Vienna University of Economics and Business Administration

Department of Information Systems and Operations

Institute for Information Systems and New Media

Augasse 2-6, A-1090 Vienna

Research Proposal:

Detection and Prediction of Errors in

EPC Business Process Models

Dipl.-Wirt.-Inf. Dipl.-Kfm. Jan Mendling

0353423

jan.mendling@wu-wien.ac.at

Supervisor: Univ.-Prof. Dr. Gustaf Neumann

CONTENTS i

Contents

1 Introduction to this Research Proposal 1

2 Background on Business Process Management 1

3 Definition of Business Process Management 3

4 Definition of Business Process Modeling 5

5 Business Process Modeling and Errors 9

6 Research Design and Research Objectives 11

Bibliography 14

1 INTRODUCTION TO THIS RESEARCH PROPOSAL 1

1 Introduction to this Research Proposal

This research proposal provides an overview of a planned doctoral thesis on detectionand prediction of errors in EPC business process models. Section 2 elaborates onthe background of business process management with a historical classification ofseminal work. Section 3 defines business process management and illustrates it bythe help of the business process management lifecycle. Business process modelsplay an important role in this lifecycle. Section 4 discusses modeling from a generalinformation systems point of view and deducts a definition for business processmodeling. The following section details business process modeling and distinguishesformal verification and external validation of business process models. Furthermore,it highlights the need to better understand why formal errors are introduced inbusiness process models. Against this background, Section 6 specifies the researchdesign and research objectives of this thesis and relates them to epistemologicalpositions of information systems research and German Wirtschaftsinformatik.

2 Background on Business Process Management

In the last couple of years there has been a growing interest in business process man-agement both in business administration as well as in information systems researchand practice. In essence, business process management deals with the efficient co-ordination of business activities within and between companies. As such, it can berelated to several seminal works on economics and business administration. HenriFayol as one of the founders of modern organization theory recommended a sub-division of labor in order to increase productivity [Fay66, p.20]. Already AdamSmith illustrated its benefits by analyzing pin production [Smi76]. As a drawback,subdivision of labor implies a need for coordination between the subtasks. Businessprocess management is concerned with such coordination mechanisms in order toleverage the efficient creation of goods and services in a production system basedon subdivision of labor. In this spirit, Frederick Taylor’s advocated the creationof an optimal work environment based on scientific methods to leverage the mostefficient way of performing individual work steps. In the optimization of each step,he proposed to “select the quickest way”, to “eliminate all false movements, slowmovements, and useless movements”, and to “collect into one series the quickestand best movements” [Tay11, p.61]. The efficient organization of business processesbecame more obvious in the idea of the assembly line system than in Taylor’s “taskmanagement”. Its inventor Henry Ford proudly praised the production cycle of only81 hours in his company “from the mine to the finished machine” to illustrate theefficiency of this model [For26, p.105].

In academia, Nordsieck was one of the first to distinguish structural and process or-ganization [Nor32, Nor34]. He described several types of workflow diagrams (Ablauf-schaubilder), e.g. for subdivision and distribution of labor, sequences of activities,or task assignment [Nor32]. In this context, Nordsieck identifies the order of work

2 BACKGROUND ON BUSINESS PROCESS MANAGEMENT 2

steps and the temporal relationship of tasks as the subject of process organizationwhose overall concern is the integration of these steps [Nor34]. He distinguishes fivelevels of automation: free course of work, concerning the contents bound course ofwork, concerning the order bound course of work, temporally bound course of work,and concerning the beat bound course of work [Nor34].

