A Survey of Comparative Business Process Modeling Approaches

Lu, Ruopeng; Sadiq, Shazia

doi:10.1007/978-3-540-72035-5_7

Cited by 148 publications

(110 citation statements)

References 17 publications

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“…In highly competitive complex and rapidly changing markets, business decisions should be traceable and adaptable [35,30]. Decision automation is one way to reach this, by encoding business knowledge in an executable decision model [39].…”

Section: What Is Decision Automation?mentioning

confidence: 99%

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Decision Exploration Lab: A Visual Analytics Solution for Decision Management

Broeksema

Baudel

Telea

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Decision Exploration Lab in visual querying mode, with the DataSpace tree (A), the Decision Map (B) and the Rule Triggering view (C).Abstract-We present a visual analytics solution designed to address prevalent issues in the area of Operational Decision Management (ODM). In ODM, which has its roots in Artificial Intelligence (Expert Systems) and Management Science, it is increasingly important to align business decisions with business goals. In our work, we consider decision models (executable models of the business domain) as ontologies that describe the business domain, and production rules that describe the business logic of decisions to be made over this ontology. Executing a decision model produces an accumulation of decisions made over time for individual cases. We are interested, first, to get insight in the decision logic and the accumulated facts by themselves. Secondly and more importantly, we want to see how the accumulated facts reveal potential divergences between the reality as captured by the decision model, and the reality as captured by the executed decisions. We illustrate the motivation, added value for visual analytics, and our proposed solution and tooling through a business case from the car insurance industry.

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Section: What Is Decision Automation?mentioning

confidence: 99%

“…The same is true for output KPIs in our context: These show increase or decrease of business performance, but give few or no clues on why this variation is happening. Also, typical KPIs say little about the relation between current vs optimal performance, which is an important business driver [35,30].…”

Section: Analytics Requirements For Decision Managementmentioning

confidence: 99%

Decision Exploration Lab: A Visual Analytics Solution for Decision Management

Broeksema

Baudel

Telea

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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“…FlowMake: The tool supports design time identification of errors in models before implementation in WFMS [32]. Graph reduction algorithm [33] is employed to verify workflows for syntactic correctness by identifying and eliminating structural conflicts like deadlocks and lack of synchronization. Correct structures are removed until the WF graph remains empty through a conflict reserving reduction process.…”

Section: Business Process Verification Approachesmentioning

confidence: 99%

“…The following section briefly describes these parameters; Expressibility; assesses the degree to which an approach can represent any number of models in different application domains [54] [55]. In [33], the expressive power of a modelling technique was gauged in terms of its capability to represent specific process requirements. In our case, we consider expressiveness of a model verification tool in terms of the degree to which it supports one to verify different properties of cBP models given their specifications.…”

Section: Assessment Criteriamentioning

confidence: 99%

A Comparative Assessment of Collaborative Business Process Verification Approaches

Kasse

Vrieze

2017

Collaboration in a Data-Rich World

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Abstract. Industry 4.0 is a key strategic trend of the economy. Virtual factories are key building blocks for Industry 4.0 where product design processes, manufacturing processes and general collaborative business processes across factories and enterprises are integrated. In the context of EU H2020 FIRST (vF Interoperation suppoRting buSiness innovaTion) project, end users of vFs are not experts in business process modelling to guarantee correct collaborative business processes for realizing execution. To enable automatic execution of business processes, verification is an important step at the business process design stage to avoid errors at runtime. Research in business process model verification has yielded a plethora of approaches in form of languages and tools that are based on Petri nets family and temporal logic. However, no report specifically targets and presents a comparative assessment of these approaches based on criteria as one we propose. In this paper we present an assessment of the most common verification approaches based on their expressibility, flexibility, suitability and complexity. We also look at how big data impacts the business process verification approach in a data-rich world.

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“…The literature on business process modeling is vast and the importance of measuring the success of process modeling projects and sessions has been widely recognized [1][2][3][4][5][6]. But prevalent success measures for individual modeling sessions primarily involve some form of model quality measure [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Business Process Model Similarity as a Proxy for Group Consensus

Rittgen

2011

Lecture Notes in Business Information Processing

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Abstract.Consensus is an important measure for the success of any business process modeling effort. Although intensively studied in the general literature on group processes, consensus has hardly been considered in business process modeling and never seriously measured. We define consensus as the level of agreement of group members' views on the process and introduce business process similarity as a proxy. We validate the measure by comparing it to an existing self-reported measure of consensus. Keywords:Business process modeling, model similarity, group consensus, mental model, view, visualization. IntroductionThe literature on business process modeling is vast and the importance of measuring the success of process modeling projects and sessions has been widely recognized [1][2][3][4][5][6]. But prevalent success measures for individual modeling sessions primarily involve some form of model quality measure [7][8][9][10]. While it is undisputed that the quality of a business process model is relevant to modeling success it is not the only and perhaps not even the most important success factor. The reason for this is twofold: the process model itself is a social construction, and its purpose is again to support some social process, e.g. a change project or system development project. In other words: the model documents the results of one social process (modeling) and serves as a point of departure for another one.If the model were to be processed by a computer its quality would be of prime importance to ensure correct interpretation by the machine. But the results that are documented in the model are primarily the mutual knowledge that has been developed in the modeling session, the conflicts that had to be solved on the way, and the consensus that has been achieved among the group members as a result.It is precisely this consensus that is a prerequisite for people's commitment to the ensuing change project, for example. Often a poor model with high consensus goes further than a good model with little consensus. Hence consensus is a major result that needs to be achieved in business process modeling sessions much like in many other forms of group work.

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A Survey of Comparative Business Process Modeling Approaches

Cited by 148 publications

References 17 publications

Decision Exploration Lab: A Visual Analytics Solution for Decision Management

Decision Exploration Lab: A Visual Analytics Solution for Decision Management

A Comparative Assessment of Collaborative Business Process Verification Approaches

Business Process Model Similarity as a Proxy for Group Consensus

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