=Paper=
{{Paper
|id=Vol-247/paper-14
|storemode=property
|title=Rule-based Autocompletion of Business Process Models
|pdfUrl=https://ceur-ws.org/Vol-247/FORUM_13.pdf
|volume=Vol-247
|dblpUrl=https://dblp.org/rec/conf/caise/HornungKO07
}}
==Rule-based Autocompletion of Business Process Models==
https://ceur-ws.org/Vol-247/FORUM_13.pdf
49
Rule-based Autocompletion of Business Process
Models
Thomas Hornung1 , Agnes Koschmider2 , and Andreas Oberweis2
1
Institute of Computer Science, Albert-Ludwigs University Freiburg, Germany
hornungt@informatik.uni-freiburg.de
2
Institute of Applied Informatics and Formal Description Methods
Universität Karlsruhe (TH), Germany
koschmider|oberweis@aifb.uni-karlsruhe.de
Abstract Several methods based upon textual programming languages
or graphical notations have been proposed for manual modeling of busi-
ness process models. But since manual process modeling is time-consu-
ming and increases the amount of structural modeling errors, we aim at
supporting the user during the process modeling phase. In this paper we
present an initial idea for an automatic approach for completion of busi-
ness process models that recommends appropriate completions to initial
process fragments based on business rules and structural constraints.
1 Introduction
Manual process modeling is a time-consuming task and thus increases the total
amount of modeling time. Typos and structural modeling errors make it partic-
ularly error-prone to model business processes manually. Therefore it would be
useful to assist the user in modeling business processes by providing an autocom-
pletion mechanism during process modeling. Usually, business process models
are modeled according to specific business rules such as all high loss estimations
must include an expert’s inspection or if more than two persons travel together,
the third pays only half price. Therefore to support automatic completion of
such business process models, a data format with support for rule inferencing is
required. By using Petri nets as the theoretical framework for this task, syntac-
tic composition problems of initial process fragments and recommended process
fragments can be solved.
To be able to resolve conflicts on a semantical level, we introduce a description
of rule-enhanced Petri nets with the ontology language OWL [1] and the rule
language SWRL [2] in Section 2. Finally Section 3 sums up our approach.
2 Autocompletion Process
This section gives a high-level introduction in our proposal for an autocompletion
mechanism for business process models as depicted in Figure 1. The idea is that
the user first selects a position in the process model and the recommendation
system shows a list of meaningful process fragments that she can enter at this
position.
50
Figure 1. Rule-based autocompletion process (rectangles depict transitions and circles
depict places)
2.1 Semantic business process models
A missing semantic representation of Petri net components hampers to utilize
reasoning techniques that make it possible to (semi-)automatically reason about
Petri net data. Therefore we describe traditional Petri nets with the Ontology
Language OWL [3]. These so-called semantic business process models (SBPM)
combine process modeling methods with semantic technologies to support auto-
matic processing of process components and they make it possible to implement
an efficient algorithm for (semi-)automatic similarity computation between pro-
cess model variants [4]. Each Petri net element has a corresponding element in
the SBPM as described in [1] with two extensions: first we introduce a prop-
erty isSelected with the domain Place or Transition and the range boolean
(true or false) that ensures that appropriate recommendations are made only
for the selected process element. The property initialElement with the do-
main PetriNet and the range Node (Place or Transition) indicates the start
element of a process as depicted in Figure 2.
2.2 Classification of rule types
Since ontologies are not good at modeling the dynamic state of Petri nets we
additionally use the Semantic Web Rule Language (SWRL) to capture the dy-
namic nature of Petri nets and to model business rules that a process model
needs to satisfy. Rule-based modeling in general ranges from Event–Condition–
Action rules for databases [5] to logic-based languages like Prolog [6], and rule
engine approaches like Jess [7]. We distinguish the following three types of rules
in our autocompletion scenario:
1. Constraint rules: They concern the integrity of semantic business process
models. These constraints are automatically pre-specified for every business
process model by our autocompletion system, since they express restrictions
on the OWL-based Petri net description, i.e. successors of places are transi-
tions and vice versa.