In the decades after World War II, organization research, at least in German speak-ing countries, devoted much more attention to structural organization than to pro-cess organization (cf. e.g. [Ulr49, Kos62, Gro66]). In the early 1970’s it becameapparent that information systems had the potential to become a new design di-mension in an organizational setting (cf. e.g. [Han70, Gro75, GS75]). But the fo-cus, even in this context, remained on the structure. The early books of Scheer[Sch76, Sch78, Sch84] nicely illustrate the focus on database technology in order tosupport business functions without giving much attention to process organization.At that time, the logic of business processes used to be hard-coded in applicationssuch as production floor automation systems and were, therefore, difficult to change[HK96, zM04]. Office automation technology at the end of the 1970’s was the start-ing point for a more explicit control over the flow of information and the coordinationof tasks. The works of Ellis & Nutts and Zisman provided business process supportin office automation systems based on Petri nets [Zis77, Zis78, Ell79, EN80].

Although the business importance of processes received some attention in the 1980’s(e.g. [Por85]) and new innovations have been made in information system support ofprocesses (e.g. system support for communication processes [Win88] based on speechact theory introduced by [Aus62, Sea69]), it was only in the early 1990’s that work-flow management prevailed as a new technology to support business processes. Anincreasing number of commercial vendors of workflow management systems bene-fited from new business administration concepts and ideas such as process innovation[Dav93] and business process reengineering [HC93]. On the other hand, these busi-ness programs heavily relied on information system technology, in particular work-flow systems, in order to establish new and more efficient ways of doing business.In the 1990’s, the application of workflow systems, in particular those supportinginformation systems integration processes, profited from open communication stan-dards and distributed systems technology that both simplify interoperability withother systems (cf. [GHS95]). The Workflow Management Coalition founded in 1993is of special importance for this improvement [Hol04]. The historical overview ofoffice automation and workflow systems given in [zM04, p.93] nicely illustrates thisbreakthrough. Up to the late 1990’s intra-enterprise processes remain the majorfocus of business process management [DHL01].

Since the advent of the eXtended Markup Language (XML) and web services tech-nology, application scenarios for business process integration have become mucheasier to implement in an inter-enterprise setting. Current standardization effortsmainly address interoperability issues related to such scenarios (cf. e.g. [MNN04,MNN05a, MzMP05]). The common interest of the industry to facilitate the integra-tion of interorganizational processes leverages the specification of standards for webservice composition like the Business Process Execution Language for Web Services

3 DEFINITION OF BUSINESS PROCESS MANAGEMENT 3

(BPEL) [CGK+02, ACD+03, AAB+05], for web service choreography like the WebService Choreography Description Language (WS-CDL) [KBR+05], or for interor-ganizational processes based on ebXML and related standards (cf. [HHK06] for anoverview). Currently, the integration of composition and choreography languages isone of the research topics in this area [MH05, WHM06].

Today, business process management is an important research area that combinesinsights from business administration, organization theory, computer science, andcomputer supported cooperative work. Furthermore, it is a considerable market forsoftware vendors, IT service provider, and business consultants.

3 Definition of Business Process Management

Since the beginning of organization theory, several definitions for business processeshave been proposed. Nordsieck in the early 1930’s describes a business process asa sequence of activities producing an output. In this context, an activity is thesmallest separable unit of work performed by a work subject [Nor34, pp.27-29]. Inthis tradition, Becker et al. [BK03] propose the following definition:

“A process is a completely closed, timely and logical sequence of activitieswhich are required to work on a process-oriented business object. Such aprocess-oriented object can be, for example, an invoice, a purchase orderor a specimen. A business process is a special process that is directed bythe business objectives of a company and by the business environment.Essential features of a business process are interfaces to the businesspartners of the company (e.g. customers, suppliers).”

As Davenport puts it [Dav93, p.5], a “process is thus a specific ordering of workactivities across time and place, with a beginning, an end, and clearly identifiedinputs and outputs: a structure for action.” Van der Aalst and Van Hee add thatthe order of the activities is determined by a set of conditions [AH02, p.4]. In thiscontext, it is important to distinguish the business process and several individualcases. Consider a business process such as car production. This process producescars as output. The production of one individual car that is sold to customer JohnSmith is a case. Accordingly, each case can be distinguished from other cases and abusiness process can be regarded as a class of similar cases [AH02].

