Rule-based Autocompletion of Business Process Models

Abstract

Several methods based upon textual programming languages or graphical notations have been proposed for manual modeling of business process models. But since manual process modeling is time-consuming 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 business process models that recommends appropriate completions to initial process fragments based on business rules and structural constraints.

Introduction

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 automatic processing of process components and they make it possible to implement an efficient algorithm for (semi-)automatic similarity computation between process 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 property 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 domain 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 dynamic 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 transitions and vice versa. 2. Event-Condition-Action rules: They concern the integrity of semantic business process models and have a syntax described by IF, THEN, AND, OR, e.g. “If customer order is checked THEN manufacture item AND send article”. 3. Dynamic rules: These rules are applied during process modeling by the modeler 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:

Finally dynamic rules can be realized by changing the action part of a business 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 process 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 compute 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 imposed 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 publishing 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 Semantic 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