51
Figure 2. Extension of the Petri net Ontology introduced in [1]
2. Event-Condition-Action rules: They concern the integrity of semantic busi-
ness process models and have a syntax described by IF, THEN, AND, OR,
e.g. “If customer order is checked THEN manufacture item AND send arti-
cle”.
3. Dynamic rules: These rules are applied during process modeling by the mod-
eler and may vary from one to another, e.g. ”in a specific context an amount
of 1000 Euro is considered a high loss”.
2.3 Formalization of rules
The next step of the autocompletion process is to formalize the rules which were
classified in the previous section. To do this we first introduce the notion of order,
i.e. which action is performed before or after the other. This is formalized with
the SWRL predicates bef ore(action1 , action2 ) and af ter(action1 , action2 ),
which evaluate to true, when action1 appears before action2 in the business
process model or after respectively. These predicates can be specified on the
semantic information provided by the OWL serialization of the process model
ontology and allow for trivial checking of the constraint rules via an additional
SWRL predicate arcP ossible(action, net), that is satisfied if the selected action
is a transition and the first element in the process fragment under consideration
is a place or vice versa.
To infer Event-Condition-Action style rules we compare the OWL version of the
currently modeled process with given serializations of business process fragments
in a repository. This is best illustrated with an example: given the business
rule “IF request is checked THEN forward order” we would check the following
conditions with the help of the beforementioned predicates:
52
1. The condition(s) of the IF part have to appear bef ore the selected element,
and
2. The action(s) of the THEN part need to be either the first action of the
process fragment or appear af ter the first element in the process fragment
under consideration, and
3. The hypothetical connection of the currently modeled process fragment and
the process fragment under consideration need to satisfy the constraint rules.
Finally dynamic rules can be realized by changing the action part of a busi-
ness rule to a question for the modeler.
3 Conclusion
Often business processes are modeled according to specific business rules. In this
paper we have described our idea of realizing an autocompletion of business pro-
cess models. The main elements are that we use semantic business process models
based on an OWL representation of Petri nets that allows us to efficiently com-
pute the semantic similarity between process model variants. Additionally we use
the Semantic Web Rule Language, which is based upon a combination of OWL
DL with Unary/Binary Datalog RuleML [8], to model additional constraints im-
posed by business rules. Although the implementation of the recommendation
system is not finished yet a study which has been conducted seems to confirm
our approach.
References
1. Koschmider, A., Oberweis, A.: Modeling semantic business process models. In
Rittgen, P., ed.: Handbook of Ontologies for Business Interaction. Idea group pub-
lishing edn. (2007) (to appear).
2. Horrocks, I., Patel-Schneider, P., Boley, H., Tabet, S., Grosof, B., Dean,
M.: SWRL: A Semantic Web Rule Language Combining OWL and RuleML.
http://www.w3.org/Submission/2004/SUBM-SWRL-20040521/ (2004)
3. McGuinness, D.L., van Harmelen, F.: OWL Web Ontology Language
Overview. Technical report, World Wide Web Consortium (W3C) (2003) Internet:
http://www.w3.org/TR/owl-features/.
4. Ehrig, M., Koschmider, A., Oberweis, A.: Measuring Similarity between Seman-
tic Business Process Models. In: Proc. of the Fourth Asia-Pacific Conference on
Conceptual Modelling, Australia (2007) (to appear).
5. Gatziu, S., Dittrich, K.R.: Events in an Active Object-oriented Database System.
In: Rules in Database Systems. (1993) 23–39
6. Clocksin, W., Mellish, C.: Programming in Prolog. 3 edn. Springer (1987)
7. Friedman-Hill, E.: Jess: the Java Expert System Shell, sandia national laboratories.
http://herzberg.ca.sandia.gov/jess/ (2001)
8. Boley, H., Tabet, S., Wagner, G.: Design Rationale for RuleML: A Markup Language
for Semantic Web Rules. In: SWWS. (2001) 381–401