Business process management can be defined as the set of all management activ-ities related to business processes. In essence, the management activities relatedto business processes can be idealistically arranged in a lifecycle. Business processmanagement lifecycles have been described e.g. in [AH02, zM04, DHA05]. In theremainder of this section, I mainly follow the lifecycle proposed in [zM04, pp.82-87], first, because it does not only include activities but also artifacts, and second,because it consolidates the lifecycle models for business process management re-ported in [Hei96, GS95, SD95, NPW03]. It shares the activities analysis, design,

3 DEFINITION OF BUSINESS PROCESS MANAGEMENT 4

and implementation with the general process of information systems developmentidentified by [WW90]. It comprises the management activities of analysis, design,implementation, enactment, monitoring, and evaluation.

• The business process management lifecycle is entered by an analysis activity(see Figure 1). This analysis covers both the environment of the process andthe organization structure. The output of this step is a set of requirements forthe business process such as a certain performance.

• These requirements drive the subsequent design activity. In particular, thedesign includes the identification of process activities, the definition of theirorder, the assignment of resources to activities, and definition of the organiza-tion structure. These different aspects of process design are typically formal-ized as a business process model. This model can be tested in a simulation ifit meets the design requirements.1

• The process model is then taken as input for the implementation. In this phasethe infrastructure for the business process is set up. This includes amongothers training of staff and provision of dedicated work infrastructure. If theprocess execution is to be supported by dedicated information systems, theprocess model is used as a blueprint for the implementation.

• As soon as the implementation is completed, the actual enactment of theprocess can begin. In this phase the specific infrastructure is used to handleindividual cases covered by the business process. The enactment producesinformation such as consumption of time, resources, materials, etc. for eachhandled cases. This data is input for two subsequent activities: monitoringand evaluation.

• Monitoring is a continuous activity that is performed with respect to eachindividual case. Depending on process metrics as e.g. maximum waiting timefor a certain process activity, monitoring triggers respective counteractions ifsuch a metric indicates a problematic situation.

• Evaluation on the other hand considers case data on an aggregated level. Theperformance results are compared with the original requirements and sourcesof further improvement are discussed. In this way, evaluation leads to newrequirements that are taken as input in the next turn of the business processlifecycle.

The business process management lifecycle reveals that business process models playan important role in the design, implementation, and enactment phase, especiallywhen information systems support the process enactment. As such they are valu-able resources for continuous process improvement, quality management, knowledgemanagement, ERP system selection, and software implementation [Ros03].

1Note that zur Muehlen considers simulation as a separate activity related to evaluation [zM04,p.86], but this neglects the fact that simulation is always done to evaluate different design alter-natives.

4 DEFINITION OF BUSINESS PROCESS MODELING 5

analysis

design

implementation

enactment

evaluation

monitoring

requirements

process model

infrastructure

case data

case data

requirements

Figure 1: Business process management lifecycle

4 Definition of Business Process Modeling

Before defining business process modeling, the term modeling has to be discussedin a more general setting. Already Nordsieck emphasized that “the utilization ofsymbols enables the model not only to replace or to complement natural language forthe representation of complex matters, but to reveal the notion of the subject matteroften in a more comprehensive way as with any other form of representation” [Nor32,p.3]. Its most protuberant features are brevity, clarity, precision, and its graphicquality [Nor32, p.3]. Stachowiak defines a model as the result of a simplified mappingfrom reality that serves a specific purpose [Sta73]. According to this perception,there are three important qualities of a model. First, there is a mapping thatestablishes a representation of natural or artificial originals that can be models itself.Second, only those attributes of the original that are considered relevant are mappedto the model; the rest is skipped. Therefore, the model provides an abstraction interms of a homomorphism in a mathematical sense. Third, the model is used bythe modeler in place of the original for a certain time and a certain purpose. Thismeans that a model always involves pragmatics.

