process Model Checking (BPMC):
this component verifies how complete and
correct the extracted process is. First the
process is translated to some formal language
(e.g., Pi-calculus). In case it is correct, the
process is matched with the knowledge base
so that the activity patterns comprised by the
process can be identified.
APPLYING ACTIVITY PATTERNS FOR DEVELOPING AN INTELLIGENT PROCESS MODELING TOOL
117

• Knowledge Base: is the database where the
activity patterns are stored. It is composed by
an ontology which describes the activity
patterns. Furthermore, it comprises a query
and update language (mechanism). The
mechanism gives design time
recommendations with respect to the most
suited activity patterns to be combined with an
already used pattern. In addition, the update
mechanism must be used to change the
probabilistic results of each pair of activity
pattern based on the process analysis results.
• Matching Algorithms: algorithms responsible
by the identification (matching) of the activity
patterns maintained in the ontology. The
selected activity patterns are those comprised
by either the user process or the processes
generated by the LPFE and BPMC
components.
• Business Process Mining: External tool to the
Intelligent Workflow Designer which receives
a set of normalized activity patterns as input.
The output of this tool will permit the
knowledge base to be updated, once it will
have new information about the frequency of
each pair of activity pattern.
5 CONCLUSIONS AND FUTURE
WORK
While workflow patterns were defined for several
aspects related to process execution, the aspect of
recurrent business functions is only partially
addressed by existing work. In prior work, we
identified a set- of seven workflow activity patterns
that appear necessary and sufficient to model a large
variety of process models. In other work we
investigated in how far process modeling tools can
be tailored to provide a direct support for pattern
reuse. Further we are working in the documentation
of the activity patterns with Pi-calculus. In this paper
we reported the results of an empirical work where
we search how often activity patterns as well as
common ocurrences of them (i.e. pairs of activity
patterns) are present in a large set of real process
models. In addition we proposed an approach for
modeling business processes and workflows. It is
based on a suite which, when being implemented in
a process modeling tool, allows to design business
processes based on well-defined (process) activity
patterns. Our suite further provides support for
analysing and verifying certain properties of the
composed process models (e.g., absence of
deadlocks and livelocks). Our approach considers
both business processes designed from scratch and
processes extracted from legacy systems.
The main advantages of this approach can be
summarized as follows: (a) the completeness and
necessity of the activity patterns for process design
has already been evidenced in prior work; (b) the
intelligent suite is tool-independent and can be
adapted for any workflow modeling tool; (c) the
business process model checking can be considered
as a very important component which can help in the
verification of how complete and correct is the
process being designed. This can be accomplished
through matching the current process model with
activity patterns stored in the knowledge base.
As future work we intend to perform additional
analyzes considering process models from different
application domains (e.g., health insurance and
automotive). Our goal is to identify more common
ocurrences of pairs of activity patterns. In this
context we also intend to continue studying the
workflow classifications so that we can find more
specific classification and with smaller granularity to
divide the set of processes. A less generic
classification will be useful when we try to converge
on the user needs using just a few steps. We also
consider making an experiment for comparing
process modeling with and without activity pattern
support. Finally, we intend to implement the
intelligent suite in an existent workflow designer
tool.
ACKNOWLEDGEMENTS
The authors would like to acknowledge the
Coordination for the Improvement of Graduated
students (CAPES), the Institute of Databases and
Information Systems of the University of Ulm (Ulm,
Germany) and the Informatics Institute of Federal
University of Rio Grande do Sul (Porto Alegre,
Brazil).
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