A weakness of Stachowiak’s concept of a model is that it implies an epistemologicalposition of positivism2. This is for instance criticized in [SR98] where the authorspropose an alternative position based on insights from critical realism and construc-tivism3. This position regards a model as a “result of a construct done by a modeler”[SR98, p.243]. As such it is heavily influenced by the subjective perception of themodeler. This fact makes modeling a non-deterministic task that requires standardsin order to achieve a certain level of inter-subjectivity. The Guidelines of Model-ing (GoM) [BRS95, SR98] define principles that serve this standardization purpose.

2Positivism is the philosophical theory that establishes sensual experience as the single objectof human knowledge

3In contrast to positivism, constructivism regards all knowledge as constructed. Therefore,there is nothing like objective knowledge or reality.

4 DEFINITION OF BUSINESS PROCESS MODELING 6

They are applicable for either epistemological positions, positivism and construc-tivism, because both the choice for a certain homomorphism (positivist position)and the perception of the modeler (constructivist position) introduce subjective el-ements.

In this way, the Guidelines of Modeling (GoM) [BRS95, SR98, BRvU00] includesix particular principles for achieving inter-subjectivity of models. The first threedefine necessary preconditions for the quality of models, i.e. correctness, relevance,and economic efficiency, and the other three are optional, i.e., clarity, comparability,and systematic design.

• Correctness: First, a model has to be syntactically correct. This fact impliesusing allowed modeling primitives and combining them according to predefinedrules. Second, a model has to be semantically correct. Therefore, it has to beconsistent with the (perception of the) real world.

• Relevance: This criterion demands that only interesting parts of the universeof discourse are reflected in the model. In this way, it is related to the notionof completeness as proposed in [BLN86].

• Economic Efficiency: This guideline introduces a trade-off between benefitsand costs of putting the other criteria into practice. For example, semanticcorrectness might be neglected to a certain extent if achieving it is to expensive.

• Clarity: This is a highly subjective guideline demanding that the model mustbe understood by the model user. It is related e.g. to layout conventions.

• Comparability demands consistent utilization of a set of guidelines in a mod-eling project. Among others, it refers to naming conventions.

• Systematic Design: This guideline demands a clear separation between modelsin different views (e.g. statical aspects and behavioral aspects) and how theycan be integrated.

Accordingly, the explicit definition of a modeling technique is an useful means toaddress several of these guidelines. A modeling technique consists of two interre-lated parts: a modeling language and a modeling method4. The modeling languageconsists of three parts: syntax, semantics, and optionally at least one notation. Thesyntax provides a set of constructs and a set of rules how these constructs can becombined. A synonym is modeling grammar [WW90, WW95, WW02]. Seman-tics bind the constructs defined in the syntax to a meaning. This can be donein a mathematical way e.g. using formal ontologies or operational semantics. Thenotation defines a set of graphical symbols that are utilized for the visualizationof models [KK02]. The modeling method defines procedures by which a modeling

4Several authors use heterogeneous terminology to refer to modeling techniques. Our conceptof a modeling language is e.g. similar to a grammar in [WW90, WW95, WW02] who also use theterm method with the same meaning. In [KK02] a modeling method is called procedure while theterm method is used to define a composition of modeling technique plus related algorithms.

4 DEFINITION OF BUSINESS PROCESS MODELING 7

language can be used [WW02]. The result of applying the modeling method isa model that complies with a specific modeling language5. Consider for exampleentity-relationship diagrams (ERDs) as a defined in [Che76]. ERDs are a modelingtechnique since they define a modeling language and a respective modeling method.Entities and Relationships are syntax elements of the language that capture certainsemantics of a universe of discourse. The notation represents entities as rectanglesand relationships as arcs connecting such rectangles carrying a diamond in the mid-dle. Respective procedures like looking for norms in documents define the modelingmethod.

The are several different approaches to provide a foundation for the correctnessand relevance of what is to be put into a model. The following paragraph sketchesontologies, speech act theory, and meta-modelling as three alternative foundations.

• Ontology is the study of what is in the world. It is a prominent sub-disciplineof philosophy. Wand and Weber have been among the first to adopt ontologiesfor a foundation of information systems modeling (see e.g. [WW90, WW95]).Basically, they make two assumptions. First, as information systems reflectwhat is in the real-world they should also be modelled with a language that iscapable of representing real-world entities. Second, the ontology proposed byMario Bunge [Bun77] is a useful basis for describing the real-world. The so-called Bunge-Wand-Weber (BWW) Ontology proposed by Wand and Weberincludes a set of things that can be observed in the world. They should beidentified in the process of modeling of a specific domain and fulfill certainconsistency criteria [WW95]. For examples of other ontologies refer to [WW02,GHW02].

• Speech act theory is a philosophy of language first proposed by Austin [Aus62]and afterwards refined by Searle [Sea69]. It emphasizes that language is notonly used to make statements about the world that are true or false, but alsoutilized to do something. The priest for example performs a speech act whenhe pronounces the couple husband and wife. The language action perspectivehas extended this view by realizing that speech acts do not appear in isolation,but that they are frequently part of a larger conversation [Win88]. Johannes-son uses this insight to provide a foundation for information systems modelingbased on conversations built from speech acts [Joh95]. Coming from the iden-tification of such conversations, Johannesson derives consistent structural andbehavioral models. Both the foundations in ontology and in speech act theoryhave in common that they imply two levels of modeling: a general level thatis based on abstract entities that the respective theory or philosophy identifiesand a concrete level where the modeler identifies instances of these abstractentities in his modeling domain.

• Metamodeling frees modeling from philosophical assumptions by extending thesubject of the modeling process also to the general level. The philosophical

5Instead of model, Wand and Weber use the term script (cf. [WW90, WW95, WW02]).

4 DEFINITION OF BUSINESS PROCESS MODELING 8

theory of this level such as e.g. an ontology is replaced by a metamodel. Es-sentially, the metamodel identifies the abstract entities that can be used inthe process of designing models, i.e., the metamodel represents the modelinglanguage. The flexibility gained from this meta principle comes at the cost ofrelativism: as a metamodel is meta relative to a model, i.e., it is a model itself.Therefore, a metamodel can also be defined for the metamodel that is calledmetametamodel. This regression can be continued to the infinity without everreaching an epistemological ground. Most modeling frameworks define threeor four modeling levels (see e.g. [OMG02, Fla98, WKR01]). The definition of amodeling language based on a metamodel is more often used than the explicitreference to a philosophical position. Examples of metamodels can be foundin [Ost95, Fla98, Sch98, Sch00]. For the application of the meta principle inother contexts refer to [Neu88, Str96].

The meta-hierarchy provides a means to distinguish different kinds of models. Still,a model can never be a metamodel by itself but only relative to a model for which itdefines the modeling language. Models can also be distinguished depending on themapping mechanism [Str96, p.21]. Non-linguistic models capture some real-world as-pects as material artifacts or as pictures. Linguistic models can be representational,verbal, logistic, or mathematical. Models also serve diverse purposes. Focusing onbusiness administration, Kosiol distinguishes descriptive models, explanatory mod-els, and decision models [Kos61]. In this context, descriptive models capture objectsof a certain area of discourse and represent them in a structured way. Beyond that,explanatory models define dependency relationships between nomological hypothe-ses. These serve as general laws to explain real-world phenomena which implies aclaim for empirical validity. Finally, decision models support the deduction of ac-tions. This involves the availability of a description model to formalize the setting ofthe decision, a set of goals that constraint the design situation, and a set of decisionparameters.

Against this background, the terms business process model, business process mod-eling language, and business process modeling can be defined as follows:

• A business process model is the result of a mapping of a business process. Thisbusiness process can be either a real-world business process as perceived by amodeler or a business process conceptualized by a modeler.

• Business process modeling is the human activity of creating a business processmodel. Business process modeling involves an abstraction from the real-worldbusiness process, because it serves a certain modeling purpose. Therefore, onlythose aspects relevant to the modeling purpose are included in the processmodel.

• Business process modeling languages guide the procedure of business processmodeling by offering a predefined set of elements and relationships for themodeling of business processes. A business process modeling language can

5 BUSINESS PROCESS MODELING AND ERRORS 9

be specified using a metamodel. In conjunction with a respective method, itestablishes a business process modeling technique.

This definition deserves some comments. In contrast to e.g. [Sta73], it does notclaim that the business process model is an abstraction and serves a purpose. Theseattributions involve some problems about whether a model always has to be ab-stract or to serve a purpose. Instead, the procedure of business process modelingis characterized so that it is guided by abstraction and a purpose in mind. This isimportant as a model is not just a “representation of a real-world system” (as Wandand Weber put it [WW90, p.123]), but a design artifact, in the sense of Hevner etal. [HMPR04], that itself becomes part of the real-world as soon as it is created.Beyond that, business process models can be characterized as linguistic models thatare mainly representational and mathematical. The representational aspect pointsto the visual notation of a business process modeling language while the mathemat-ical notion refers to the formal syntax and semantics. In practice, business processmodels are often used for documentation purposes [DGR+06]. Therefore, they canbe regarded as descriptive models for organization and information systems engi-neers. Still, they also serve as explanatory and decision models for the people whoare involved in the actual processing of cases. In this thesis, the focus is on thedescriptive nature of business process models.

5 Business Process Modeling and Errors

It is a fundamental insight of software engineering that design errors should bedetected as early as possible (see e.g. [Boe81, WW02]). The later errors are detected,the more rework has to be done, and the more design effort has been useless. Thisholds also for the consecutive steps of analysis, design, and implementation in thebusiness process management lifecycle. In the design phase, process models aretypically created with semi-formal business process modeling languages while formalexecutable models are needed for the implementation. This problem is often referredto as the gap between business process design and implementation phase (see e.g.[zMR04]). Therefore, the Guidelines of Process Modeling stress correctness as themost important quality attribute of business process models [BRvU00, Ros98].

In order to provide a better understanding of potential errors in business processmodels, it is proposed to adapt the information modeling process identified by[FvdW06]. This process can also serve as a framework for understanding businessprocess modeling in the analysis and design phase of the business process manage-ment lifecycle. Figure 2 gives a business process modeling process mainly inspiredby [FvdW06] consisting of eight steps. In accordance with [vHSSV06], it is proposedto first verify the process model (step 6) before validating it (step 7-8).

The BPM modeling process starts with collecting information objects relevant to thedomain (step 1). Such information objects include documents, diagrams, pictures,and interview recordings. In step 2, these different inputs is verbalized to text that

5 BUSINESS PROCESS MODELING AND ERRORS 10

requirements

Collect information objects

Verbalize information objects

Reformulate specification

Discover modeling concepts

Map to modeling language

Verification of model

Paraphrasing preliminary model

Validate model against specification

Analysis

Design

Informalspecification

Normal formspecification

Process model

Modifiedspecification

process model

Implementation

1

2

3

4

5

6

7

8

Figure 2: Business process modeling process in detail, adapted from [FvdW06]

serves as a unifying format. This text is rearranged according to some general guide-line of how to express facts (step 3) yielding an informal specification. The followingstep (step 4) takes this informal specification as a basis to discover modeling conceptsand to produce a normalized specification. This normal form specification is thenmapped to constructs of the process modeling language (step 5) in order to createa business process model. These models have to be verified for internal correctness(step 6) before they can be paraphrased back to natural language (step 7) in order tovalidate them against the specification (step 8). In steps 6-8 the order of activitiesfollows the proposal of Van Hee et al. [vHSSV06]. It is a good idea to first verify theinternal correctness of a model before validating it against the specification, becauseit prevents incorrect models from being unnecessarily validated.

The BPM modeling process points to two categories of potential errors based on the

6 RESEARCH DESIGN AND RESEARCH OBJECTIVES 11

distinction of verification and validation.

• Verification addresses the internal correctness of models and is specific forthe process modeling language that is used. Several correctness criteria havebeen proposed for business process modeling languages including soundnessfor Workflow nets [vdA97], relaxed soundness [DR01], or well-structuredness(see [DZ05]).

• In contrast to that, validation addresses the consistency of the business processmodel with the universe of discourse. As it is an external correctness criterion,it is more difficult and more ambiguous to decide. While verification typicallyrelies on an algorithmic analysis of the process model, validation requires theconsultation of the specification and discussion with business process stake-holders.

In this thesis, I will refer to formal errors in connection with the internal correctnessof business process models. Formal errors can be identified via verification. Fur-thermore, I use the term inconsistencies with respect to a mismatch of model andspecification. Inconsistencies are identified by validation.

Up to now, little work has been conducted on formal errors of business processmodels in practice. One reason for that is that large repositories of business processmodels capture specific and valuable business knowledge of industrial or consultingcompanies. This fact is a serious problem for academia since practical modelingexperience can hardly be reflected in a scientific way. Thomas [Tho05] calls thisthe “dilemma” of modeling research. One case of a model that is, at least partially,publicly available is the SAP reference model. It has been described in [CKL97,KT98] and is referred to in many research papers (see e.g. [FL03, LS02, MNN05b,RvdA06, TS06]). The extensive database of this reference model contains almost10,000 sub-models, 604 of them being process models [CKL97, KNS92, KT98]. Theverification of these models has shown that there are several formal errors in themodels (cf. [vDvdAV05, vDJV05, MMN+06a]). In [MMN+06a], the authors identifya lower bound for the number of errors of 34 (5.6%) using the relaxed soundnesscriterion.

6 Research Design and Research Objectives

Against this background, the aim of this doctoral thesis is the development of aframework for the detection and prediction of formal errors and other quality at-tributes in business process models. In particular, a research design is identifiedthat offers the following features:

• automatic detection of formal errors in large business process model collectionsbased on formal verification techniques, and

6 RESEARCH DESIGN AND RESEARCH OBJECTIVES 12

• automatic generation of quality attributes, in particular metrics, for businessprocess models based on theoretical insights from software engineering andgraph theory, and

• tool-based statistical evaluation of the suitability of quality attributes for theprediction of error probability in business process models.

The applicability of this framework will we be demonstrated with Event-driven Pro-cess Chains (EPCs) [KNS92]. This choice is motivated by three reasons. First, EPCsare often used in practice in business process modeling. They are supported by sev-eral business process modeling tools such as e.g. ARIS Toolset of IDS Scheer AG.Its widespread application in practice provides the opportunity to test the frame-work against real-world business process models as for example the SAP ReferenceModel. Second, EPCs offer those routing elements that are offered by most busi-ness process modeling languages: AND-join and -split, XOR-join and -split, andOR-join and -split. Furthermore, OR-joins imply some interesting challenges from aformal point of view since their behavior involves non-local synchronization (see e.g.[NR02, vdADK02, Kin03, Kin04, Kin06]). Finally, the reader can easier follow theargumentation if there are no changes between different business process modelinglanguages. It should be pointed out that despite the focus on Event-driven ProcessChains (EPCs), the findings are also applicable for other business process modelinglanguages.

In this context, this thesis provides the following contributions to the state of theart in the detection and prediction of formal errors in EPC business process models.

1. Enhance state of the art in error detection in business process models. Up tonow, there are only some approaches available that can provide the basis forverification of process modeling languages like EPCs. The challenge in thiscontext is the non-local behavior of the OR-join. In [CK04, CK05] the au-thors present an approach to calculate the state space of EPCs. In [CFK05]they present an optimization. Based on the state space, it is easy to verifythe soundness of an EPC. Yet, the structure of the model is only hardly ex-ploited to increase the performance of the calculation. In [vDvdAV05] theauthors use reduction rules in order to simplify the EPC. In interaction withthe process owner, the allowed combinations of start and end events are iden-tified. Due to this interactive nature, the verification cannot be automated.In [MMN+06a, MMN+06b] EPC models are mapped to YAWL and then ana-lyzed using the WofYAWL analysis tool. Yet, this mapping causes complexityproblems due to OR-connectors with more than 10 subsequent elements. Fur-thermore, WofYAWL checks only relaxed soundness and it is not clear howmany errors remain undetected. Therefore, this thesis will specify an EPCanalysis technique that is tailored to the specifics of EPCs and that does notencounter performance bottlenecks as WofYAWL.

2. Identify a set of potential error determinants for business process models.While there is several research on quality attributes and their power to pre-

6 RESEARCH DESIGN AND RESEARCH OBJECTIVES 13

dict error probability in software programs, e.g. the field of measuring thecomplexity of business process models is rather new. In [JC06] several metricsfor software complexity are discussed and adapted to business process mod-els. Yet, there is hardly a study available on how good such metrics performin practice. In [MMN+06a] the SAP reference models are analyzed and theprocess complexity metrics provided by Cardoso [Car05] are not significantlyrelated with error probability. Therefore, this thesis will gather a set of poten-tial quality attributes for processes that seem promising to serve as predictorsof error probability.

3. Test these error determinants on real-world collections of EPC business pro-cess models. Up to now, only the SAP reference model has been subject toa comprehensive analysis of formal errors. The research design applied in[MMN+06a] suggests that simple process complexity metrics such as size andnumber of model elements perform better than state based metrics. There-fore, I will analyze further process repositories of industry modeling projects.This should deliver insight in how far quality attributes can generally serveas predictors for error probability or if there are significant differences amongdifferent repositories.

This thesis is organized in two major parts, each following a particular researchparadigm. The first part establishes formal analysis methods for detection of errorsin business process models. In this way it follows the foundations of design science aselaborated in Hevner et al. [HMPR04] and Simon [Sim96]. March and Smith identifytwo design processes, namely build and evaluate, and four kinds of design artifacts:constructs, models, methods, and instantiations [MS95]. In the context of this thesis,I develop an verification method for business process models. It is evaluated byproving its correctness using formal methods and by demonstrating its applicabilityby a prototypical implementation as an analysis tool. The second part builds on theerror detection method of the first part and identifies error determinants for severalreal-world collections of business process models as input. As such this part follows abehavioral science research design as it “seeks to develop and justify theories [...] thatexplain or predict organizational and human phenomena surrounding the analysis,design, implementation, management, and use of information systems” [HMPR04,76] with process modeling errors being the investigated phenomenon. By applyingstatistical methods, hypotheses about the influence of quality attributes on errorprobability of business process models are tested, and quality metrics are defined.

This research design takes advantage of both research paradigms for informationsystems. Information systems research and its German counterpart Wirtschaftsin-formatik build on both design science and behavioral science research. Accord-ing to Hansen and Neumann [HN05] it is defined as follows: “The study that isconcerned with design of computer-based information systems in business is calledWirtschaftsinformatik (in English: Management Information Systems, InformationSystems, Business Informatics). It is understood to be an interdisciplinary subjectbetween business science and computer science” (own translation). This definition

REFERENCES 14

stresses the design science paradigm which is typical for the German informationsystems community. Only recently, it was been criticized that Wirtschaftsinformatiktakes too little advantage of behavioral, especially empirical, methodologies [BH05].The research design of this thesis built on both paradigms follows the idea of Hevneret al. that design and behavioral science are complementary: “truth informs designand utility informs theory” [HMPR04]. Depending on the detection of formal errorsin business process models, potential predictors for such errors are identified by an-alyzing real-world process models. The results of this research provides feedback tomodelers about how they should perform modeling in practice. Furthermore, theempirical findings will provide insight into further enhancements in the design ofquality metrics for business process models.

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